diff --git "a/Prediction_MODEL_FInal.ipynb" "b/Prediction_MODEL_FInal.ipynb"
new file mode 100644--- /dev/null
+++ "b/Prediction_MODEL_FInal.ipynb"
@@ -0,0 +1,6656 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff58dba0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "26da1e5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv(\"laptop_data.csv\") #importing dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "99d24ad7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8GB</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37kg</td>\n",
+       "      <td>71378.6832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>1440x900</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8GB</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34kg</td>\n",
+       "      <td>47895.5232</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
+       "\n",
+       "                    Cpu  Ram               Memory  \\\n",
+       "0  Intel Core i5 2.3GHz  8GB            128GB SSD   \n",
+       "1  Intel Core i5 1.8GHz  8GB  128GB Flash Storage   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight       Price  \n",
+       "0  Intel Iris Plus Graphics 640  macOS  1.37kg  71378.6832  \n",
+       "1        Intel HD Graphics 6000  macOS  1.34kg  47895.5232  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42938375",
+   "metadata": {},
+   "source": [
+    "# Here are some columns which we do not need but at the time of data cleaning we will be removing those columns too.\n",
+    "## STAGE:-\n",
+    "### 1.Data Cleaning\n",
+    "###  2. Feature Engineering\n",
+    "###  3. EDA (Exploratory Data Analysis\n",
+    "###  4. Selecting The Best Model\n",
+    "###  5. Making Website For The Model\n",
+    "###  6. Finally Deployment Of It. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "5613e1f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1303, 12)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape #rows and columns in dara "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "22d3ae32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1303 entries, 0 to 1302\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   Unnamed: 0        1303 non-null   int64  \n",
+      " 1   Company           1303 non-null   object \n",
+      " 2   TypeName          1303 non-null   object \n",
+      " 3   Inches            1303 non-null   float64\n",
+      " 4   ScreenResolution  1303 non-null   object \n",
+      " 5   Cpu               1303 non-null   object \n",
+      " 6   Ram               1303 non-null   object \n",
+      " 7   Memory            1303 non-null   object \n",
+      " 8   Gpu               1303 non-null   object \n",
+      " 9   OpSys             1303 non-null   object \n",
+      " 10  Weight            1303 non-null   object \n",
+      " 11  Price             1303 non-null   float64\n",
+      "dtypes: float64(2), int64(1), object(9)\n",
+      "memory usage: 122.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() #information of the each column in the database "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "75d135ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum() #to check the duplicate values in the dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "534db510",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 0          0\n",
+       "Company             0\n",
+       "TypeName            0\n",
+       "Inches              0\n",
+       "ScreenResolution    0\n",
+       "Cpu                 0\n",
+       "Ram                 0\n",
+       "Memory              0\n",
+       "Gpu                 0\n",
+       "OpSys               0\n",
+       "Weight              0\n",
+       "Price               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum() #to check the missing values of the dataset "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53efc5c7",
+   "metadata": {},
+   "source": [
+    "#  DATA CLEANING SECTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "b1291b3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=[\"Unnamed: 0\"],inplace= True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "0419494e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8GB</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37kg</td>\n",
+       "      <td>71378.6832</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "\n",
+       "                    Cpu  Ram     Memory                           Gpu  OpSys  \\\n",
+       "0  Intel Core i5 2.3GHz  8GB  128GB SSD  Intel Iris Plus Graphics 640  macOS   \n",
+       "\n",
+       "   Weight       Price  \n",
+       "0  1.37kg  71378.6832  "
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1) #Unnamed column has been droppped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "2edf5488",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Ram\"]= df[\"Ram\"].str.replace(\"GB\",\"\") #Removed the Gb from the Ram "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "dad4e31c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Weight\"]= df[\"Weight\"].str.replace(\"kg\",\"\") #Removed the Weight from the Weight column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "6c283cae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "\n",
+       "                    Cpu Ram     Memory                           Gpu  OpSys  \\\n",
+       "0  Intel Core i5 2.3GHz   8  128GB SSD  Intel Iris Plus Graphics 640  macOS   \n",
+       "\n",
+       "  Weight       Price  \n",
+       "0   1.37  71378.6832  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "10bd9666",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Ram\"]= df[\"Ram\"].astype(\"int32\") #Converted Ram dtype from object to int 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "e2c59a02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Weight\"]= df[\"Weight\"].astype(\"float32\") #Converted Weight dtype from object to int float"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "759b1a63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1303 entries, 0 to 1302\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   Company           1303 non-null   object \n",
+      " 1   TypeName          1303 non-null   object \n",
+      " 2   Inches            1303 non-null   float64\n",
+      " 3   ScreenResolution  1303 non-null   object \n",
+      " 4   Cpu               1303 non-null   object \n",
+      " 5   Ram               1303 non-null   int32  \n",
+      " 6   Memory            1303 non-null   object \n",
+      " 7   Gpu               1303 non-null   object \n",
+      " 8   OpSys             1303 non-null   object \n",
+      " 9   Weight            1303 non-null   float32\n",
+      " 10  Price             1303 non-null   float64\n",
+      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
+      "memory usage: 101.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4b85288",
+   "metadata": {},
+   "source": [
+    "# EDA OR ANALYSIS ON THE DATA\n",
+    "## UNIVARIATE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "cdab8319",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\941010651.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(df[\"Price\"])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"Price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "dc3683af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#There are few laptops which have higher prices but so many with lower prices ##OR YOU CAN SAY DATA IS SKEWED "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5836c4c2",
+   "metadata": {},
+   "source": [
+    "# Column Company\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "474bf90e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Company\"].value_counts().plot(kind=\"bar\") #To see how many laptops are there of each company "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "cf2138b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"Company\"], y=df[\"Price\"])\n",
+    "plt.xticks(rotation='vertical')\n",
+    "plt.show() # to see the avaerage price of a company, this indicates that the company also effects the price "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be837b41",
+   "metadata": {},
+   "source": [
+    "# Column TypeName"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "db90287e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"TypeName\"].value_counts().plot(kind=\"bar\") #To see the how many types of laptop we have "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "e365cdbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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l5+ebNdHR0UpMTNSSJUu0ZMkSJSYmKiYmxtyen5+vtm3bKisrS6tXr9b8+fP1+eefKy4uzqzJzMzUI488otDQUG3atEmTJ0/W2LFjNX78+JuwZwAAgDtyGMVkysXhcGjBggXq2LGjpAuzVaGhoRo4cKBeeOEFSRdmp4KDg/XWW2/pmWeeUUZGhipWrKiPPvpInTt3liQdPnxYYWFh+uabb9SqVSvt3LlTderU0fr169WwYUNJ0vr169WoUSPt2rVL4eHh+vbbb9WuXTulpKQoNDRUkjR//nz16NFDaWlpCggI0NSpUzVs2DAdPXpUPj4+kqQ333xTkydP1qFDh+RwOK7qc2ZmZsrpdCojI0MBAQFW7kIAAK7ZG13/ancLxcLw2Z8Vuv1qf38X23OsDhw4oNTUVEVFRZljPj4+atq0qdauXStJSkhIUF5enktNaGioIiIizJp169bJ6XSaoUqS7r//fjmdTpeaiIgIM1RJUqtWrZSTk6OEhASzpmnTpmaoulhz+PBh/frrr9bvAAAA4HaKbbBKTU2VJAUHB7uMBwcHm9tSU1Pl7e2twMDAQmuCgoIKfP+goCCXmkvfJzAwUN7e3oXWXHx+seZycnJylJmZ6fIAAAAlk5fdDfyRSw+xGYbxh4fdLq25XL0VNRePohbWz+jRozVy5MhC+wWAojBgwAAdO3ZMklSxYkVNmjTJ5o6AkqfYzliFhIRIKjgblJaWZs4UhYSEKDc3V+np6YXWHD16tMD3P3bsmEvNpe+Tnp6uvLy8QmvS0tIkFZxV+71hw4YpIyPDfKSkpBT+wQHgJjl27JiOHj2qo0ePmgELgLWKbbCqVq2aQkJCtHTpUnMsNzdXK1euVOPGjSVJkZGRKlWqlEvNkSNHlJSUZNY0atRIGRkZ2rhxo1mzYcMGZWRkuNQkJSXpyJEjZk18fLx8fHwUGRlp1qxatcplCYb4+HiFhoaqatWqV/wcPj4+CggIcHkAAICSydZgdfr0aSUmJioxMVHShRPWExMTlZycLIfDoYEDB2rUqFFasGCBkpKS1KNHD5UpU0bR0dGSJKfTqV69eikuLk7Lli3T1q1b1bVrV9WtW1ctW7aUJNWuXVutW7dW7969tX79eq1fv169e/dWu3btFB4eLkmKiopSnTp1FBMTo61bt2rZsmUaMmSIevfubQah6Oho+fj4qEePHkpKStKCBQs0atQoDR48+KqvCAQAACWbredYbd68Wc2bNzefDx48WJLUvXt3zZgxQ88//7yys7PVt29fpaenq2HDhoqPj5e/v7/5mgkTJsjLy0udOnVSdna2WrRooRkzZsjT09OsmTNnjmJjY82rBzt06OCydpanp6cWL16svn37qkmTJvL19VV0dLTGjh1r1jidTi1dulTPPfec7r33XgUGBmrw4MFmzwAAAMVmHatbBetYAbBLdHS0ec5pcHCw5s6da3NHKA5Yx+qCEr+OFQAAgLshWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFjEy+4GAOBWsvKhpra991kvT8nhuPB1aqqtvTRdtdK29wZuJmasAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALOJldwMoXgYMGKBjx45JkipWrKhJkybZ3BEAAO6DYAUXx44d09GjR+1uA8BNEGBIkvG7rwFYjWAFALeIp/Pz7W4BKPE4xwoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAgLhBZDkUNn2fbeAemnzbR9JP20rb0kjOlm23sDAHA9mLECAACwSLEOVufOndM///lPVatWTb6+vrrzzjv12muv6fz582aNYRgaMWKEQkND5evrq2bNmmnHjh0u3ycnJ0f9+/dXhQoV5Ofnpw4dOujQoUMuNenp6YqJiZHT6ZTT6VRMTIxOnjzpUpOcnKz27dvLz89PFSpUUGxsrHJzc2/a5wcAAO6lWAert956S//5z380ZcoU7dy5U2+//bbGjBmjyZMnmzVvv/22xo8frylTpmjTpk0KCQnRI488olOnTpk1AwcO1IIFCzR//nytXr1ap0+fVrt27ZT/u/tmRUdHKzExUUuWLNGSJUuUmJiomJgYc3t+fr7atm2rrKwsrV69WvPnz9fnn3+uuLi4otkZAACg2CvW51itW7dOjz76qNq2bStJqlq1qubNm6fNmzdLujBbNXHiRA0fPlyPP/64JGnmzJkKDg7W3Llz9cwzzygjI0PTp0/XRx99pJYtW0qSZs+erbCwMH3//fdq1aqVdu7cqSVLlmj9+vVq2LChJGnatGlq1KiRdu/erfDwcMXHx+vnn39WSkqKQkNDJUnjxo1Tjx499MYbbyggIKCodw8AAChmivWM1QMPPKBly5Zpz549kqRt27Zp9erVatOmjSTpwIEDSk1NVVRUlPkaHx8fNW3aVGvXrpUkJSQkKC8vz6UmNDRUERERZs26devkdDrNUCVJ999/v5xOp0tNRESEGaokqVWrVsrJyVFCQsJN2gMAAMCdFOsZqxdeeEEZGRm666675Onpqfz8fL3xxht66qmnJEmpqamSpODgYJfXBQcH6+DBg2aNt7e3AgMDC9RcfH1qaqqCgoIKvH9QUJBLzaXvExgYKG9vb7PmcnJycpSTk2M+z8zMvKrPDgAA3E+xnrH6+OOPNXv2bM2dO1dbtmzRzJkzNXbsWM2cOdOlzuFwuDw3DKPA2KUurblc/fXUXGr06NHmCfFOp1NhYWGF9gUAANxXsQ5WQ4cO1Ysvvqgnn3xSdevWVUxMjAYNGqTRo0dLkkJCQiSpwIxRWlqaObsUEhKi3NxcpaenF1pz9OjRAu9/7Ngxl5pL3yc9PV15eXkFZrJ+b9iwYcrIyDAfKSkp17ILAACAGynWwerMmTPy8HBt0dPT01xuoVq1agoJCdHSpUvN7bm5uVq5cqUaN24sSYqMjFSpUqVcao4cOaKkpCSzplGjRsrIyNDGjRvNmg0bNigjI8OlJikpSUeOHDFr4uPj5ePjo8jIyCt+Bh8fHwUEBLg8AABAyVSsz7Fq37693njjDVWuXFl33323tm7dqvHjx6tnz56SLhyaGzhwoEaNGqWaNWuqZs2aGjVqlMqUKaPo6GhJktPpVK9evRQXF6fy5curXLlyGjJkiOrWrWteJVi7dm21bt1avXv31nvvvSdJ6tOnj9q1a6fw8HBJUlRUlOrUqaOYmBiNGTNGJ06c0JAhQ9S7d+8SFZbOl/K77NcAAOCPFetgNXnyZL388svq27ev0tLSFBoaqmeeeUavvPKKWfP8888rOztbffv2VXp6uho2bKj4+Hj5+/ubNRMmTJCXl5c6deqk7OxstWjRQjNmzJCnp6dZM2fOHMXGxppXD3bo0EFTpkwxt3t6emrx4sXq27evmjRpIl9fX0VHR2vs2LFFsCeKzunwv9jdAgAAbsthGIZhdxO3kszMTDmdTmVkZFxxpsvO+/MVJ9wrECXRyoea2t1CsdB01Uq7W8D/eaPrX+1uoVgYPvuzQrdfze9vqZifYwUAAOBOCFYAAAAWIVgBAABY5IaC1b59+/Tdd98pOztb0oXFMgEAAG5V1xWsjh8/rpYtW6pWrVpq06aNubbT3//+d8XFxVnaIAAAgLu4rmA1aNAgeXl5KTk5WWXKlDHHO3furCVLlljWHAAAgDu5rnWs4uPj9d133+mOO+5wGa9Zs6Z582MAAIBbzXUFq6ysLJeZqov+97//ycfH54abAgCgOBswYICOHTsmSapYsaImTZpkc0coLq7rUOBDDz2kWbP+/yKWDodD58+f15gxY9S8eXPLmgMAoDg6duyYjh49qqNHj5oBC5Cuc8ZqzJgxatasmTZv3qzc3Fw9//zz2rFjh06cOKE1a9ZY3SMAAIBbuK4Zqzp16mj79u1q0KCBHnnkEWVlZenxxx/X1q1bVb16dat7BAAAcAvXfRPmkJAQjRw50speAAAA3Np1zVh9+OGH+vTTTwuMf/rpp5o5c+YNNwUAAOCOritYvfnmm6pQoUKB8aCgII0aNeqGmwIAAHBH1xWsDh48qGrVqhUYr1KlipKTk2+4KQAAAHd0XcEqKChI27dvLzC+bds2lS9f/oabAgAAcEfXFayefPJJxcbGasWKFcrPz1d+fr6WL1+uAQMG6Mknn7S6RwAAALdwXVcFvv766zp48KBatGghL68L3+L8+fPq1q0b51gBAIBb1nUFK29vb3388cf617/+pW3btsnX11d169ZVlSpVrO4PAADAbVz3OlaSVKtWLdWqVcuqXgAAANzaVQerwYMH61//+pf8/Pw0ePDgQmvHjx9/w40BAAC4m6sOVlu3blVeXp4kacuWLXI4HJetu9I4AABASXfVwWrFihXm1z/88MPN6AUAAMCtXfNyC+fOnZOXl5eSkpJuRj8AAABu65pPXvfy8lKVKlWUn59/M/oBAOAPTYlbZOv7nzpxxuVrO/vpN669be+Ngq5rgdB//vOfGjZsmE6cOGF1PwAAAG7rupZbeOedd7Rv3z6FhoaqSpUq8vPzc9m+ZcsWS5oDAABwJ9cVrDp27CiHwyHDMKzuBwAAwG1dU7A6c+aMhg4dqoULFyovL08tWrTQ5MmTVaFChZvVHwAAgNu4pnOsXn31Vc2YMUNt27bVU089pe+//17/+Mc/blZvAAAAbuWaZqy++OILTZ8+XU8++aQkqUuXLmrSpIny8/Pl6el5UxoEAABwF9c0Y5WSkqIHH3zQfN6gQQN5eXnp8OHDljcGAADgbq4pWOXn58vb29tlzMvLS+fOnbO0KQAAAHd0TYcCDcNQjx495OPjY46dPXtWzz77rMuSC1988YV1HQIAALiJawpW3bt3LzDWtWtXy5oBAABwZ9cUrD788MOb1QcAAIDbu65b2gAAAKAgghUAAIBFCFYAAAAWIVgBAABYhGAFAABgkWIfrH777Td17dpV5cuXV5kyZVSvXj0lJCSY2w3D0IgRIxQaGipfX181a9ZMO3bscPkeOTk56t+/vypUqCA/Pz916NBBhw4dcqlJT09XTEyMnE6nnE6nYmJidPLkSZea5ORktW/fXn5+fqpQoYJiY2OVm5t70z47AKB4Ku3trzKlLjxKe/vb3Q6KkWtabqGopaenq0mTJmrevLm+/fZbBQUFaf/+/SpbtqxZ8/bbb2v8+PGaMWOGatWqpddff12PPPKIdu/eLX//Cz/sAwcO1KJFizR//nyVL19ecXFxateunRISEsx7HEZHR+vQoUNasmSJJKlPnz6KiYnRokWLJF1Ydb5t27aqWLGiVq9erePHj6t79+4yDEOTJ08u2h0DALBV05qd7G4BxVSxDlZvvfWWwsLCXNbPqlq1qvm1YRiaOHGihg8frscff1ySNHPmTAUHB2vu3Ll65plnlJGRoenTp+ujjz5Sy5YtJUmzZ89WWFiYvv/+e7Vq1Uo7d+7UkiVLtH79ejVs2FCSNG3aNDVq1Ei7d+9WeHi44uPj9fPPPyslJUWhoaGSpHHjxqlHjx564403FBAQUER7BQAAFFfF+lDgV199pXvvvVd/+9vfFBQUpPr162vatGnm9gMHDig1NVVRUVHmmI+Pj5o2baq1a9dKkhISEpSXl+dSExoaqoiICLNm3bp1cjqdZqiSpPvvv19Op9OlJiIiwgxVktSqVSvl5OS4HJoEAAC3rmIdrH755RdNnTpVNWvW1Hfffadnn31WsbGxmjVrliQpNTVVkhQcHOzyuuDgYHNbamqqvL29FRgYWGhNUFBQgfcPCgpyqbn0fQIDA+Xt7W3WXE5OTo4yMzNdHgAAoGQq1ocCz58/r3vvvVejRo2SJNWvX187duzQ1KlT1a1bN7PO4XC4vM4wjAJjl7q05nL111NzqdGjR2vkyJGF9gIAAEqGYj1jdfvtt6tOnTouY7Vr11ZycrIkKSQkRJIKzBilpaWZs0shISHKzc1Venp6oTVHjx4t8P7Hjh1zqbn0fdLT05WXl1dgJuv3hg0bpoyMDPORkpLyh58bAAC4p2IdrJo0aaLdu3e7jO3Zs0dVqlSRJFWrVk0hISFaunSpuT03N1crV65U48aNJUmRkZEqVaqUS82RI0eUlJRk1jRq1EgZGRnauHGjWbNhwwZlZGS41CQlJenIkSNmTXx8vHx8fBQZGXnFz+Dj46OAgACXBwAAKJmK9aHAQYMGqXHjxho1apQ6deqkjRs36v3339f7778v6cKhuYEDB2rUqFGqWbOmatasqVGjRqlMmTKKjo6WJDmdTvXq1UtxcXEqX768ypUrpyFDhqhu3brmVYK1a9dW69at1bt3b7333nuSLiy30K5dO4WHh0uSoqKiVKdOHcXExGjMmDE6ceKEhgwZot69exOWAACApGIerO677z4tWLBAw4YN02uvvaZq1app4sSJ6tKli1nz/PPPKzs7W3379lV6eroaNmyo+Ph4cw0rSZowYYK8vLzUqVMnZWdnq0WLFpoxY4a5hpUkzZkzR7GxsebVgx06dNCUKVPM7Z6enlq8eLH69u2rJk2ayNfXV9HR0Ro7dmwR7AkAAOAOHIZhGHY3cSvJzMyU0+lURkbGFWe6IofOKuKuiqeEMd3+uAhwMysfamp3C8VC01Urb+j1U+IWWdSJ++s3rv0Nvf6Nrn+1qBP3Nnz2Z4Vuv5rf31IxP8cKAADAnRCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsIiX3Q0AQGEGDBigY8eOSZIqVqyoSZMm2dwRAFwZwQpAsXbs2DEdPXrU7jYA4KpwKBAAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAi7DcAnCTsP4SANx6CFbATcL6SwBw6+FQIAAAgEUIVgAAABYhWAEAAFiEYAUAAGARtwpWo0ePlsPh0MCBA80xwzA0YsQIhYaGytfXV82aNdOOHTtcXpeTk6P+/furQoUK8vPzU4cOHXTo0CGXmvT0dMXExMjpdMrpdComJkYnT550qUlOTlb79u3l5+enChUqKDY2Vrm5uTfr4wIAADfjNlcFbtq0Se+//77+9Kc/uYy//fbbGj9+vGbMmKFatWrp9ddf1yOPPKLdu3fL399fkjRw4EAtWrRI8+fPV/ny5RUXF6d27dopISFBnp6ekqTo6GgdOnRIS5YskST16dNHMTExWrRokSQpPz9fbdu2VcWKFbV69WodP35c3bt3l2EYmjx5chHuCVyt5Nfq2vr+506Wl+T5f18ftrWfyq/8ZNt7A8CtxC1mrE6fPq0uXbpo2rRpCgwMNMcNw9DEiRM1fPhwPf7444qIiNDMmTN15swZzZ07V5KUkZGh6dOna9y4cWrZsqXq16+v2bNn66efftL3338vSdq5c6eWLFmi//73v2rUqJEaNWqkadOm6euvv9bu3bslSfHx8fr55581e/Zs1a9fXy1bttS4ceM0bdo0ZWZmFv1OAQAAxY5bBKvnnntObdu2VcuWLV3GDxw4oNTUVEVFRZljPj4+atq0qdauXStJSkhIUF5enktNaGioIiIizJp169bJ6XSqYcOGZs39998vp9PpUhMREaHQ0FCzplWrVsrJyVFCQoL1HxoAALidYn8ocP78+dqyZYs2bdpUYFtqaqokKTg42GU8ODhYBw8eNGu8vb1dZrou1lx8fWpqqoKCggp8/6CgIJeaS98nMDBQ3t7eZs3l5OTkKCcnx3zO7BYAACVXsZ6xSklJ0YABAzR79myVLl36inUOh8PluWEYBcYudWnN5eqvp+ZSo0ePNk+IdzqdCgsLK7QvAADgvop1sEpISFBaWpoiIyPl5eUlLy8vrVy5Uu+88468vLzMGaRLZ4zS0tLMbSEhIcrNzVV6enqhNZe79cixY8dcai59n/T0dOXl5RWYyfq9YcOGKSMjw3ykpKRc416Auyrnk6/y//co55NvdzsAgCJQrINVixYt9NNPPykxMdF83HvvverSpYsSExN15513KiQkREuXLjVfk5ubq5UrV6px48aSpMjISJUqVcql5siRI0pKSjJrGjVqpIyMDG3cuNGs2bBhgzIyMlxqkpKSdOTIEbMmPj5ePj4+ioyMvOJn8PHxUUBAgMsDt4aX6p/U2PuPa+z9x/VS/ZN2twMAKALF+hwrf39/RUREuIz5+fmpfPny5vjAgQM1atQo1axZUzVr1tSoUaNUpkwZRUdHS5KcTqd69eqluLg4lS9fXuXKldOQIUNUt25d82T42rVrq3Xr1urdu7fee+89SReWW2jXrp3Cw8MlSVFRUapTp45iYmI0ZswYnThxQkOGDFHv3r0JSwAAQFIxD1ZX4/nnn1d2drb69u2r9PR0NWzYUPHx8eYaVpI0YcIEeXl5qVOnTsrOzlaLFi00Y8YMcw0rSZozZ45iY2PNqwc7dOigKVOmmNs9PT21ePFi9e3bV02aNJGvr6+io6M1duzYovuwAACgWHO7YPXDDz+4PHc4HBoxYoRGjBhxxdeULl1akydPLnQhz3Llymn27NmFvnflypX19ddfX0u7AADgFlKsz7ECAABwJwQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLuN0tbQAUvSaTm9j23j6ZPnLIIUlKzUy1tZc1/dfY9t4A3AMzVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARQhWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAUAAGARghUAAIBFCFYAAAAW8bK7AQAojOFrXPZrACiOCFYAirXch3LtbgEArlqxPhQ4evRo3XffffL391dQUJA6duyo3bt3u9QYhqERI0YoNDRUvr6+atasmXbs2OFSk5OTo/79+6tChQry8/NThw4ddOjQIZea9PR0xcTEyOl0yul0KiYmRidPnnSpSU5OVvv27eXn56cKFSooNjZWubn8ow8AAC4o1sFq5cqVeu6557R+/XotXbpU586dU1RUlLKyssyat99+W+PHj9eUKVO0adMmhYSE6JFHHtGpU6fMmoEDB2rBggWaP3++Vq9erdOnT6tdu3bKz883a6Kjo5WYmKglS5ZoyZIlSkxMVExMjLk9Pz9fbdu2VVZWllavXq358+fr888/V1xcXNHsDAAAUOwV60OBS5YscXn+4YcfKigoSAkJCXrooYdkGIYmTpyo4cOH6/HHH5ckzZw5U8HBwZo7d66eeeYZZWRkaPr06froo4/UsmVLSdLs2bMVFham77//Xq1atdLOnTu1ZMkSrV+/Xg0bNpQkTZs2TY0aNdLu3bsVHh6u+Ph4/fzzz0pJSVFoaKgkady4cerRo4feeOMNBQQEFOGeAQAAxVGxnrG6VEZGhiSpXLlykqQDBw4oNTVVUVFRZo2Pj4+aNm2qtWvXSpISEhKUl5fnUhMaGqqIiAizZt26dXI6nWaokqT7779fTqfTpSYiIsIMVZLUqlUr5eTkKCEh4Yo95+TkKDMz0+UBAABKJrcJVoZhaPDgwXrggQcUEREhSUpNTZUkBQcHu9QGBweb21JTU+Xt7a3AwMBCa4KCggq8Z1BQkEvNpe8TGBgob29vs+ZyRo8ebZ635XQ6FRYWdi0fGwAAuBG3CVb9+vXT9u3bNW/evALbHA6Hy3PDMAqMXerSmsvVX0/NpYYNG6aMjAzzkZKSUmhfAADAfblFsOrfv7+++uorrVixQnfccYc5HhISIkkFZozS0tLM2aWQkBDl5uYqPT290JqjR48WeN9jx4651Fz6Punp6crLyyswk/V7Pj4+CggIcHkAAICSqVgHK8Mw1K9fP33xxRdavny5qlWr5rK9WrVqCgkJ0dKlS82x3NxcrVy5Uo0bN5YkRUZGqlSpUi41R44cUVJSklnTqFEjZWRkaOPGjWbNhg0blJGR4VKTlJSkI0eOmDXx8fHy8fFRZGSk9R8eAAC4nWJ9VeBzzz2nuXPn6ssvv5S/v785Y+R0OuXr6yuHw6GBAwdq1KhRqlmzpmrWrKlRo0apTJkyio6ONmt79eqluLg4lS9fXuXKldOQIUNUt25d8yrB2rVrq3Xr1urdu7fee+89SVKfPn3Url07hYeHS5KioqJUp04dxcTEaMyYMTpx4oSGDBmi3r17MwsFAAAkFfNgNXXqVElSs2bNXMY//PBD9ejRQ5L0/PPPKzs7W3379lV6eroaNmyo+Ph4+fv7m/UTJkyQl5eXOnXqpOzsbLVo0UIzZsyQp6enWTNnzhzFxsaaVw926NBBU6ZMMbd7enpq8eLF6tu3r5o0aSJfX19FR0dr7NixN+nTAwAAd1Osg5Vh/PF9wRwOh0aMGKERI0ZcsaZ06dKaPHmyJk+efMWacuXKafbs2YW+V+XKlfX111//YU8AAODWVKzPsQIAAHAnBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAsQrACAACwCMEKAADAIgSr6/Duu++qWrVqKl26tCIjI/Xjjz/a3RIAACgGCFbX6OOPP9bAgQM1fPhwbd26VQ8++KD+8pe/KDk52e7WAACAzQhW12j8+PHq1auX/v73v6t27dqaOHGiwsLCNHXqVLtbAwAANvOyuwF3kpubq4SEBL344osu41FRUVq7du1lX5OTk6OcnBzzeUZGhiQpMzPziu+Tn5NtQbfur7B9dDVOnc23qBP3d6P78lz2OYs6cW83uh8lKesc+1K68X2ZnXPGok7c343uy7N5eRZ14t7+aD9e3G4YRuHfyMBV++233wxJxpo1a1zG33jjDaNWrVqXfc2rr75qSOLBgwcPHjx4lIBHSkpKoVmBGavr4HA4XJ4bhlFg7KJhw4Zp8ODB5vPz58/rxIkTKl++/BVfY7fMzEyFhYUpJSVFAQEBdrfj1tiX1mA/Wod9aR32pTXcZT8ahqFTp04pNDS00DqC1TWoUKGCPD09lZqa6jKelpam4ODgy77Gx8dHPj4+LmNly5a9WS1aKiAgoFj/kLsT9qU12I/WYV9ah31pDXfYj06n8w9rOHn9Gnh7eysyMlJLly51GV+6dKkaN25sU1cAAKC4YMbqGg0ePFgxMTG699571ahRI73//vtKTk7Ws88+a3drAADAZgSra9S5c2cdP35cr732mo4cOaKIiAh98803qlKlit2tWcbHx0evvvpqgUOYuHbsS2uwH63DvrQO+9IaJW0/Ogzjj64bBAAAwNXgHCsAAACLEKwAAAAsQrACAACwCMEKAADAIgQrAAAAixCsALil7GxuVn61MjMzL/s4deqUcnNz7W7PrXz00Udq0qSJQkNDdfDgQUnSxIkT9eWXX9rcGYoLghUkScuWLbvitilTphRhJ+5v+/btV9y2cOHComukBHjuuecuO56VlaW//OUvRdyN+ypbtqwCAwMLPMqWLStfX19VqVJFr776qs6fP293q8Xa1KlTNXjwYLVp00YnT55Ufn6+pAv7d+LEifY252aOHj2qmJgYhYaGysvLS56eni4Pd8Y6VpB04R+GpUuX6r777nMZnzhxol555RVlZmba1Jn7uf3227VmzRrdeeedLuOff/65unXrpqysLJs6cz81a9ZU586d9frrr5tjWVlZat26tSTpxx9/tKs1tzJr1iwNHz5cPXr0UIMGDWQYhjZt2qSZM2fqn//8p44dO6axY8dq6NCheumll+xut9iqU6eORo0apY4dO8rf31/btm3TnXfeqaSkJDVr1kz/+9//7G7RbfzlL39RcnKy+vXrp9tvv10Oh8Nl+6OPPmpTZxYwAMMwPvjgA6NChQrGjh07zLExY8YYAQEBxqpVq2zszP2MHDnSqFq1qnH48GFzbP78+UaZMmWMTz75xMbO3M8vv/xihIaGGuPHjzcMwzAyMzONRo0aGQ8++KBx+vRpm7tzHw8//LDx8ccfFxj/+OOPjYcfftgwDMOYNWuWER4eXtStuZXSpUsbv/76q2EYhnHbbbcZ+/fvNwzDMPbs2WOULl3aztbczm233WZs3brV7jZuCm5pA0nS008/rePHjysqKkqrV6/Wxx9/rFGjRunbb7/lBtPX6JVXXtHx48fVsmVL/fjjj1qyZIn+/ve/66OPPtITTzxhd3tupVq1avruu+/UrFkzeXh4aP78+fLx8dHixYvl5+dnd3tuY926dfrPf/5TYLx+/fpat26dJOmBBx5QcnJyUbfmVqpVq6bExMQCtzD79ttvVadOHZu6ck9hYWEySuoBM7uTHYqXF1980ShfvrxRtmxZY/369Xa349a6du1q1KxZ0yhTpoyxcOFCu9txa+vWrTP8/PyMhx9+2Dhz5ozd7bidmjVrGi+88EKB8RdeeMGoVauWYRiGsWnTJiM0NLSoW3MrH3zwgVGpUiVj/vz5hp+fnzFv3jzj9ddfN7/G1fvuu++MqKgo48CBA3a3YjnOsbqFvfPOO5cdHzt2rB566CE1aNDAHIuNjS2qttzSV199VWAsLy9PgwYNUlRUlDp06GCO//5rFFS/fv0C51tI0sGDBxUUFCRfX19zbMuWLUXZmtv66quv9Le//U133XWX7rvvPjkcDm3atEm7du3SZ599pnbt2mnq1Knau3evxo8fb3e7xdq0adP0+uuvKyUlRZJUqVIljRgxQr169bK5M/cSGBioM2fO6Ny5cypTpoxKlSrlsv3EiRM2dXbjCFa3sGrVql1VncPh0C+//HKTu3FvHh5Xd4Gtw+EwryTC5Y0cOfKqa1999dWb2EnJ8uuvv+o///mP9uzZI8MwdNddd+mZZ55R1apV7W7NLf3vf//T+fPnFRQUZHcrbmnmzJmFbu/evXsRdWI9ghUAAIBFOHkdBVzM2pc7HAPYISEhQTt37pTD4VCdOnVUv359u1tyOydPntTGjRuVlpZWYL2qbt262dRV8XelQ9OXw6Hpa5Ofn6+FCxe6/L/doUMHt1/HimAF06xZszRmzBjt3btXklSrVi0NHTpUMTExNnfmflauXKmxY8ea/2DUrl1bQ4cO1YMPPmh3a24lLS1NTz75pH744QeVLVtWhmEoIyNDzZs31/z581WxYkW7W3QLixYtUpcuXZSVlSV/f3+XoOBwOAhWhejYsaPdLZRI+/btU5s2bfTbb78pPDxchmFoz549CgsL0+LFi1W9enW7W7xuHAqEJGn8+PF6+eWX1a9fPzVp0kSGYWjNmjX697//rddff12DBg2yu0W3MXv2bD399NN6/PHHzX25du1aLViwQDNmzFB0dLTdLbqNzp07a//+/froo49Uu3ZtSdLPP/+s7t27q0aNGpo3b57NHbqHWrVqqU2bNho1apTKlCljdzuA2rRpI8MwNGfOHJUrV06SdPz4cXXt2lUeHh5avHixzR1eP4IVJF04kX3kyJEF/nKdOXOmRowYoQMHDtjUmfupXbu2+vTpUyCMjh8/XtOmTdPOnTtt6sz9OJ1Off/99wXuCLBx40ZFRUXp5MmT9jTmZvz8/PTTTz8VuBsArs/mzZtdZqMjIyPtbsnt+Pn5af369apbt67L+LZt29SkSROdPn3aps5uHPcKhCTpyJEjl10ItHHjxjpy5IgNHbmvX375Re3bty8w3qFDBwLqNTp//nyBy7AlqVSpUtzX7hq0atVKmzdvtrsNt3fo0CE9+OCDatCggQYMGKDY2Fjdd999euCBB8zlF3B1fHx8dOrUqQLjp0+flre3tw0dWYdgBUlSjRo19MknnxQY//jjj1WzZk0bOnJfYWFhl72p9bJlyxQWFmZDR+7r4Ycf1oABA3T48GFz7LffftOgQYPUokULGztzL23bttXQoUM1YsQIff755/rqq69cHrg6PXv2VF5ennbu3KkTJ07oxIkT2rlzpwzDYB2ra9SuXTv16dNHGzZskGEYMgxD69ev17PPPuv2a/1xKBCSLtwguHPnzmrZsqWaNGkih8Oh1atXa9myZfrkk0/02GOP2d2i25g6daoGDhyonj17qnHjxua+nDFjhiZNmqRnnnnG7hbdRkpKih599FElJSUpLCxMDodDycnJqlu3rr788kvdcccddrfoFgpbZ4211a6er6+v1q5dW+Cq1C1btqhJkybKzs62qTP3c/LkSXXv3l2LFi0yZ6XPnTunDh06aMaMGXI6nTZ3eP0IVjAlJCRowoQJ5l9gderUUVxcHJe2X4cFCxZo3Lhx5vlUF68KdOs7ttto6dKl2rVrl/lz2bJlS7tbwi0oPDxcH330kctdKaQL5/xFR0dr3759NnXmvvbu3evy/3aNGjXsbumGEawAALgKX375pUaNGqV///vfioyMlMPh0ObNm9W/f3+98MILLM0ASQQr/E5JXazNLixqaQ3WBLs+77zzjvr06aPSpUtf8b6gF3Ev0CsLDAx0WfcrKytL586dk5fXhWUgL37t5+fn1ve3KwqDBw/Wv/71L/n5+Wnw4MGF1rrzPSsJVpB0YbG2tm3b6tChQyVusbaixqKW1mFNsOtXrVo1bd68WeXLly/0vqDcC7Rwf3RPu99z5/vbFYXmzZtrwYIFKlu2rJo3b15o7YoVK4qoK+sRrCCpZC/WVtRY1NI6rAkGwN0QrCCpZC/WVtRY1NI6Pj4+2rFjR4ETWvft26eIiAidPXvWps5wq8jMzFRAQID5dWEu1uGP9ezZU5MmTZK/v7/LeFZWlvr3768PPvjAps5uHPcKhKSSvVhbUWNRS+tcXBPs0mDFmmDXxjAMffbZZ1qxYsVlb8L8xRdf2NRZ8RcYGKgjR44oKChIZcuWvewNmQ3DYNmKazRz5ky9+eabBYJVdna2Zs2aRbCC+7u4WNv06dPNS4k3bNhQIhZrK2oXF7WcN2+eQkNDJbGo5fWKi4tTbGysEhMTL7smGK7OgAED9P7776t58+YKDg6+bDjA5S1fvtw8PcKdz/spLjIzM80FQU+dOqXSpUub2/Lz8/XNN98oKCjIxg5vHIcCIalkL9ZW1FjU0lqsCXbjypUrp9mzZ6tNmzZ2t+LWkpOTzf+nf88wDKWkpKhy5co2deY+PDw8Cg32DodDI0eO1PDhw4uwK2sRrOBi79695i+wkrJYm11Y1BLFRbVq1fTtt9/qrrvusrsVt+bp6WkeFvy948ePKygoiEOBV2HlypUyDEMPP/ywPv/8c3M2UJK8vb1VpUoVc6bfXRGsUMDFHwkOF6C42Lx5s8s6VpGRkXa35FZmzpypJUuW6IMPPpCvr6/d7bgtDw8PHT16tMCSKQcPHlSdOnWUlZVlU2fu5+DBgwoLCyv0dkvuinOsYJo+fbomTJigvXv3SpJq1qypgQMH6u9//7vNnbmfZcuWmbcHcjgcuuuuuzRw4EBmra7RoUOH9NRTT2nNmjUqW7aspAuHrRs3bqx58+ZxAvtV+tvf/qZ58+YpKChIVatWLXBxxZYtW2zqzD1cXMzS4XDo5ZdfVpkyZcxt+fn52rBhg+rVq2dTd+6pSpUqkqQzZ84oOTlZubm5Ltv/9Kc/2dGWJQhWkCS9/PLLmjBhgvr3769GjRpJktatW6dBgwbp119/1euvv25zh+5jypQpGjRokP76179qwIABkqT169erTZs2Gj9+vPr162dzh+6jZ8+eysvL086dOxUeHi5J2r17t3r27KlevXopPj7e5g7dQ48ePZSQkKCuXbty8vp12Lp1q6QLs/k//fSTy5XS3t7euueeezRkyBC72nNLx44d09NPP61vv/32stvd+rCqARiGUb58eWPu3LkFxufOnWuUL1/eho7cV2hoqDF58uQC41OmTDFuv/12GzpyX6VLlza2bNlSYDwhIcEoXbq0DR25pzJlyhg//vij3W24ve7duxuZmZl2t1EiREdHG40bNzY2btxo+Pn5GfHx8cZHH31khIeHG19//bXd7d2QkndwE9clPz9f9957b4HxyMhInTt3zoaO3FdmZqZat25dYDwqKuoPFxiEq8qVKysvL6/A+Llz51SpUiUbOnJPYWFhLF55g86dO6fZs2fr4MGDdrdSIixfvlwTJkzQfffdJw8PD1WpUkVdu3bV22+/rdGjR9vd3g0hWEGS1LVrV02dOrXA+Pvvv68uXbrY0JH76tChgxYsWFBg/Msvv1T79u1t6Mh9vf322+rfv782b95sXlSxefNmDRgwQGPHjrW5O/cxbtw4Pf/88/r111/tbsVteXl5qUqVKu59iKoYycrKMq+uLFeunI4dOyZJqlu3rtuf88c5Vrew399d3OFw6L///a/i4+N1//33S7pwXlBKSoq6detmV4tu45133jG/rl27tt544w398MMP5vlq69ev15o1axQXF2dXi24jMDDQ5RygrKwsNWzYUF5eF/65OnfunLy8vNSzZ0917NjRpi7dS9euXXXmzBlVr15dZcqUKXDy+okTJ2zqzL3885//1LBhwzR79myXZQJw7cLDw7V7925VrVpV9erV03vvvaeqVavqP//5j26//Xa727shLLdwC/uju4tf5HA4tHz58pvcjXurVq3aVdU5HA798ssvN7kb9zZz5syrru3evftN7KTk+KN9yn68OvXr19e+ffuUl5enKlWqyM/Pz2W7u8+0FKU5c+YoLy9PPXr00NatW9WqVSsdP35c3t7emjFjhjp37mx3i9eNYAUAwFUYOXJkodtfffXVIuqk5Dlz5ox27dqlypUrq0KFCna3c0MIVsBNkpubqwMHDqh69ermYSxcG1a6tl52dnaBCwI4sR1F7bXXXtOQIUNc1gSTLvx8jhkzRq+88opNnd04ghVMmzZt0qeffnrZxdq++OILm7pyP2fOnFH//v3Nwy979uzRnXfeqdjYWIWGhurFF1+0uUP34eHhodTU1ALB6vDhw6pevbqys7Nt6sy9ZGVl6YUXXtAnn3yi48ePF9hOQL16J0+e1Geffab9+/dr6NChKleunLZs2aLg4GCuVL0GJfmPJv6MhiRp/vz56tatm6KiorR06VJFRUVp7969Sk1N1WOPPWZ3e25l2LBh2rZtm3744QeXZRdatmypV199lWB1FS5eDHDxoorbbrvN3Jafn69Vq1Zx37tr8Pzzz2vFihV699131a1bN/373//Wb7/9pvfee09vvvmm3e25je3bt6tly5ZyOp369ddf1bt3b5UrV04LFizQwYMHNWvWLLtbdBuGYVx2odpt27a5/4UBdi2gheKlbt26xpQpUwzDMIzbbrvN2L9/v3H+/Hmjd+/exiuvvGJzd+6lcuXKxrp16wzD+P/70jAMY+/evYa/v7+drbmNqlWrGlWrVjUcDocRFhZmPq9atapRq1YtIyoqyli/fr3dbbqNsLAwY8WKFYZhGIa/v7+xd+9ewzAMY9asWcZf/vIXGztzLy1atDCGDh1qGIbr/9tr1qwxqlSpYmNn7qNs2bJGYGCg4eHhYX598REQEGB4eHgYffv2tbvNG8KMFSRJ+/fvV9u2bSVJPj4+ysrKksPh0KBBg/Twww//4Umb+P+OHTtWYHpbkrlP8ccOHDgg6cKVq1988YUCAwNt7si9nThxwrxyNSAgwFxe4YEHHtA//vEPO1tzK5s2bdJ7771XYLxSpUpKTU21oSP3M3HiRBmGoZ49e2rkyJFyOp3mNm9vb1WtWtVcpsZdEawg6cICbadOnZJ04R+JpKQk1a1bVydPntSZM2ds7s693HfffVq8eLH69+8vSWaYmjZtmtv/g1HUVqxYIYkLAW7UnXfeqV9//VVVqlRRnTp19Mknn6hBgwZatGiReXNr/LHSpUtf9u4Ju3fvVsWKFW3oyP1cXNqjWrVqatKkSYn8/5mV1yFJevDBB7V06VJJUqdOnTRgwAD17t1bTz31lFq0aGFzd+5l9OjRGj58uP7xj3/o3LlzmjRpkh555BHNmDFDb7zxht3tuZXs7Gz16tVLZcqU0d13363k5GRJUmxsLOcGXYOnn35a27Ztk3ThHMB3331XPj4+GjRokIYOHWpzd+7j0Ucf1WuvvWZeVelwOJScnKwXX3xRTzzxhM3duRd/f3/t3LnTfP7ll1+qY8eOeumllwpcPOV27D4WieLh+PHjxm+//WYYhmHk5+cbb731ltG+fXtj0KBBxokTJ2zuzv1s377d6Natm3H33XcbtWvXNrp06WJs377d7rbcTmxsrBEZGWn8+OOPhp+fn3lOy5dffmnUq1fP5u7c18GDB43PP//cSExMtLsVt5KRkWE0adLEKFu2rOHp6WmEhYUZpUqVMh566CHj9OnTdrfnVu69917js88+MwzDMPbv32/4+PgYTz31lFGjRg1jwIAB9jZ3g1huATp37pzmzJmjVq1aKSQkxO52AFOVKlX08ccf6/7775e/v7+2bdumO++8U/v27dOf//xnbmr9B7Kzs7Vs2TK1a9dO0oXZqpycHHO7l5eXXnvtNZUuXdquFt3S8uXLtWXLFp0/f15//vOf1bJlS7tbcjtOp1NbtmxR9erV9dZbb2n58uX67rvvtGbNGj355JNKSUmxu8XrVvIObuKaeXl56R//+IfLtCyuX0len6WocSHAjZk1a5a+/vprM1hNmTJFd999t3x9fSVJu3bt0u23365BgwbZ2abb+PXXX1W1alU9/PDDevjhh+1ux60ZhqHz589Lkr7//nvzZzQsLEz/+9//7GzthnGOFSRJDRs21NatW+1uo0S40iRwTk6OvL29i7gb93bxQoCLuBDg2syZM0c9e/Z0GZs7d65WrFihFStWaMyYMfrkk09s6s793HnnnXrggQf03nvvcePqG3Tvvffq9ddf10cffaSVK1eaV6UfOHBAwcHBNnd3Y5ixgiSpb9++iouL06FDhxQZGVng5qJ/+tOfbOrMfbCopfVGjx6t1q1b6+effzYvBNixY4fWrVunlStX2t1esbdnzx7VqlXLfF66dGl5ePz/v6cbNGig5557zo7W3NLmzZs1b948vf766xowYIBatWqlrl27qkOHDvLx8bG7PbcyceJEdenSRQsXLtTw4cNVo0YNSdJnn32mxo0b29zdjeEcK0iSyz+2FzkcDnN1XA5f/bGL6wQdPHhQd9xxhzw9Pc1tF9dnee2119SwYUO7WnRLP/30k8aOHauEhATznJYXXnhBdevWtbu1Ys/X11eJiYkKDw+/7PZdu3apXr16Onv2bBF35t4Mw9APP/yguXPn6vPPP1d+fr6eeOIJffDBB3a35vbOnj0rT09PlSpVyu5WrhvBCpIuhIHCVKlSpYg6cX8saoniombNmnrzzTevuBTAJ598opdeekn79u0r4s5Kji1btqhXr17avn07f4Beg9GjR2vYsGEFxvPz89W1a1fNmzfPhq6swTlWkHQhWFWqVElVqlRxeVSqVOkPQxdcrVixwgxVhmFc8ZwrXJmHh4c8PT0LfZTEhQWt1qZNG73yyiuXnZHKzs7WyJEjzXNbcPVSUlL09ttvq169errvvvvk5+enKVOm2N2WW5k4caLef/99l7H8/Hw9+eSTSkxMtKcpizBjBUlcyWa1WbNmacyYMdq7d68kqVatWho6dKhiYmJs7sw9fPnll1fctnbtWk2ePFmGYSg7O7sIu3I/R48eVb169eTt7a1+/fqpVq1acjgc2rVrl6ZMmaJz585p69atbn+ycFF5//33NWfOHK1Zs0bh4eHq0qWLoqOjVbVqVbtbczsJCQlq2bKl3nvvPXXq1El5eXnq3Lmzdu3apeXLl7v30j82rJ2FYsjhcBhpaWkFxnfv3s2Ng6/RuHHjjDJlyhjPP/+88eWXXxoLFy40hg4dapQpU8YYP3683e25rZ07dxodO3Y0PD09jW7duhkHDx60uyW38MsvvxitWrUyPDw8DIfDYTgcDsPDw8No1aqVueAqrs4dd9xhDBkyxNi6davdrZQIK1asMAICAoyFCxca7du3N+rUqWOkpqba3dYNY8bqFvf4449LujBD0Lp1a5crW/Lz87V9+3aFh4dryZIldrXodqpVq6aRI0eqW7duLuMzZ87UiBEjzBsM4+ocPnxYr776qmbOnKlWrVpp9OjRioiIsLstt3PixAnzXKoaNWqoXLlyNnfkfoz/u5gH1vnqq6/0xBNPqHbt2lq+fLkqVKhgd0s3jJMUbnEX7yxuGIb8/f3NhQOlC1ey3X///erdu7dd7bmlI0eOXPZy4caNG+vIkSM2dOSeMjIyNGrUKE2ePFn16tXTsmXL9OCDD9rdltsqV66cGjRoYHcbbs3hcOjkyZPauHGj0tLSzAUuL7r0jym4uviH/KUqVqyosmXLqk+fPubYF198UVRtWY5gdYv78MMPJUlVq1bVkCFDCqxfhWtXo0YN82qr3/v4449Vs2ZNm7pyL2+//bbeeusthYSEaN68eXr00UftbgnQokWL1KVLF2VlZcnf399l9srhcBCs/sDFP+Qv1apVqyLu5ObiUCBgsc8//1ydO3dWy5Yt1aRJEzkcDq1evVrLli3TJ598oscee8zuFos9Dw8P+fr6qmXLli7rgV3Knf+qhfupVauW2rRpo1GjRqlMmTJ2t+O2DMNQcnKyKlasWCL3I8HqFla/fv2rPl9gy5YtN7mbkiUhIUHjx4/Xrl27ZBiG6tSpo7i4ONWvX9/u1txCjx49rupn8+KMK1AU/Pz89NNPP+nOO++0uxW3dv78eZUuXVo7duwokbP4HAq8hXXs2NHuFkqsyMhIzZkzx+423NaMGTPsbgEooFWrVtq8eTPB6gZ5eHioZs2aOn78eIkMVsxYARbx8PD4w1kWh8Ohc+fOFVFHAKw0ffp0vfbaa3r66adVt27dArdd6dChg02duZ/FixfrzTff1NSpU0vcVb4Eq1vclcJAQECAwsPD9fzzz1/xSg64YlFLoGS73D1VL+KeqtcmMDBQZ86c0blz5+Tt7e1yRbp0YXkQd8WhwFvcF198cdlgdfGS4q5du2rmzJn629/+ZkN37uVyV67t2rVLw4YNM68m+te//mVDZwCscOnyCrh+EydOtLuFm4YZKxTq3//+t2bNmqUNGzbY3YpbYVFLALg1EaxQqL1796pBgwZKT0+3uxW3cOmilm+99RaLWgIlyMqVKzV27Fjt3LlTDodDtWvX1tChQ/n//Drk5+dr4cKF5r6sU6eOOnToUOgSK+6AQ4EoVHZ2tkqXLm13G26BRS2Bkm327Nl6+umn9fjjjys2NlaGYWjt2rVq0aKFZsyYoejoaLtbdBv79u1TmzZt9Ntvvyk8PFyGYWjPnj0KCwvT4sWLVb16dbtbvG7MWKFQ/fv31/79+/XNN9/Y3Uqxx6KWQMlWu3Zt9enTR4MGDXIZHz9+vKZNm6adO3fa1Jn7adOmjQzD0Jw5c8z7Vh4/flxdu3aVh4eHFi9ebHOH149gdYsbPHjwZcczMjK0efNm7d+/Xz/++CMLW14FFrUESjYfHx/t2LFDNWrUcBnft2+fIiIidPbsWZs6cz9+fn5av3696tat6zK+bds2NWnSRKdPn7apsxvHocBb3NatWy87HhAQoNatW6tv376qUqVKEXflnljUEijZwsLCtGzZsgLBatmyZQoLC7OpK/fk4+OjU6dOFRg/ffq0vL29bejIOgSrW9yKFSvsbgEA3EJcXJxiY2OVmJioxo0bm/cBnTFjhiZNmmR3e26lXbt26tOnj6ZPn64GDRpIkjZs2KBnn33W7Rda5VAgAABXacGCBRo3bpx5PtXFqwK5WOXq7Nu3TzVq1NDJkyfVvXt3LVq0yFzBPi8vT48++qhmzJghp9Npc6fXj2AFAACKhIeHhypVqqTmzZurefPmatKkiXbv3m3erP7Sw6zuiEOBAAAUIj09XbNnz1b37t0VEBDgsi0jI0OzZs267DYUtHLlSq1cuVI//PCD+vXrp7Nnz6py5cp6+OGHlZmZKV9fX1WqVMnuNm8IM1YAABTiX//6l7Zv365PP/30sts7deqke+65R8OHDy/iztxbXl6e1q1bpx9++EE//PCD1q9fr5ycHNWoUUO7d++2u73rRrACAKAQ9erV07hx49SiRYvLbl+2bJmGDBlyxausUbjs7GytXr1a3333naZNm6bTp0+79Q2tORQIAEAh9u/fr5o1a15xe82aNbV///4i7Mi9nT17VmvXrtWKFSv0ww8/aNOmTapWrZqaNm2qqVOnqmnTpna3eEMIVgAAFMLT01OHDx9W5cqVL7v98OHD8vDwKOKu3FPTpk21adMmVa9eXQ899JD69++vpk2bKjg42O7WLMNPAgAAhahfv74WLlx4xe0LFizg7hRXae3atapQoYKaN2+uFi1a6OGHHy5RoUoiWAEAUKh+/fpp3LhxmjJlisu5P/n5+Zo8ebImTJig5557zsYO3cfJkyf1/vvvq0yZMnrrrbdUqVIl1a1bV/369dNnn32mY8eO2d3iDePkdQAA/sDw4cM1evRo+fv7684775TD4dD+/ft1+vRpDR06VG+++abdLbqlU6dOafXq1eb5Vtu2bVPNmjWVlJRkd2vXjWAFAMBV2Lhxo+bMmaN9+/bJMAzVqlVL0dHR5i1ZcO3Onz+vTZs2acWKFVqxYoVWr16ts2fPuvVVgQQrAABQJM6fP6/Nmzfrhx9+0IoVK7RmzRplZWW5rMbevHlzValSxe5WrxvBCgAAFImAgABlZWXp9ttvV7NmzdSsWTM1b95c1atXt7s1yxCsAABAkXjvvffUvHlz1apVy+5WbhqCFQAAgEVYbgEAAMAiBCsAAACLEKwAALgBO3fu1J133ml3GygmCFYAANyA3NxcHTx40O42UExwE2YAAAoxePDgQreXhNuwwDpcFQgAQCE8PT1Vr149BQQEXHb76dOntWXLFrdeLRzWYcYKAIBC1KxZU4MGDVLXrl0vuz0xMVGRkZFF3BWKK86xAgCgEJGRkUpISLjidofDIQ7+4CIOBQIAUIjU1FTl5OS49f3rUHQIVgAAABbhUCAAAIBFCFYAAAAWIVgBAABYhGAFAABgEYIVAACARVggFACAq5Cfn68ZM2Zo2bJlSktL0/nz5122L1++3KbOUJwQrAAAuAoDBgzQjBkz1LZtW0VERMjhcNjdEooh1rECAOAqVKhQQbNmzVKbNm3sbgXFGOdYAQBwFby9vVWjRg2720AxR7ACAOAqxMXFadKkSdwXEIXiUCAAAFfhscce04oVK1SuXDndfffdKlWqlMv2L774wqbOUJxw8joAAFehbNmyeuyxx+xuA8UcM1YAAAAW4RwrAAAAi3AoEACAK/jzn/+sZcuWKTAwUPXr1y907aotW7YUYWcorghWAABcwaOPPiofHx9JUseOHe1tBm6Bc6wAAAAswjlWAAAAFiFYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEA8Aeys7O1evVq/fzzzwW2nT17VrNmzbKhKxRHLLcAAEAh9uzZo6ioKCUnJ8vhcOjBBx/UvHnzdPvtt0uSjh49qtDQUOXn59vcKYoDZqwAACjECy+8oLp16yotLU27d+9WQECAmjRpouTkZLtbQzHEjBUAAIUIDg7W999/r7p165pjzz33nL7++mutWLFCfn5+zFjBxC1tAAAoRHZ2try8XH9d/vvf/5aHh4eaNm2quXPn2tQZiiOCFQAAhbjrrru0efNm1a5d22V88uTJMgxDHTp0sKkzFEecYwUAQCEee+wxzZs377LbpkyZoqeeekqcVYOLOMcKAADAIsxYAQAAWIRgBQAAYBGCFQAAgEUIVgAAABYhWAEAAFiEYAXAbTkcjkIfPXr0uOk9jBgxQg6HQ88++6zLeGJiohwOh3799deb3gOA4oNgBcBtHTlyxHxMnDhRAQEBLmOTJk0qkj5Kly6t6dOna8+ePUXyfgCKL4IVALcVEhJiPpxOpxwOh0JCQhQcHKwHHnhA06ZNc6lPSkqSh4eH9u/fL+nCjNfUqVP1l7/8Rb6+vqpWrZo+/fRTl9f89ttv6ty5swIDA1W+fHk9+uijBWahwsPD1bx5c/3zn/+8Yq/5+fnq1auXqlWrJl9fX4WHhxcIfj169FDHjh01atQoBQcHq2zZsho5cqTOnTunoUOHqly5crrjjjv0wQcfXHOPAIoGwQpAieNwONSzZ099+OGHLuMffPCBHnzwQVWvXt0ce/nll/XEE09o27Zt6tq1q5566int3LlTknTmzBk1b95ct912m1atWqXVq1frtttuU+vWrZWbm+vyvd988019/vnn2rRp02V7On/+vO644w598skn+vnnn/XKK6/opZde0ieffOJSt3z5ch0+fFirVq3S+PHjNWLECLVr106BgYHasGGDnn32WT377LNKSUm55h4BFAEDAEqADz/80HA6nebzw4cPG56ensaGDRsMwzCM3Nxco2LFisaMGTPMGknGs88+6/J9GjZsaPzjH/8wDMMwpk+fboSHhxvnz583t+fk5Bi+vr7Gd999ZxiGYbz66qvGPffcYxiGYTz55JPGww8/bBiGYWzdutWQZBw4cOCKPfft29d44oknzOfdu3c3qlSpYuTn55tj4eHhxoMPPmg+P3funOHn52fMmzfvqnsEUHS4CTOAEun2229X27Zt9cEHH6hBgwb6+uuvdfbsWf3tb39zqWvUqFGB54mJiZKkhIQE7du3T/7+/i41Z8+eNQ8n/t7rr7+u2rVrKz4+XkFBQQW2/+c//9F///tfHTx4UNnZ2crNzVW9evVcau6++255ePz/gwnBwcGKiIgwn3t6eqp8+fJKS0u7rh4B3FwEKwAl1t///nfFxMRowoQJ+vDDD9W5c2eVKVPmD1/ncDgkXTh8FxkZqTlz5hSoqVixYoGx6tWrq3fv3nrxxRc1ffp0l22ffPKJBg0apHHjxqlRo0by9/fXmDFjtGHDBpe6UqVKFejlcmPnz5+/rh4B3FwEKwAlVps2beTn56epU6fq22+/1apVqwrUrF+/Xt26dXN5Xr9+fUnSn//8Z3388ccKCgpSQEDAVb3nK6+8ourVq2v+/Pku4z/++KMaN26svn37mmNWzChdT48Abh5OXgdQYnl6eqpHjx4aNmyYatSoUeCwnyR9+umn+uCDD7Rnzx69+uqr2rhxo/r16ydJ6tKliypUqKBHH31UP/74ow4cOKCVK1dqwIABOnTo0GXfMzg4WIMHD9Y777zjMl6jRg1t3rxZ3333nfbs2aOXX375iie6X4vr6RHAzUOwAlCi9erVS7m5uerZs+dlt48cOVLz58/Xn/70J82cOVNz5sxRnTp1JEllypTRqlWrVLlyZT3++OOqXbu2evbsqezs7EJnh4YOHarbbrvNZezZZ5/V448/rs6dO6thw4Y6fvy4y+zV9breHgHcHA7DMAy7mwCAm2XNmjVq1qyZDh06pODgYJdtDodDCxYsUMeOHe1pDkCJwzlWAEqknJwcpaSk6OWXX1anTp0KhCoAuBk4FAigRJo3b57Cw8OVkZGht99+2+52ANwiOBQIAABgEWasAAAALEKwAgAAsAjBCgAAwCIEKwAAAIsQrAAAACxCsAIAALAIwQoAAMAiBCsAAACLEKwAAAAs8v8AfGVpqdi2qsAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"TypeName\"], y=df[\"Price\"])\n",
+    "plt.xticks(rotation='vertical')   \n",
+    "plt.show() # To See the average value of the TypeName"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "1dfe2363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#laptop which you want to buy makes the big diffeence in price "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83161cb3",
+   "metadata": {},
+   "source": [
+    "# Column Inches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "fa51b837",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\4187967466.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(df[\"Inches\"])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Inches', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"Inches\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "c8dbba0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Inches', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x=df[\"Inches\"],y=df[\"Price\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "f77a4a40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scatter plot shows that as the laptop size is increasing the price is also increasing /\n",
+    "#it was not a strong correleation but a minor one"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e862e1c9",
+   "metadata": {},
+   "source": [
+    "# Column ScreenResolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e4b216d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "\n",
+       "                    Cpu  Ram     Memory                           Gpu  OpSys  \\\n",
+       "0  Intel Core i5 2.3GHz    8  128GB SSD  Intel Iris Plus Graphics 640  macOS   \n",
+       "\n",
+       "   Weight       Price  \n",
+       "0    1.37  71378.6832  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "5a2324e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Full HD 1920x1080                                507\n",
+       "1366x768                                         281\n",
+       "IPS Panel Full HD 1920x1080                      230\n",
+       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
+       "Full HD / Touchscreen 1920x1080                   47\n",
+       "1600x900                                          23\n",
+       "Touchscreen 1366x768                              16\n",
+       "Quad HD+ / Touchscreen 3200x1800                  15\n",
+       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
+       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
+       "4K Ultra HD / Touchscreen 3840x2160               10\n",
+       "4K Ultra HD 3840x2160                              7\n",
+       "Touchscreen 2560x1440                              7\n",
+       "IPS Panel 1366x768                                 7\n",
+       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
+       "IPS Panel Retina Display 2560x1600                 6\n",
+       "IPS Panel Retina Display 2304x1440                 6\n",
+       "Touchscreen 2256x1504                              6\n",
+       "IPS Panel Touchscreen 2560x1440                    5\n",
+       "IPS Panel Retina Display 2880x1800                 4\n",
+       "IPS Panel Touchscreen 1920x1200                    4\n",
+       "1440x900                                           4\n",
+       "IPS Panel 2560x1440                                4\n",
+       "IPS Panel Quad HD+ 2560x1440                       3\n",
+       "Quad HD+ 3200x1800                                 3\n",
+       "1920x1080                                          3\n",
+       "Touchscreen 2400x1600                              3\n",
+       "2560x1440                                          3\n",
+       "IPS Panel Touchscreen 1366x768                     3\n",
+       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
+       "IPS Panel Full HD 2160x1440                        2\n",
+       "IPS Panel Quad HD+ 3200x1800                       2\n",
+       "IPS Panel Retina Display 2736x1824                 1\n",
+       "IPS Panel Full HD 1920x1200                        1\n",
+       "IPS Panel Full HD 2560x1440                        1\n",
+       "IPS Panel Full HD 1366x768                         1\n",
+       "Touchscreen / Full HD 1920x1080                    1\n",
+       "Touchscreen / Quad HD+ 3200x1800                   1\n",
+       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
+       "IPS Panel Touchscreen 2400x1600                    1\n",
+       "Name: ScreenResolution, dtype: int64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"ScreenResolution\"].value_counts() #data inside screenresolution and its value counts(occurence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "a31dce70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Touchscreen\"]=df[\"ScreenResolution\"].apply(lambda x: 1 if \"Touchscreen\" in x else 0) #Creating a new column only for those values wich have touch screen laptops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "7b2f5f50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1256</th>\n",
+       "      <td>Asus</td>\n",
+       "      <td>Gaming</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>IPS Panel Full HD 1920x1080</td>\n",
+       "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>128GB SSD +  1TB HDD</td>\n",
+       "      <td>Nvidia GeForce GTX 970M</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>101232.00</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Dell</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>IPS Panel Full HD 1920x1080</td>\n",
+       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD +  1TB HDD</td>\n",
+       "      <td>AMD Radeon 530</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>52161.12</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>420</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>IPS Panel 4K Ultra HD / Touchscreen 3840x2160</td>\n",
+       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD</td>\n",
+       "      <td>Nvidia GeForce GTX 1050</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>101178.72</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>945</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>Gaming</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>IPS Panel Full HD 1920x1080</td>\n",
+       "      <td>AMD FX 8800P 2.1GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD +  1TB HDD</td>\n",
+       "      <td>AMD Radeon R9 M385</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>53226.72</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1191</th>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>IPS Panel Touchscreen 2400x1600</td>\n",
+       "      <td>Samsung Cortex A72&amp;A53 2.0GHz</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32GB Flash Storage</td>\n",
+       "      <td>ARM Mali T860 MP4</td>\n",
+       "      <td>Chrome OS</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>35111.52</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company            TypeName  Inches  \\\n",
+       "1256     Asus              Gaming    17.3   \n",
+       "37       Dell            Notebook    17.3   \n",
+       "420    Lenovo  2 in 1 Convertible    15.6   \n",
+       "945    Lenovo              Gaming    15.6   \n",
+       "1191  Samsung  2 in 1 Convertible    12.3   \n",
+       "\n",
+       "                                   ScreenResolution  \\\n",
+       "1256                    IPS Panel Full HD 1920x1080   \n",
+       "37                      IPS Panel Full HD 1920x1080   \n",
+       "420   IPS Panel 4K Ultra HD / Touchscreen 3840x2160   \n",
+       "945                     IPS Panel Full HD 1920x1080   \n",
+       "1191                IPS Panel Touchscreen 2400x1600   \n",
+       "\n",
+       "                                Cpu  Ram                Memory  \\\n",
+       "1256    Intel Core i7 6700HQ 2.6GHz   16  128GB SSD +  1TB HDD   \n",
+       "37       Intel Core i5 8250U 1.6GHz    8  128GB SSD +  1TB HDD   \n",
+       "420     Intel Core i7 7700HQ 2.8GHz   16             512GB SSD   \n",
+       "945             AMD FX 8800P 2.1GHz   16  512GB SSD +  1TB HDD   \n",
+       "1191  Samsung Cortex A72&A53 2.0GHz    4    32GB Flash Storage   \n",
+       "\n",
+       "                          Gpu       OpSys  Weight      Price  Touchscreen  \n",
+       "1256  Nvidia GeForce GTX 970M  Windows 10    4.00  101232.00            0  \n",
+       "37             AMD Radeon 530  Windows 10    2.80   52161.12            0  \n",
+       "420   Nvidia GeForce GTX 1050  Windows 10    2.00  101178.72            1  \n",
+       "945        AMD Radeon R9 M385  Windows 10    2.50   53226.72            0  \n",
+       "1191        ARM Mali T860 MP4   Chrome OS    1.15   35111.52            1  "
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)\n",
+    "df.sample(2)\n",
+    "df.sample(5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "190fb140",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Touchscreen\"].value_counts().plot(kind=\"bar\") # TO see how many laptops are touchscreen and how many are not "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "59a1c9a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Touchscreen', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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J7/dr+PDhWrt2rWbOnClJevfdd5WcnKwnnnhCeXl52r17tzIyMtTQ0KDs7GxJUkNDg3JycvT6668rLS1NTz75pPLz89Xa2iqPxyNJqqmp0ezZs9XR0aG4uLjPPJ+uri4ZhiG/32+rHgA+qeW20aFuAQhLI2959bTu3+7v75CuNP3xj3/URRddpB/+8IdKSkrSBRdcoFWrVlnze/fuVXt7u3Jzc60xp9Op8ePHa+vWrZKkxsZG9fb2BtV4PB5lZmZaNdu2bZNhGFZgkqQxY8bIMIygmszMTCswSVJeXp4CgUDQ5cJPCgQC6urqCtoAAMCZKaSh6a233tIDDzyg1NRUPfXUU7r++utVUlKiRx55RJLU3t4uSXK5XEHvc7lc1lx7e7uioqIUHx9/wpqkpKR+x09KSgqqOfY48fHxioqKsmqOVVlZad0jZRiGkpOTT/YjAAAAg0RIQ9PRo0d14YUXqqKiQhdccIHmzZunoqIiPfDAA0F1Docj6LVpmv3GjnVszfHqB1LzSYsXL5bf77e21tbWE/YEAAAGr8hQHnzEiBHKyMgIGktPT9eGDRskSW63W9JHq0AjRoywajo6OqxVIbfbrZ6eHvl8vqDVpo6ODo0dO9aq2b9/f7/jd3Z2Bu1n+/btQfM+n0+9vb39VqA+5nQ65XQ6T+qccWa48cYb1dnZKUkaPny47r333hB3BAA43UK60jRu3Djt2bMnaOzvf/+7Ro0aJUlKSUmR2+3W5s2brfmenh5t2bLFCkRZWVkaMmRIUE1bW5uam5utmpycHPn9fu3YscOq2b59u/x+f1BNc3Oz2trarJq6ujo5nU5lZWWd4jPHYNfZ2an9+/dr//79VngCAJzZQrrS9POf/1xjx45VRUWFZsyYoR07dujBBx/Ugw8+KOmjy2WlpaWqqKhQamqqUlNTVVFRoaFDh6qwsFCSZBiG5syZo7KyMiUkJGjYsGEqLy/X6NGjNWnSJEkfrV5NmTJFRUVFWrlypSRp7ty5ys/PV1pamiQpNzdXGRkZ8nq9qqqq0oEDB1ReXq6ioiK+CQcAAEIbmr773e9q48aNWrx4sW677TalpKTonnvu0TXXXGPVLFy4UN3d3SouLpbP51N2drbq6uoUGxtr1axYsUKRkZGaMWOGuru7NXHiRK1Zs0YRERFWzbp161RSUmJ9y66goEDV1dXWfEREhDZt2qTi4mKNGzdO0dHRKiws1LJly76ATwIAAIS7kD6n6UzDc5q+PAoLC6375Fwul9avXx/ijnAm4DlNwPHxnCYAAIBBhNAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANoT0OU04eVk3PRLqFiApznfY+n8cbb7D/OcSBhqrfhzqFgCc4VhpAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCBJ4IDA3B0SMxxfwYAnLkITcAAHE6bGuoWAABfMC7PAQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANIQ1NS5YskcPhCNrcbrc1b5qmlixZIo/Ho+joaE2YMEG7du0K2kcgEND8+fOVmJiomJgYFRQUaN++fUE1Pp9PXq9XhmHIMAx5vV4dPHgwqKalpUXTp09XTEyMEhMTVVJSop6entN27gAAYHAJ+UrTeeedp7a2Nmt79dVXrbmlS5dq+fLlqq6u1s6dO+V2uzV58mQdOnTIqiktLdXGjRtVU1Oj+vp6HT58WPn5+err67NqCgsL1dTUpNraWtXW1qqpqUler9ea7+vr07Rp03TkyBHV19erpqZGGzZsUFlZ2RfzIQAAgLAXGfIGIiODVpc+Zpqm7rnnHt1888268sorJUkPP/ywXC6X1q9fr3nz5snv9+uhhx7S2rVrNWnSJEnSo48+quTkZD399NPKy8vT7t27VVtbq4aGBmVnZ0uSVq1apZycHO3Zs0dpaWmqq6vTa6+9ptbWVnk8HknS3XffrdmzZ+uOO+5QXFzcF/RpAACAcBXylaY33nhDHo9HKSkpuuqqq/TWW29Jkvbu3av29nbl5uZatU6nU+PHj9fWrVslSY2Njert7Q2q8Xg8yszMtGq2bdsmwzCswCRJY8aMkWEYQTWZmZlWYJKkvLw8BQIBNTY2fmrvgUBAXV1dQRsAADgzhTQ0ZWdn65FHHtFTTz2lVatWqb29XWPHjtX777+v9vZ2SZLL5Qp6j8vlsuba29sVFRWl+Pj4E9YkJSX1O3ZSUlJQzbHHiY+PV1RUlFVzPJWVldZ9UoZhKDk5+SQ/AQAAMFiENDRNnTpV//Iv/6LRo0dr0qRJ2rRpk6SPLsN9zOFwBL3HNM1+Y8c6tuZ49QOpOdbixYvl9/utrbW19YR9AQCAwSvkl+c+KSYmRqNHj9Ybb7xh3ed07EpPR0eHtSrkdrvV09Mjn893wpr9+/f3O1ZnZ2dQzbHH8fl86u3t7bcC9UlOp1NxcXFBGwAAODOFVWgKBALavXu3RowYoZSUFLndbm3evNma7+np0ZYtWzR27FhJUlZWloYMGRJU09bWpubmZqsmJydHfr9fO3bssGq2b98uv98fVNPc3Ky2tjarpq6uTk6nU1lZWaf1nAEAwOAQ0m/PlZeXa/r06Ro5cqQ6Ojp0++23q6urS7NmzZLD4VBpaakqKiqUmpqq1NRUVVRUaOjQoSosLJQkGYahOXPmqKysTAkJCRo2bJjKy8uty32SlJ6erilTpqioqEgrV66UJM2dO1f5+flKS0uTJOXm5iojI0Ner1dVVVU6cOCAysvLVVRUxOoRAACQFOLQtG/fPl199dV67733NHz4cI0ZM0YNDQ0aNWqUJGnhwoXq7u5WcXGxfD6fsrOzVVdXp9jYWGsfK1asUGRkpGbMmKHu7m5NnDhRa9asUUREhFWzbt06lZSUWN+yKygoUHV1tTUfERGhTZs2qbi4WOPGjVN0dLQKCwu1bNmyL+iTAAAA4c5hmqYZ6ibOFF1dXTIMQ36//7StUGXd9Mhp2S8w2DVW/TjULXxuLbeNDnULQFgaecurn130Odj9/R1W9zQBAACEK0ITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsOFzhaY333xTTz31lLq7uyV99AduAQAAzkQDCk3vv/++Jk2apHPPPVff//73rb/Zdt1116msrOyUNggAABAOBhSafv7znysyMlItLS0aOnSoNT5z5kzV1taesuYAAADCxYD+9lxdXZ2eeuopnXPOOUHjqampevvtt09JYwAAAOFkQCtNR44cCVph+th7770np9P5uZsCAAAINwMKTZdeeqkeeeT//nCsw+HQ0aNHVVVVpcsuu+yUNQcAABAuBnR5rqqqShMmTNCLL76onp4eLVy4ULt27dKBAwf017/+9VT3CAAAEHIDWmnKyMjQK6+8oosvvliTJ0/WkSNHdOWVV+rll1/WN77xjVPdIwAAQMgNaKVJktxut37961+fyl4AAADC1oBWmlavXq3f//73/cZ///vf6+GHH/7cTQEAAISbAYWmO++8U4mJif3Gk5KSVFFR8bmbAgAACDcDCk1vv/22UlJS+o2PGjVKLS0tn7spAACAcDOg0JSUlKRXXnml3/jf/vY3JSQkfO6mAAAAws2AQtNVV12lkpISPfvss+rr61NfX5+eeeYZ3XjjjbrqqqtOdY8AAAAhN6Bvz91+++16++23NXHiREVGfrSLo0eP6sc//jH3NAEAgDPSgEJTVFSUHnvsMf3mN7/R3/72N0VHR2v06NEaNWrUqe4PAAAgLAz4OU2SdO655+rcc889Vb0AAACELduhacGCBfrNb36jmJgYLViw4IS1y5cv/9yNAQAAhBPboenll19Wb2+vJOmll16Sw+E4bt2njQMAAAxmtkPTs88+a/383HPPnY5eAAAAwtZJP3Lgww8/VGRkpJqbm09HPwAAAGHppENTZGSkRo0apb6+vtPRDwAAQFga0MMtf/nLX2rx4sU6cODAqe4HAAAgLA3okQP//u//rjfffFMej0ejRo1STExM0PxLL710SpoDAAAIFwMKTVdccYUcDodM0zzV/QAAAISlk7o898EHH+iGG27Qgw8+qOrqar3++uu64YYbdOuttwZtA1FZWSmHw6HS0lJrzDRNLVmyRB6PR9HR0ZowYYJ27doV9L5AIKD58+crMTFRMTExKigo0L59+4JqfD6fvF6vDMOQYRjyer06ePBgUE1LS4umT5+umJgYJSYmqqSkRD09PQM6FwAAcOY5qdB06623as2aNZo2bZquvvpqPf300/rpT3/6uZvYuXOnHnzwQZ1//vlB40uXLtXy5ctVXV2tnTt3yu12a/LkyTp06JBVU1paqo0bN6qmpkb19fU6fPiw8vPzg25ULywsVFNTk2pra1VbW6umpiZ5vV5rvq+vT9OmTdORI0dUX1+vmpoabdiwQWVlZZ/73AAAwJnhpC7PPf7443rooYd01VVXSZKuueYajRs3Tn19fYqIiBhQA4cPH9Y111yjVatW6fbbb7fGTdPUPffco5tvvllXXnmlJOnhhx+Wy+XS+vXrNW/ePPn9fj300ENau3atJk2aJEl69NFHlZycrKefflp5eXnavXu3amtr1dDQoOzsbEnSqlWrlJOToz179igtLU11dXV67bXX1NraKo/HI0m6++67NXv2bN1xxx2Ki4s7bu+BQECBQMB63dXVNaDPAAAAhL+TWmlqbW3VJZdcYr2++OKLFRkZqXfffXfADdxwww2aNm2aFXo+tnfvXrW3tys3N9caczqdGj9+vLZu3SpJamxsVG9vb1CNx+NRZmamVbNt2zYZhmEFJkkaM2aMDMMIqsnMzLQCkyTl5eUpEAiosbHxU3uvrKy0LvkZhqHk5OQBfw4AACC8nVRo6uvrU1RUVNBYZGSkPvzwwwEdvKamRi+99JIqKyv7zbW3t0uSXC5X0LjL5bLm2tvbFRUVpfj4+BPWJCUl9dt/UlJSUM2xx4mPj1dUVJRVczyLFy+W3++3ttbW1s86ZQAAMEid1OU50zQ1e/ZsOZ1Oa+yf//ynrr/++qDHDjz++OOfua/W1lbdeOONqqur09lnn/2pdcf+LTvTND/z79sdW3O8+oHUHMvpdAZ9FgAA4Mx1UqFp1qxZ/cZ+9KMfDejAjY2N6ujoUFZWljXW19en559/XtXV1dqzZ4+kj1aBRowYYdV0dHRYq0Jut1s9PT3y+XxBq00dHR0aO3asVbN///5+x+/s7Azaz/bt24PmfT6fent7+61AAQCAL6eTCk2rV68+ZQeeOHGiXn311aCxn/zkJ/rWt76lRYsW6etf/7rcbrc2b96sCy64QJLU09OjLVu26K677pIkZWVlaciQIdq8ebNmzJghSWpra1Nzc7OWLl0qScrJyZHf79eOHTt08cUXS5K2b98uv99vBaucnBzdcccdamtrswJaXV2dnE5nUKgDAABfXgN6uOWpEBsbq8zMzKCxmJgYJSQkWOOlpaWqqKhQamqqUlNTVVFRoaFDh6qwsFCSZBiG5syZo7KyMiUkJGjYsGEqLy/X6NGjrRvL09PTNWXKFBUVFWnlypWSpLlz5yo/P19paWmSpNzcXGVkZMjr9aqqqkoHDhxQeXm5ioqKPvWbcwAA4MslZKHJjoULF6q7u1vFxcXy+XzKzs5WXV2dYmNjrZoVK1YoMjJSM2bMUHd3tyZOnKg1a9YEPQJh3bp1Kikpsb5lV1BQoOrqams+IiJCmzZtUnFxscaNG6fo6GgVFhZq2bJlX9zJAgCAsOYw+Vsop0xXV5cMw5Df7z9tK1RZNz1yWvYLDHaNVT8OdQufW8tto0PdAhCWRt7y6mcXfQ52f3+f1CMHAAAAvqwITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMCGkIamBx54QOeff77i4uIUFxennJwcPfnkk9a8aZpasmSJPB6PoqOjNWHCBO3atStoH4FAQPPnz1diYqJiYmJUUFCgffv2BdX4fD55vV4ZhiHDMOT1enXw4MGgmpaWFk2fPl0xMTFKTExUSUmJenp6Ttu5AwCAwSWkoemcc87RnXfeqRdffFEvvviiLr/8cv3gBz+wgtHSpUu1fPlyVVdXa+fOnXK73Zo8ebIOHTpk7aO0tFQbN25UTU2N6uvrdfjwYeXn56uvr8+qKSwsVFNTk2pra1VbW6umpiZ5vV5rvq+vT9OmTdORI0dUX1+vmpoabdiwQWVlZV/chwEAAMKawzRNM9RNfNKwYcNUVVWla6+9Vh6PR6WlpVq0aJGkj1aVXC6X7rrrLs2bN09+v1/Dhw/X2rVrNXPmTEnSu+++q+TkZD3xxBPKy8vT7t27lZGRoYaGBmVnZ0uSGhoalJOTo9dff11paWl68sknlZ+fr9bWVnk8HklSTU2NZs+erY6ODsXFxdnqvaurS4ZhyO/3237Pycq66ZHTsl9gsGus+nGoW/jcWm4bHeoWgLA08pZXT+v+7f7+Dpt7mvr6+lRTU6MjR44oJydHe/fuVXt7u3Jzc60ap9Op8ePHa+vWrZKkxsZG9fb2BtV4PB5lZmZaNdu2bZNhGFZgkqQxY8bIMIygmszMTCswSVJeXp4CgYAaGxs/tedAIKCurq6gDQAAnJlCHppeffVVfeUrX5HT6dT111+vjRs3KiMjQ+3t7ZIkl8sVVO9yuay59vZ2RUVFKT4+/oQ1SUlJ/Y6blJQUVHPsceLj4xUVFWXVHE9lZaV1n5RhGEpOTj7JswcAAINFyENTWlqampqa1NDQoJ/+9KeaNWuWXnvtNWve4XAE1Zum2W/sWMfWHK9+IDXHWrx4sfx+v7W1traesC8AADB4hTw0RUVF6Zvf/KYuuugiVVZW6tvf/rbuvfdeud1uSeq30tPR0WGtCrndbvX09Mjn852wZv/+/f2O29nZGVRz7HF8Pp96e3v7rUB9ktPptL759/EGAADOTCEPTccyTVOBQEApKSlyu93avHmzNdfT06MtW7Zo7NixkqSsrCwNGTIkqKatrU3Nzc1WTU5Ojvx+v3bs2GHVbN++XX6/P6imublZbW1tVk1dXZ2cTqeysrJO6/kCAIDBITKUB/+3f/s3TZ06VcnJyTp06JBqamr03HPPqba2Vg6HQ6WlpaqoqFBqaqpSU1NVUVGhoUOHqrCwUJJkGIbmzJmjsrIyJSQkaNiwYSovL9fo0aM1adIkSVJ6erqmTJmioqIirVy5UpI0d+5c5efnKy0tTZKUm5urjIwMeb1eVVVV6cCBAyovL1dRURGrRwAAQFKIQ9P+/fvl9XrV1tYmwzB0/vnnq7a2VpMnT5YkLVy4UN3d3SouLpbP51N2drbq6uoUGxtr7WPFihWKjIzUjBkz1N3drYkTJ2rNmjWKiIiwatatW6eSkhLrW3YFBQWqrq625iMiIrRp0yYVFxdr3Lhxio6OVmFhoZYtW/YFfRIAACDchd1zmgYzntMEhA7PaQLOXDynCQAAYBAhNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbQhqaKisr9d3vflexsbFKSkrSFVdcoT179gTVmKapJUuWyOPxKDo6WhMmTNCuXbuCagKBgObPn6/ExETFxMSooKBA+/btC6rx+Xzyer0yDEOGYcjr9ergwYNBNS0tLZo+fbpiYmKUmJiokpIS9fT0nJZzBwAAg0tIQ9OWLVt0ww03qKGhQZs3b9aHH36o3NxcHTlyxKpZunSpli9frurqau3cuVNut1uTJ0/WoUOHrJrS0lJt3LhRNTU1qq+v1+HDh5Wfn6++vj6rprCwUE1NTaqtrVVtba2amprk9Xqt+b6+Pk2bNk1HjhxRfX29ampqtGHDBpWVlX0xHwYAAAhrDtM0zVA38bHOzk4lJSVpy5YtuvTSS2Wapjwej0pLS7Vo0SJJH60quVwu3XXXXZo3b578fr+GDx+utWvXaubMmZKkd999V8nJyXriiSeUl5en3bt3KyMjQw0NDcrOzpYkNTQ0KCcnR6+//rrS0tL05JNPKj8/X62trfJ4PJKkmpoazZ49Wx0dHYqLi/vM/ru6umQYhvx+v636gci66ZHTsl9gsGus+nGoW/jcWm4bHeoWgLA08pZXT+v+7f7+Dqt7mvx+vyRp2LBhkqS9e/eqvb1dubm5Vo3T6dT48eO1detWSVJjY6N6e3uDajwejzIzM62abdu2yTAMKzBJ0pgxY2QYRlBNZmamFZgkKS8vT4FAQI2NjcftNxAIqKurK2gDAABnprAJTaZpasGCBfre976nzMxMSVJ7e7skyeVyBdW6XC5rrr29XVFRUYqPjz9hTVJSUr9jJiUlBdUce5z4+HhFRUVZNceqrKy07pEyDEPJyckne9oAAGCQCJvQ9LOf/UyvvPKKfve73/WbczgcQa9N0+w3dqxja45XP5CaT1q8eLH8fr+1tba2nrAnAAAweIVFaJo/f77++Mc/6tlnn9U555xjjbvdbknqt9LT0dFhrQq53W719PTI5/OdsGb//v39jtvZ2RlUc+xxfD6fent7+61AfczpdCouLi5oAwAAZ6aQhibTNPWzn/1Mjz/+uJ555hmlpKQEzaekpMjtdmvz5s3WWE9Pj7Zs2aKxY8dKkrKysjRkyJCgmra2NjU3N1s1OTk58vv92rFjh1Wzfft2+f3+oJrm5ma1tbVZNXV1dXI6ncrKyjr1Jw8AAAaVyFAe/IYbbtD69ev13//934qNjbVWegzDUHR0tBwOh0pLS1VRUaHU1FSlpqaqoqJCQ4cOVWFhoVU7Z84clZWVKSEhQcOGDVN5eblGjx6tSZMmSZLS09M1ZcoUFRUVaeXKlZKkuXPnKj8/X2lpaZKk3NxcZWRkyOv1qqqqSgcOHFB5ebmKiopYQQIAAKENTQ888IAkacKECUHjq1ev1uzZsyVJCxcuVHd3t4qLi+Xz+ZSdna26ujrFxsZa9StWrFBkZKRmzJih7u5uTZw4UWvWrFFERIRVs27dOpWUlFjfsisoKFB1dbU1HxERoU2bNqm4uFjjxo1TdHS0CgsLtWzZstN09gAAYDAJq+c0DXY8pwkIHZ7TBJy5eE4TAADAIEJoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwIaWh6/vnnNX36dHk8HjkcDv3hD38ImjdNU0uWLJHH41F0dLQmTJigXbt2BdUEAgHNnz9fiYmJiomJUUFBgfbt2xdU4/P55PV6ZRiGDMOQ1+vVwYMHg2paWlo0ffp0xcTEKDExUSUlJerp6Tkdpw0AAAahkIamI0eO6Nvf/raqq6uPO7906VItX75c1dXV2rlzp9xutyZPnqxDhw5ZNaWlpdq4caNqampUX1+vw4cPKz8/X319fVZNYWGhmpqaVFtbq9raWjU1Ncnr9VrzfX19mjZtmo4cOaL6+nrV1NRow4YNKisrO30nDwAABpXIUB586tSpmjp16nHnTNPUPffco5tvvllXXnmlJOnhhx+Wy+XS+vXrNW/ePPn9fj300ENau3atJk2aJEl69NFHlZycrKefflp5eXnavXu3amtr1dDQoOzsbEnSqlWrlJOToz179igtLU11dXV67bXX1NraKo/HI0m6++67NXv2bN1xxx2Ki4s7bo+BQECBQMB63dXVdco+GwAAEF7C9p6mvXv3qr29Xbm5udaY0+nU+PHjtXXrVklSY2Ojent7g2o8Ho8yMzOtmm3btskwDCswSdKYMWNkGEZQTWZmphWYJCkvL0+BQECNjY2f2mNlZaV1yc8wDCUnJ5+akwcAAGEnbENTe3u7JMnlcgWNu1wua669vV1RUVGKj48/YU1SUlK//SclJQXVHHuc+Ph4RUVFWTXHs3jxYvn9fmtrbW09ybMEAACDRUgvz9nhcDiCXpum2W/sWMfWHK9+IDXHcjqdcjqdJ+wFAACcGcJ2pcntdktSv5Wejo4Oa1XI7Xarp6dHPp/vhDX79+/vt//Ozs6gmmOP4/P51Nvb228FCgAAfDmFbWhKSUmR2+3W5s2brbGenh5t2bJFY8eOlSRlZWVpyJAhQTVtbW1qbm62anJycuT3+7Vjxw6rZvv27fL7/UE1zc3Namtrs2rq6urkdDqVlZV1Ws8TAAAMDiG9PHf48GG9+eab1uu9e/eqqalJw4YN08iRI1VaWqqKigqlpqYqNTVVFRUVGjp0qAoLCyVJhmFozpw5KisrU0JCgoYNG6by8nKNHj3a+jZdenq6pkyZoqKiIq1cuVKSNHfuXOXn5ystLU2SlJubq4yMDHm9XlVVVenAgQMqLy9XUVHRp35zDgAAfLmENDS9+OKLuuyyy6zXCxYskCTNmjVLa9as0cKFC9Xd3a3i4mL5fD5lZ2errq5OsbGx1ntWrFihyMhIzZgxQ93d3Zo4caLWrFmjiIgIq2bdunUqKSmxvmVXUFAQ9GyoiIgIbdq0ScXFxRo3bpyio6NVWFioZcuWne6PAAAADBIO0zTNUDdxpujq6pJhGPL7/adthSrrpkdOy36Bwa6x6sehbuFza7ltdKhbAMLSyFtePa37t/v7O2zvaQIAAAgnhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6HpGPfff79SUlJ09tlnKysrSy+88EKoWwIAAGGA0PQJjz32mEpLS3XzzTfr5Zdf1iWXXKKpU6eqpaUl1K0BAIAQIzR9wvLlyzVnzhxdd911Sk9P1z333KPk5GQ98MADoW4NAACEWGSoGwgXPT09amxs1C9+8Yug8dzcXG3duvW47wkEAgoEAtZrv98vSerq6jptffYFuk/bvoHB7HT+u/uiHPpnX6hbAMLS6f73/fH+TdM8YR2h6X+999576uvrk8vlChp3uVxqb28/7nsqKyv161//ut94cnLyaekRwKcz7rs+1C0AOF0qjS/kMIcOHZJhfPqxCE3HcDgcQa9N0+w39rHFixdrwYIF1uujR4/qwIEDSkhI+NT34MzR1dWl5ORktba2Ki4uLtTtADiF+Pf95WKapg4dOiSPx3PCOkLT/0pMTFRERES/VaWOjo5+q08fczqdcjqdQWNf/epXT1eLCFNxcXH8jypwhuLf95fHiVaYPsaN4P8rKipKWVlZ2rx5c9D45s2bNXbs2BB1BQAAwgUrTZ+wYMECeb1eXXTRRcrJydGDDz6olpYWXX8990oAAPBlR2j6hJkzZ+r999/Xbbfdpra2NmVmZuqJJ57QqFGjQt0awpDT6dStt97a7xItgMGPf984Hof5Wd+vAwAAAPc0AQAA2EFoAgAAsIHQBAAAYAOhCQAAwAZCEzAA999/v1JSUnT22WcrKytLL7zwQqhbAnAKPP/885o+fbo8Ho8cDof+8Ic/hLolhBFCE3CSHnvsMZWWlurmm2/Wyy+/rEsuuURTp05VS0tLqFsD8DkdOXJE3/72t1VdXR3qVhCGeOQAcJKys7N14YUX6oEHHrDG0tPTdcUVV6iysjKEnQE4lRwOhzZu3Kgrrrgi1K0gTLDSBJyEnp4eNTY2Kjc3N2g8NzdXW7duDVFXAIAvAqEJOAnvvfee+vr6+v0RZ5fL1e+PPQMAziyEJmAAHA5H0GvTNPuNAQDOLIQm4CQkJiYqIiKi36pSR0dHv9UnAMCZhdAEnISoqChlZWVp8+bNQeObN2/W2LFjQ9QVAOCLEBnqBoDBZsGCBfJ6vbrooouUk5OjBx98UC0tLbr++utD3RqAz+nw4cN68803rdd79+5VU1OThg0bppEjR4awM4QDHjkADMD999+vpUuXqq2tTZmZmVqxYoUuvfTSULcF4HN67rnndNlll/UbnzVrltasWfPFN4SwQmgCAACwgXuaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgB8qc2ePVtXXHFFqNsAMAgQmgCElMPhOOE2e/bsULcIAJL4g70AQqytrc36+bHHHtMtt9yiPXv2WGPR0dGhaOu06e3t1ZAhQ0LdBoABYKUJQEi53W5rMwxDDocjaGz9+vX6xje+oaioKKWlpWnt2rXWe//xj3/I4XCoqanJGjt48KAcDoeee+45a2zXrl2aNm2a4uLiFBsbq0suuUT/8z//E9THsmXLNGLECCUkJOiGG25Qb2+vNXf//fcrNTVVZ599tlwul/71X//Vmjt69KjuuusuffOb35TT6dTIkSN1xx13BPX3X//1X5owYYLOPvtsPfroo5Kk1atXKz09XWeffba+9a1v6f777w/q55133tHMmTMVHx+vhIQE/eAHP9A//vEPa/7jy4on6hvAqcVKE4CwtXHjRt1444265557NGnSJP35z3/WT37yE51zzjnH/Uv0x/POO+/o0ksv1YQJE/TMM88oLi5Of/3rX/Xhhx9aNc8++6xGjBihZ599Vm+++aZmzpyp73znOyoqKtKLL76okpISrV27VmPHjtWBAwf0wgsvWO9dvHixVq1apRUrVuh73/ue2tra9Prrrwf1sGjRIt19991avXq1nE6nVq1apVtvvVXV1dW64IIL9PLLL6uoqEgxMTGaNWuWPvjgA1122WW65JJL9PzzzysyMlK33367pkyZoldeeUVRUVGf2TeA08AEgDCxevVq0zAM6/XYsWPNoqKioJof/vCH5ve//33TNE1z7969piTz5ZdftuZ9Pp8pyXz22WdN0zTNxYsXmykpKWZPT89xjzlr1ixz1KhR5ocffhh0jJkzZ5qmaZobNmww4+LizK6urn7v7erqMp1Op7lq1arj7vvj/u65556g8eTkZHP9+vVBY7/5zW/MnJwc0zRN86GHHjLT0tLMo0ePWvOBQMCMjo42n3rqKVt9Azj1uDwHIGzt3r1b48aNCxobN26cdu/ebXsfTU1NuuSSS054H9F5552niIgI6/WIESPU0dEhSZo8ebJGjRqlr3/96/J6vVq3bp0++OADq79AIKCJEyeesIeLLrrI+rmzs1Otra2aM2eOvvKVr1jb7bffbl0ybGxs1JtvvqnY2FhrftiwYfrnP/8ZdFnxRH0DOPW4PAcgrDkcjqDXpmlaY2eddZY19rFj7+mxcyP5sYHK4XDo6NGjkqTY2Fi99NJLeu6551RXV6dbbrlFS5Ys0c6dO23fpB4TE2P9/PF+V61apezs7KC6jwPQ0aNHlZWVpXXr1vXb1/Dhw231DeDUY6UJQNhKT09XfX190NjWrVuVnp4u6f8CxCe/gffJm8Il6fzzz9cLL7zwuW6QjoyM1KRJk7R06VK98sor+sc//qFnnnlGqampio6O1l/+8hfb+3K5XPp//+//6a233tI3v/nNoC0lJUWSdOGFF+qNN95QUlJSvxrDMAZ8HgA+H1aaAIStm266STNmzNCFF16oiRMn6k9/+pMef/xxPf3005I+WkUaM2aM7rzzTn3ta1/Te++9p1/+8pdB+/jZz36m++67T1dddZUWL14swzDU0NCgiy++WGlpaZ/Zw5///Ge99dZbuvTSSxUfH68nnnhCR48eVVpams4++2wtWrRICxcuVFRUlMaNG6fOzk7t2rVLc+bM+dR9LlmyRCUlJYqLi9PUqVMVCAT04osvyufzacGCBbrmmmtUVVWlH/zgB7rtttt0zjnnqKWlRY8//rhuuukmnXPOOZ/vgwUwIKw0AQhbV1xxhe69915VVVXpvPPO08qVK7V69WpNmDDBqvnP//xP9fb26qKLLtKNN96o22+/PWgfCQkJeuaZZ3T48GGNHz9eWVlZWrVqle1nJX31q1/V448/rssvv1zp6en67W9/q9/97nc677zzJEm/+tWvVFZWpltuuUXp6emaOXPmZ95XdN111+k//uM/tGbNGo0ePVrjx4/XmjVrrJWmoUOH6vnnn9fIkSN15ZVXKj09Xddee626u7sVFxd3Ep8ggFPJYX7yZgAAAAAcFytNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANjw/wFgwgbiBfo7PAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"Touchscreen\"],y=df[\"Price\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "72f0f94e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#It shows that touchscreen laptops have higher price than non touch screen laptops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "44d0bcbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"IPS\"]=df[\"ScreenResolution\"].apply(lambda x: 1 if \"IPS\" in x else 0) #Creating a new column only for those values wich have IPS in laptops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "8acf76ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>1440x900</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>Full HD 1920x1080</td>\n",
+       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
+       "      <td>Intel Core i7 2.7GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD</td>\n",
+       "      <td>AMD Radeon Pro 455</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 3.1GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 650</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "1   Apple  Ultrabook    13.3                            1440x900   \n",
+       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
+       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
+       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "\n",
+       "                          Cpu  Ram               Memory  \\\n",
+       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
+       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
+       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight        Price  Touchscreen  IPS  \n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1  \n",
+       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0  \n",
+       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0  \n",
+       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1  \n",
+       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1  "
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "b623a7be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"IPS\"].value_counts().plot(kind=\"bar\") #To see how many laptops have iPS or not "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "284afd9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='IPS', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"IPS\"],y=df[\"Price\"]) #price distribution of IPS with price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "1735fc69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#IPS displays have higher price than non IPS devices "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "e502a693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To extract the resolution into column X and Column Y \n",
+    "#for example 1280x460 ,X= 1280 and Y= 460\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "9cb9ee1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>IPS Panel Retina Display 2560</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1440</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Full HD 1920</td>\n",
+       "      <td>1080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>IPS Panel Retina Display 2880</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>IPS Panel Retina Display 2560</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>IPS Panel Full HD / Touchscreen 1920</td>\n",
+       "      <td>1080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>IPS Panel Quad HD+ / Touchscreen 3200</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>1366</td>\n",
+       "      <td>768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>1366</td>\n",
+       "      <td>768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>1366</td>\n",
+       "      <td>768</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1303 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          0     1\n",
+       "0             IPS Panel Retina Display 2560  1600\n",
+       "1                                      1440   900\n",
+       "2                              Full HD 1920  1080\n",
+       "3             IPS Panel Retina Display 2880  1800\n",
+       "4             IPS Panel Retina Display 2560  1600\n",
+       "...                                     ...   ...\n",
+       "1298   IPS Panel Full HD / Touchscreen 1920  1080\n",
+       "1299  IPS Panel Quad HD+ / Touchscreen 3200  1800\n",
+       "1300                                   1366   768\n",
+       "1301                                   1366   768\n",
+       "1302                                   1366   768\n",
+       "\n",
+       "[1303 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"ScreenResolution\"].str.split('x',n=1,expand=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "a2e83dc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new = df[\"ScreenResolution\"].str.split('x',n=1,expand=True) #made a new DataFrame from the column ScreenResolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "f4e35269",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"X_res\"] = new[0] #Storing Index oth value to the X_res and making new column in dbase\n",
+    "df[\"y_res\"]= new[1] #storing index 1st value to the Y_res and making new column in dbase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "fd8477cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>X_res</th>\n",
+       "      <th>y_res</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>IPS Panel Retina Display 2560</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>1440x900</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>Full HD 1920x1080</td>\n",
+       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Full HD 1920</td>\n",
+       "      <td>1080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
+       "      <td>Intel Core i7 2.7GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD</td>\n",
+       "      <td>AMD Radeon Pro 455</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>IPS Panel Retina Display 2880</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 3.1GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 650</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>IPS Panel Retina Display 2560</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "1   Apple  Ultrabook    13.3                            1440x900   \n",
+       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
+       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
+       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "\n",
+       "                          Cpu  Ram               Memory  \\\n",
+       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
+       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
+       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight        Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
+       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
+       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
+       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
+       "\n",
+       "                           X_res y_res  \n",
+       "0  IPS Panel Retina Display 2560  1600  \n",
+       "1                           1440   900  \n",
+       "2                   Full HD 1920  1080  \n",
+       "3  IPS Panel Retina Display 2880  1800  \n",
+       "4  IPS Panel Retina Display 2560  1600  "
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "88b43c50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#As you can seee our Y_res is perfect but there is a slight problem in X_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "6ed5b44e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       [2560]\n",
+       "1       [1440]\n",
+       "2       [1920]\n",
+       "3       [2880]\n",
+       "4       [2560]\n",
+       "         ...  \n",
+       "1298    [1920]\n",
+       "1299    [3200]\n",
+       "1300    [1366]\n",
+       "1301    [1366]\n",
+       "1302    [1366]\n",
+       "Name: X_res, Length: 1303, dtype: object"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"X_res\"].str.replace(\",\",\"\").str.findall(r'(\\d+\\.?\\d+)') \n",
+    "#in This we are finding a pattern with the help of regular expression. Pattern is where we have 3 or 4 digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "ab707b53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       2560\n",
+       "1       1440\n",
+       "2       1920\n",
+       "3       2880\n",
+       "4       2560\n",
+       "        ... \n",
+       "1298    1920\n",
+       "1299    3200\n",
+       "1300    1366\n",
+       "1301    1366\n",
+       "1302    1366\n",
+       "Name: X_res, Length: 1303, dtype: object"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"X_res\"].str.replace(\",\",\"\").str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])\n",
+    "#removed it from list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "06432c2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"X_res\"] =df[\"X_res\"].str.replace(\",\",\"\").str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0]) #removed it from list\n",
+    "#stored all this  opeartions into the X_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "2c799a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>ScreenResolution</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>X_res</th>\n",
+       "      <th>y_res</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2560</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>1440x900</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>Full HD 1920x1080</td>\n",
+       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
+       "      <td>Intel Core i7 2.7GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD</td>\n",
+       "      <td>AMD Radeon Pro 455</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2880</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
+       "      <td>Intel Core i5 3.1GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 650</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2560</td>\n",
+       "      <td>1600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
+       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "1   Apple  Ultrabook    13.3                            1440x900   \n",
+       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
+       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
+       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
+       "\n",
+       "                          Cpu  Ram               Memory  \\\n",
+       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
+       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
+       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight        Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
+       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
+       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
+       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
+       "\n",
+       "  X_res y_res  \n",
+       "0  2560  1600  \n",
+       "1  1440   900  \n",
+       "2  1920  1080  \n",
+       "3  2880  1800  \n",
+       "4  2560  1600  "
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "e6c1658a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1303 entries, 0 to 1302\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   Company           1303 non-null   object \n",
+      " 1   TypeName          1303 non-null   object \n",
+      " 2   Inches            1303 non-null   float64\n",
+      " 3   ScreenResolution  1303 non-null   object \n",
+      " 4   Cpu               1303 non-null   object \n",
+      " 5   Ram               1303 non-null   int32  \n",
+      " 6   Memory            1303 non-null   object \n",
+      " 7   Gpu               1303 non-null   object \n",
+      " 8   OpSys             1303 non-null   object \n",
+      " 9   Weight            1303 non-null   float32\n",
+      " 10  Price             1303 non-null   float64\n",
+      " 11  Touchscreen       1303 non-null   int64  \n",
+      " 12  IPS               1303 non-null   int64  \n",
+      " 13  X_res             1303 non-null   object \n",
+      " 14  y_res             1303 non-null   object \n",
+      "dtypes: float32(1), float64(2), int32(1), int64(2), object(9)\n",
+      "memory usage: 142.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() #dtype ofX_res and Y_res is in object , we have to convert it into the Int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "f2111a9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"X_res\"]= df[\"X_res\"].astype('int')\n",
+    "df[\"y_res\"]= df[\"y_res\"].astype('int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "687a56ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1303 entries, 0 to 1302\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   Company           1303 non-null   object \n",
+      " 1   TypeName          1303 non-null   object \n",
+      " 2   Inches            1303 non-null   float64\n",
+      " 3   ScreenResolution  1303 non-null   object \n",
+      " 4   Cpu               1303 non-null   object \n",
+      " 5   Ram               1303 non-null   int32  \n",
+      " 6   Memory            1303 non-null   object \n",
+      " 7   Gpu               1303 non-null   object \n",
+      " 8   OpSys             1303 non-null   object \n",
+      " 9   Weight            1303 non-null   float32\n",
+      " 10  Price             1303 non-null   float64\n",
+      " 11  Touchscreen       1303 non-null   int64  \n",
+      " 12  IPS               1303 non-null   int64  \n",
+      " 13  X_res             1303 non-null   int32  \n",
+      " 14  y_res             1303 non-null   int32  \n",
+      "dtypes: float32(1), float64(2), int32(3), int64(2), object(7)\n",
+      "memory usage: 132.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info() #converted X_res , y-res from object to the int"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53aeb03b",
+   "metadata": {},
+   "source": [
+    "# Q:Why do we have splitted the X_res and y_res?\n",
+    "# Ans: Because it has the very high correaltion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "50171724",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\4121284421.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  df.corr()[\"Price\"] #You can see reslution has the very high correlation with price\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Inches         0.068197\n",
+       "Ram            0.743007\n",
+       "Weight         0.210370\n",
+       "Price          1.000000\n",
+       "Touchscreen    0.191226\n",
+       "IPS            0.252208\n",
+       "X_res          0.556529\n",
+       "y_res          0.552809\n",
+       "Name: Price, dtype: float64"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()[\"Price\"] #You can see reslution has the very high correlation with price "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "0a9a7d5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"ppi\"]= (((df[\"X_res\"]**2) + (df[\"y_res\"]**2))**0.5/df[\"Inches\"]).astype(\"float\")\n",
+    "#created a new columns with the help of the values x_res, y_res and Inches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "49462faf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\815546952.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  df.corr()['Price']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Inches         0.068197\n",
+       "Ram            0.743007\n",
+       "Weight         0.210370\n",
+       "Price          1.000000\n",
+       "Touchscreen    0.191226\n",
+       "IPS            0.252208\n",
+       "X_res          0.556529\n",
+       "y_res          0.552809\n",
+       "ppi            0.473487\n",
+       "Name: Price, dtype: float64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "f869032b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#now we have done and extracted all the values from the ScreenResolution column and now we are dropping it \n",
+    "df.drop(columns=[\"ScreenResolution\"],inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "6f11b626",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Inches</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>X_res</th>\n",
+       "      <th>y_res</th>\n",
+       "      <th>ppi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2560</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>226.983005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>900</td>\n",
+       "      <td>127.677940</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Inches                   Cpu  Ram               Memory  \\\n",
+       "0   Apple  Ultrabook    13.3  Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1   Apple  Ultrabook    13.3  Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight       Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37  71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34  47895.5232            0    0   \n",
+       "\n",
+       "   X_res  y_res         ppi  \n",
+       "0   2560   1600  226.983005  \n",
+       "1   1440    900  127.677940  "
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)\n",
+    "#also now we do not need Inches, X_res and Y_res because we have ppi \n",
+    "#so lets drop those columns too "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "373c159d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=[\"Inches\"],inplace = True) #Dropped inches\n",
+    "df.drop(columns=[\"X_res\"],inplace = True) #Dropped X_res\n",
+    "df.drop(columns=[\"y_res\"],inplace = True) #Dropped y_res\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "aff1dca7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName                   Cpu  Ram               Memory  \\\n",
+       "0   Apple  Ultrabook  Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1   Apple  Ultrabook  Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight       Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37  71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34  47895.5232            0    0   \n",
+       "\n",
+       "          ppi  \n",
+       "0  226.983005  \n",
+       "1  127.677940  "
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4609ebcd",
+   "metadata": {},
+   "source": [
+    "# Column Cpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "61a7a715",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName                   Cpu  Ram               Memory  \\\n",
+       "0   Apple  Ultrabook  Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1   Apple  Ultrabook  Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight       Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37  71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34  47895.5232            0    0   \n",
+       "\n",
+       "          ppi  \n",
+       "0  226.983005  \n",
+       "1  127.677940  "
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "f3ce68a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intel Core i5 7200U 2.5GHz       190\n",
+       "Intel Core i7 7700HQ 2.8GHz      146\n",
+       "Intel Core i7 7500U 2.7GHz       134\n",
+       "Intel Core i7 8550U 1.8GHz        73\n",
+       "Intel Core i5 8250U 1.6GHz        72\n",
+       "                                ... \n",
+       "Intel Core M M3-6Y30 0.9GHz        1\n",
+       "AMD A9-Series 9420 2.9GHz          1\n",
+       "Intel Core i3 6006U 2.2GHz         1\n",
+       "AMD A6-Series 7310 2GHz            1\n",
+       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
+       "Name: Cpu, Length: 118, dtype: int64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Cpu\"].value_counts() #to see the values in the Cpu column "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "f54606a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                         [Intel, Core, i5, 2.3GHz]\n",
+       "1                         [Intel, Core, i5, 1.8GHz]\n",
+       "2                  [Intel, Core, i5, 7200U, 2.5GHz]\n",
+       "3                         [Intel, Core, i7, 2.7GHz]\n",
+       "4                         [Intel, Core, i5, 3.1GHz]\n",
+       "                           ...                     \n",
+       "1298               [Intel, Core, i7, 6500U, 2.5GHz]\n",
+       "1299               [Intel, Core, i7, 6500U, 2.5GHz]\n",
+       "1300    [Intel, Celeron, Dual, Core, N3050, 1.6GHz]\n",
+       "1301               [Intel, Core, i7, 6500U, 2.5GHz]\n",
+       "1302    [Intel, Celeron, Dual, Core, N3050, 1.6GHz]\n",
+       "Name: Cpu, Length: 1303, dtype: object"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Cpu\"].apply(lambda x: x.split()) #Splitted all the values of column Cpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "c1843165",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Cpu_Name\"] =df[\"Cpu\"].apply(lambda x:\" \".join( x.split()[0:3])) \n",
+    "#Made a new column for first three words of CPu as CPU_Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "d9fd412c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu_Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName                   Cpu  Ram               Memory  \\\n",
+       "0   Apple  Ultrabook  Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1   Apple  Ultrabook  Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight       Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37  71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34  47895.5232            0    0   \n",
+       "\n",
+       "          ppi       Cpu_Name  \n",
+       "0  226.983005  Intel Core i5  \n",
+       "1  127.677940  Intel Core i5  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "59d70abb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_processor(text):\n",
+    "    if text == \"Intel Core i7\" or text == \"Intel Core i5\" or text == \"Intel Core i3\":\n",
+    "        return text\n",
+    "    else: \n",
+    "        if text.split()[0] == \"Intel\":\n",
+    "            return \"Some Other Processor\"\n",
+    "        else:\n",
+    "            return \"AMD Processor\"\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "1582f1ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Cpu brand\"] = df[\"Cpu_Name\"].apply(fetch_processor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "d9f7a84c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Cpu</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu_Name</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 2.3GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 1.8GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i7 2.7GHz</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD</td>\n",
+       "      <td>AMD Radeon Pro 455</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>Intel Core i5 3.1GHz</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 650</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
+       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
+       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
+       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
+       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
+       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
+       "\n",
+       "                            Gpu  OpSys  Weight        Price  Touchscreen  IPS  \\\n",
+       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
+       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
+       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
+       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
+       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
+       "\n",
+       "          ppi       Cpu_Name      Cpu brand  \n",
+       "0  226.983005  Intel Core i5  Intel Core i5  \n",
+       "1  127.677940  Intel Core i5  Intel Core i5  \n",
+       "2  141.211998  Intel Core i5  Intel Core i5  \n",
+       "3  220.534624  Intel Core i7  Intel Core i7  \n",
+       "4  226.983005  Intel Core i5  Intel Core i5  "
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "d2995032",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Cpu brand'].value_counts().plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "2a9a3e34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"Cpu brand\"],y=df[\"Price\"])\n",
+    "plt.xticks(rotation=\"vertical\")\n",
+    "plt.show()\n",
+    "\n",
+    "# shows which processor has the highest price and which doesnt \n",
+    "#in short it shows that the Cpu processor affects the price of the commodity "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "1ff7c3e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Since we have extracted all the values from cpu and cpu  name , we cand rop them now \n",
+    "df.drop(columns=[\"Cpu\"],inplace = True) # DRopped Cpu column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "8607e220",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=[\"Cpu_Name\"],inplace = True) #droppped Cpu_name column "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "d345c344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>128GB Flash Storage</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>512GB SSD</td>\n",
+       "      <td>AMD Radeon Pro 455</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>256GB SSD</td>\n",
+       "      <td>Intel Iris Plus Graphics 650</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram               Memory                           Gpu  \\\n",
+       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
+       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
+       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
+       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
+       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
+       "\n",
+       "   OpSys  Weight        Price  Touchscreen  IPS         ppi      Cpu brand  \n",
+       "0  macOS    1.37   71378.6832            0    1  226.983005  Intel Core i5  \n",
+       "1  macOS    1.34   47895.5232            0    0  127.677940  Intel Core i5  \n",
+       "2  No OS    1.86   30636.0000            0    0  141.211998  Intel Core i5  \n",
+       "3  macOS    1.83  135195.3360            0    1  220.534624  Intel Core i7  \n",
+       "4  macOS    1.37   96095.8080            0    1  226.983005  Intel Core i5  "
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16d30ff8",
+   "metadata": {},
+   "source": [
+    "# Column Ram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "20ffbeb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Qhg8frtWrV+vQoUM6dOiQbrnlFn31q18NRMiaNWu0bt06bdq0SfX19XK73Zo+fbo6OjoCf6O0tFS7du1SdXW16urq1NnZqVmzZqmnp6d/VwYAACJWSIFy22236Stf+Yquv/56XX/99Xrsscd0zTXX6ODBgzLGaMOGDSorK9Ps2bOVnZ2t7du368yZM9q5c6ckyefzacuWLVq7dq0KCgo0btw4VVVV6ciRI9q/f/8VWSAAAIg8l3wNSk9Pj6qrq/XBBx9o0qRJamxslNfrVWFhYWCM0+nU1KlTdeDAAUlSQ0ODzp49GzTG4/EoOzs7MOZC/H6/2tvbgzYAABC9Qg6UI0eO6JprrpHT6dS9996rXbt2aezYsfJ6vZIkl8sVNN7lcgWOeb1eJSQkaPDgwRcdcyGVlZVKTU0NbBkZGaFOGwAARJCQA+Xzn/+8Dh8+rIMHD2rhwoW6++67dfz48cBxh8MRNN4Y02ff+T5pzKpVq+Tz+QJbU1NTqNMGAAARJORASUhI0OjRozV+/HhVVlbqxhtv1OOPPy632y1Jfc6EtLa2Bs6quN1udXd3q62t7aJjLsTpdAY+OXRuAwAA0euyvwfFGCO/36/MzEy53W7V1NQEjnV3d6u2tlZ5eXmSpNzcXMXHxweNaWlp0dGjRwNjAAAA4kIZ/C//8i+aOXOmMjIy1NHRoerqar344ovas2ePHA6HSktLVVFRoaysLGVlZamiokIDBw7U3LlzJUmpqakqLi7W0qVLlZaWpiFDhmjZsmXKyclRQUHBFVkgAACIPCEFyv/+7//qrrvuUktLi1JTU3XDDTdoz549mj59uiRp+fLl6urqUklJidra2jRx4kTt27dPycnJgb+xfv16xcXFac6cOerq6tK0adO0bds2xcbG9u/KAABAxHIYY0y4JxGq9vZ2paamyufzhXw9yqiVu6/QrC7sndW3XrXHiua1AQAiXyiv3/wWDwAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALBOSIFSWVmpCRMmKDk5WUOHDtXtt9+uEydOBI0xxqi8vFwej0eJiYnKz8/XsWPHgsb4/X4tXrxY6enpSkpKUlFRkZqbmy9/NQAAICqEFCi1tbVatGiRDh48qJqaGn344YcqLCzUBx98EBizZs0arVu3Tps2bVJ9fb3cbremT5+ujo6OwJjS0lLt2rVL1dXVqqurU2dnp2bNmqWenp7+WxkAAIhYcaEM3rNnT9DtrVu3aujQoWpoaNCUKVNkjNGGDRtUVlam2bNnS5K2b98ul8ulnTt3asGCBfL5fNqyZYt27NihgoICSVJVVZUyMjK0f/9+zZgxo5+WBgAAItVlXYPi8/kkSUOGDJEkNTY2yuv1qrCwMDDG6XRq6tSpOnDggCSpoaFBZ8+eDRrj8XiUnZ0dGAMAAD7bQjqD8reMMVqyZIluvvlmZWdnS5K8Xq8kyeVyBY11uVx69913A2MSEhI0ePDgPmPO3f98fr9ffr8/cLu9vf1Spw0AACLAJQfKfffdp9dee011dXV9jjkcjqDbxpg++873cWMqKyv18MMPX+pUEQVGrdx9VR/vndW3XtXHAwAEu6S3eBYvXqxnn31WL7zwgoYPHx7Y73a7JanPmZDW1tbAWRW3263u7m61tbVddMz5Vq1aJZ/PF9iampouZdoAACBChBQoxhjdd999+vWvf63f/e53yszMDDqemZkpt9utmpqawL7u7m7V1tYqLy9PkpSbm6v4+PigMS0tLTp69GhgzPmcTqdSUlKCNgAAEL1Ceotn0aJF2rlzp/7zP/9TycnJgTMlqampSkxMlMPhUGlpqSoqKpSVlaWsrCxVVFRo4MCBmjt3bmBscXGxli5dqrS0NA0ZMkTLli1TTk5O4FM9AADgsy2kQNm8ebMkKT8/P2j/1q1b9e1vf1uStHz5cnV1damkpERtbW2aOHGi9u3bp+Tk5MD49evXKy4uTnPmzFFXV5emTZumbdu2KTY29vJWAwAAokJIgWKM+cQxDodD5eXlKi8vv+iYAQMGaOPGjdq4cWMoDw8AAD4j+C0eAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWCfkQPnv//5v3XbbbfJ4PHI4HHrmmWeCjhtjVF5eLo/Ho8TEROXn5+vYsWNBY/x+vxYvXqz09HQlJSWpqKhIzc3Nl7UQAAAQPUIOlA8++EA33nijNm3adMHja9as0bp167Rp0ybV19fL7XZr+vTp6ujoCIwpLS3Vrl27VF1drbq6OnV2dmrWrFnq6em59JUAAICoERfqHWbOnKmZM2de8JgxRhs2bFBZWZlmz54tSdq+fbtcLpd27typBQsWyOfzacuWLdqxY4cKCgokSVVVVcrIyND+/fs1Y8aMy1gOAACIBv16DUpjY6O8Xq8KCwsD+5xOp6ZOnaoDBw5IkhoaGnT27NmgMR6PR9nZ2YExAADgsy3kMygfx+v1SpJcLlfQfpfLpXfffTcwJiEhQYMHD+4z5tz9z+f3++X3+wO329vb+3PaAADAMlfkUzwOhyPotjGmz77zfdyYyspKpaamBraMjIx+mysAALBPvwaK2+2WpD5nQlpbWwNnVdxut7q7u9XW1nbRMedbtWqVfD5fYGtqaurPaQMAAMv0a6BkZmbK7XarpqYmsK+7u1u1tbXKy8uTJOXm5io+Pj5oTEtLi44ePRoYcz6n06mUlJSgDQAARK+Qr0Hp7OzUm2++Gbjd2Niow4cPa8iQIRoxYoRKS0tVUVGhrKwsZWVlqaKiQgMHDtTcuXMlSampqSouLtbSpUuVlpamIUOGaNmyZcrJyQl8qgcAAHy2hRwohw4d0pe//OXA7SVLlkiS7r77bm3btk3Lly9XV1eXSkpK1NbWpokTJ2rfvn1KTk4O3Gf9+vWKi4vTnDlz1NXVpWnTpmnbtm2KjY3thyUBAIBIF3Kg5Ofnyxhz0eMOh0Pl5eUqLy+/6JgBAwZo48aN2rhxY6gPDwAAPgP4LR4AAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANaJC/cEgM+6USt3X9XHe2f1rVf18QDgUnAGBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdvqgNwBV1Nb+Iji+hA6IHZ1AAAIB1OIMCAJeIs0PAlcMZFAAAYB0CBQAAWIe3eAAAffD2FcKNMygAAMA6BAoAALAOgQIAAKxDoAAAAOuENVB+/OMfKzMzUwMGDFBubq5eeumlcE4HAABYImyB8h//8R8qLS1VWVmZXn31VX3pS1/SzJkzderUqXBNCQAAWCJsgbJu3ToVFxfru9/9rsaMGaMNGzYoIyNDmzdvDteUAACAJcLyPSjd3d1qaGjQypUrg/YXFhbqwIED4ZgSAOAzgu94iQxhCZQ///nP6unpkcvlCtrvcrnk9Xr7jPf7/fL7/YHbPp9PktTe3h7yY/f6z4R8n8txKXO8VKyt/7C2/nM118fa+g9r6x9Xe23ZD+29qo939OEZIY0/98/DGPPJg00YvPfee0aSOXDgQND+Rx991Hz+85/vM/6hhx4yktjY2NjY2NiiYGtqavrEVgjLGZT09HTFxsb2OVvS2tra56yKJK1atUpLliwJ3O7t7dX777+vtLQ0ORyOKz7f9vZ2ZWRkqKmpSSkpKVf88a4m1haZWFtkiua1SdG9PtbWP4wx6ujokMfj+cSxYQmUhIQE5ebmqqamRnfccUdgf01Njb761a/2Ge90OuV0OoP2DRo06EpPs4+UlJSo+xfzHNYWmVhbZIrmtUnRvT7WdvlSU1M/1biw/VjgkiVLdNddd2n8+PGaNGmSnnjiCZ06dUr33ntvuKYEAAAsEbZA+frXv66//OUveuSRR9TS0qLs7Gw999xzGjlyZLimBAAALBG2QJGkkpISlZSUhHMKn4rT6dRDDz3U522maMDaIhNri0zRvDYputfH2q4+hzGf5rM+AAAAVw8/FggAAKxDoAAAAOsQKAAAwDoECj7dVw4DAHAVESiQ0+nU66+/Hu5pAAAQENaPGdtq48aNOnTokG699VbNmTNHO3bsUGVlpXp7ezV79mw98sgjiouLvH90f/tzAX+rp6dHq1evVlpamiRp3bp1V3Na+ASvvvqqBg0apMzMTElSVVWVNm/erFOnTmnkyJG677779I1vfCPMs8SFdHV1qaGhQUOGDNHYsWODjv31r3/VL37xC82bNy9Ms0OorrvuOu3du1dZWVnhnkq/s3Ftkfcqe4V9//vf1w9/+EMVFhbqe9/7nhobG/XDH/5QDzzwgGJiYrR+/XrFx8fr4YcfDvdUQ7ZhwwbdeOONfX4mwBij119/XUlJSVflt42ulObmZg0YMEDp6emSpJdeekk/+clPAi/kixYt0qRJk8I8y9AVFxdr7dq1yszM1M9+9jPdf//9mj9/vu666y6dOHFC8+fP15kzZ/Sd73wn3FO9LM3NzRo0aJCuueaaoP1nz57VH/7wB02ZMiVMM7s0J0+eVGFhoU6dOiWHw6EvfelLevrppzVs2DBJH/0q+z//8z9HfKBE2/MmST/60Y8uuP/UqVPaunWr3G63JOn++++/mtPqFxG1tv74deJoct1115lf/epXxhhjDh8+bGJjY01VVVXg+K9//WszevTocE3vslRUVJjMzEzzX//1X0H74+LizLFjx8I0q/4zadIk89xzzxljjHnmmWdMTEyMKSoqMitWrDB33HGHiY+PN7/5zW/CPMvQDRw40Lz77rvGGGPGjRtnfvrTnwYd//d//3czduzYcEytX5w+fdpMmDDBxMTEmNjYWDNv3jzT0dEROO71ek1MTEwYZ3hpbr/9djNr1izzpz/9ybzxxhvmtttuM5mZmYHnMlLXdU60Pm/GGONwOMzw4cPNqFGjgjaHw2H+7u/+zowaNcpkZmaGe5qXJJLWRqCcJzExMfAfEGOMiY+PN0ePHg3cfuedd8zAgQPDMbV+8fLLL5vrr7/eLF261HR3dxtjoidQkpOTTWNjozHGmIkTJ5rVq1cHHd+4caMZN25cGGZ2edLS0syhQ4eMMcYMHTrUHD58OOj4m2++aRITE8MxtX4xb94888UvftHU19ebmpoaM378eJObm2vef/99Y8xHL3QOhyPMswzd0KFDzWuvvRa0r6SkxIwYMcK89dZbEf0Cbkz0Pm/GGHPPPfeYm266yRw/fjxofzT8tzKS1sZFsudxu906fvy4JOmNN95QT09P4LYkHTt2TEOHDg3X9C7bhAkT1NDQoD/96U8aP368jhw5EtFv6/ytmJgYtbe3S5IaGxs1c+bMoOMzZ87UiRMnwjG1yzJz5kxt3rxZkjR16lT98pe/DDr+i1/8QqNHjw7H1PrF/v379fjjj2v8+PEqKChQXV2dhg8frltuuUXvv/++JEXkv6NdXV19rlX7t3/7NxUVFWnq1Kk6efJkmGbWP6L1eZOkn/70p3rooYc0Y8YMbdq0KdzT6VcRtbZwF5JtysrKzLXXXmu++93vmszMTLNq1SozYsQIs3nzZvOTn/zEZGRkmAceeCDc0+wXTz/9tHG5XCYmJsa6cr4URUVFZuXKlcYYY2bMmGEef/zxoONPPvmkycrKCsfULst7771nRo0aZaZMmWKWLFliEhMTzc0332zmz59vpkyZYhISEszu3bvDPc1LlpSUZE6ePBm07+zZs+b22283N9xwg3nttdci8kzDhAkTzFNPPXXBY4sWLTKDBg2KyHWdE63P299qbm42t9xyi/nHf/xH09LSYuVZhksVCWsjUM7z4YcfmkcffdTMmjUr8BbB008/bTIyMkxaWpr59re/bTo7O8M8y/7T1NRknnnmmahY0/Hjx01aWpqZN2+e+f73v2+uueYa861vfcs89thjZt68ecbpdJqtW7eGe5qXpK2tzaxYscKMHTvWDBgwwCQkJJiRI0eauXPnmvr6+nBP77Lk5OSYX/7yl332n3uxGzFiRES+0FVUVJiZM2de9PjChQsj9i0QY6L3eTtfb2+vqaioMG6328TGxlr3In45bF8bPxaIqPLWW2/pX//1X7V79251dnZKkuLi4jRhwgQ9+OCDuv3228M7QfSxYsUKHT58WHv37u1z7MMPP9TXvvY1/eY3v1Fvb28YZoeL+TTP229/+1v19PSEYXb9r6GhQXV1dZo3b54GDx4c7un0K1vXRqAgKhlj1Nraqt7eXqWnpys+Pj7cU8JFfPjhhzpz5oxSUlIueLynp0fNzc0aOXLkVZ4ZPg7PG640LpJFVHI4HHK5XBo2bFggTpqamiL+u0KiUVxc3EVf5CTp9OnTEfm9Q9EuLi5O7733nrZu3ao//vGPkqQ//vGPWrhwob7zne+otrY2ouOkq6tLdXV1QR+SOOevf/2rnnrqqTDM6vK9+uqramxsDNyuqqrS5MmTlZGRoZtvvlnV1dVhnF0wAgWfGe+//762b98e7mkgRDxvdtqzZ49uuukmLVu2TOPGjdOePXs0ZcoUvfnmmzp16pRmzJih3/3ud+Ge5iU5efKkxowZoylTpignJ0f5+flqaWkJHD/3JXuRqLi4WO+8844k6Wc/+5nuuecejR8/XmVlZZowYYLmz5+vn//85+Gd5P/HN8kiajz77LMfe/ztt9++SjNBKHjeItMjjzyiBx98UI8++qiqq6s1d+5cLVy4UI899pgkqaysTKtXr9Ytt9wS5pmGbsWKFcrJydGhQ4f0f//3f1qyZIkmT56sF198USNGjAj39C7LiRMn9LnPfU6S9OMf/1gbNmzQPffcEzg+YcIEPfbYY1acbeYaFESNmJgYORyOj/11ZofDETUX7UULnrfIlJqaqoaGBo0ePVq9vb1yOp36n//5H33hC1+QJB09elQFBQXyer1hnmnoXC6X9u/fr5ycnMC+RYsW6be//a1eeOEFJSUlyePxROS/k+np6dq7d69yc3Plcrm0b98+3XjjjYHjb731lnJycnTmzJkwzvIjvMWDqDFs2DD96le/Um9v7wW3V155JdxTxAXwvEW+mJgYDRgwIOh3vpKTk+Xz+cI3qcsQzV+yF0lf/MhbPIgaubm5euWVVy76UeJP+r90hAfPW2QaNWqU3nzzzcCL2R/+8Iegtz+ampoCP4wYaf7+7/9ehw4d0pgxY4L2b9y4UcYYFRUVhWlml+8HP/iBJk+erKlTp2r8+PFau3atXnzxRY0ZM0YnTpzQwYMHtWvXrnBPUxJnUBBFHnzwQeXl5V30+OjRo/XCCy9cxRnh0+B5i0wLFy4MeosjOzs76KzD888/H5HXn0jSHXfcoaeffvqCxzZt2qRvfvObERvNHo9Hr776qiZNmqQ9e/bIGKOXX35Z+/bt0/Dhw/X73/9eX/nKV8I9TUlcgwIAACzEGRQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdf4fEhvQ935F40MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Ram\"].value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "19936ffc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"Ram\"],y=df[\"Price\"])\n",
+    "plt.xticks(rotation=\"vertical\")\n",
+    "plt.show()\n",
+    "\n",
+    "#This shows that as the Ram size is increasing the price is also increasing "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "373713f1",
+   "metadata": {},
+   "source": [
+    "# Column Memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "35f4e020",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "256GB SSD                        412\n",
+       "1TB HDD                          223\n",
+       "500GB HDD                        132\n",
+       "512GB SSD                        118\n",
+       "128GB SSD +  1TB HDD              94\n",
+       "128GB SSD                         76\n",
+       "256GB SSD +  1TB HDD              73\n",
+       "32GB Flash Storage                38\n",
+       "2TB HDD                           16\n",
+       "64GB Flash Storage                15\n",
+       "512GB SSD +  1TB HDD              14\n",
+       "1TB SSD                           14\n",
+       "256GB SSD +  2TB HDD              10\n",
+       "1.0TB Hybrid                       9\n",
+       "256GB Flash Storage                8\n",
+       "16GB Flash Storage                 7\n",
+       "32GB SSD                           6\n",
+       "180GB SSD                          5\n",
+       "128GB Flash Storage                4\n",
+       "512GB SSD +  2TB HDD               3\n",
+       "16GB SSD                           3\n",
+       "512GB Flash Storage                2\n",
+       "1TB SSD +  1TB HDD                 2\n",
+       "256GB SSD +  500GB HDD             2\n",
+       "128GB SSD +  2TB HDD               2\n",
+       "256GB SSD +  256GB SSD             2\n",
+       "512GB SSD +  256GB SSD             1\n",
+       "512GB SSD +  512GB SSD             1\n",
+       "64GB Flash Storage +  1TB HDD      1\n",
+       "1TB HDD +  1TB HDD                 1\n",
+       "32GB HDD                           1\n",
+       "64GB SSD                           1\n",
+       "128GB HDD                          1\n",
+       "240GB SSD                          1\n",
+       "8GB SSD                            1\n",
+       "508GB Hybrid                       1\n",
+       "1.0TB HDD                          1\n",
+       "512GB SSD +  1.0TB Hybrid          1\n",
+       "256GB SSD +  1.0TB Hybrid          1\n",
+       "Name: Memory, dtype: int64"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Memory\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "1a208037",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\4023190604.py:16: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  df['first'] = df['first'].str.replace(r'\\D', '')\n",
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\4023190604.py:25: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+      "  df['second'] = df['second'].str.replace(r'\\D', '')\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
+    "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
+    "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
+    "new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
+    "\n",
+    "df[\"first\"]= new[0]\n",
+    "df[\"first\"]=df[\"first\"].str.strip()\n",
+    "\n",
+    "df[\"second\"]= new[1]\n",
+    "\n",
+    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
+    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
+    "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
+    "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
+    "\n",
+    "df['first'] = df['first'].str.replace(r'\\D', '')\n",
+    "\n",
+    "df[\"second\"].fillna(\"0\", inplace = True)\n",
+    "\n",
+    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
+    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
+    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
+    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
+    "\n",
+    "df['second'] = df['second'].str.replace(r'\\D', '')\n",
+    "\n",
+    "df[\"first\"] = df[\"first\"].astype(int)\n",
+    "df[\"second\"] = df[\"second\"].astype(int)\n",
+    "\n",
+    "df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
+    "df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
+    "df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
+    "df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
+    "\n",
+    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
+    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
+    "       'Layer2Flash_Storage'],inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "998133f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Memory</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Hybrid</th>\n",
+       "      <th>Flash_Storage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>746</th>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>256 SSD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>87858.720</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>165.632118</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>8</td>\n",
+       "      <td>512 SSD</td>\n",
+       "      <td>Intel UHD Graphics 620</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>1.19</td>\n",
+       "      <td>79866.720</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>165.632118</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1130</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2000 HDD</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>2.04</td>\n",
+       "      <td>33513.120</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1096</th>\n",
+       "      <td>Dell</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1000 HDD</td>\n",
+       "      <td>AMD Radeon R7 M445</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>34035.264</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>Mediacom</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32 Flash Storage</td>\n",
+       "      <td>Intel HD Graphics</td>\n",
+       "      <td>Windows 10</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>13586.400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>165.632118</td>\n",
+       "      <td>Some Other Processor</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Company            TypeName  Ram            Memory  \\\n",
+       "746    Samsung           Ultrabook   16           256 SSD   \n",
+       "684     Lenovo  2 in 1 Convertible    8           512 SSD   \n",
+       "1130        HP            Notebook    8          2000 HDD   \n",
+       "1096      Dell            Notebook    8          1000 HDD   \n",
+       "556   Mediacom            Notebook    4  32 Flash Storage   \n",
+       "\n",
+       "                         Gpu       OpSys  Weight      Price  Touchscreen  IPS  \\\n",
+       "746    Intel HD Graphics 620  Windows 10    0.81  87858.720            0    0   \n",
+       "684   Intel UHD Graphics 620  Windows 10    1.19  79866.720            1    1   \n",
+       "1130   Intel HD Graphics 620  Windows 10    2.04  33513.120            0    0   \n",
+       "1096      AMD Radeon R7 M445  Windows 10    2.36  34035.264            0    0   \n",
+       "556        Intel HD Graphics  Windows 10    1.35  13586.400            0    1   \n",
+       "\n",
+       "             ppi             Cpu brand   HDD  SSD  Hybrid  Flash_Storage  \n",
+       "746   165.632118         Intel Core i7     0  256       0              0  \n",
+       "684   165.632118         Intel Core i7     0  512       0              0  \n",
+       "1130  100.454670         Intel Core i7  2000    0       0              0  \n",
+       "1096  141.211998         Intel Core i5  1000    0       0              0  \n",
+       "556   165.632118  Some Other Processor     0    0       0             32  "
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "a0d78722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#since i have extracted all the values from column memroy. now i can drop it \n",
+    "df.drop(columns=[\"Memory\"],inplace= True) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "58f9fe2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Hybrid</th>\n",
+       "      <th>Flash_Storage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
+       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
+       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
+       "\n",
+       "        Price  Touchscreen  IPS         ppi      Cpu brand  HDD  SSD  Hybrid  \\\n",
+       "0  71378.6832            0    1  226.983005  Intel Core i5    0  128       0   \n",
+       "1  47895.5232            0    0  127.677940  Intel Core i5    0    0       0   \n",
+       "\n",
+       "   Flash_Storage  \n",
+       "0              0  \n",
+       "1            128  "
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "10c49486",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\1734695575.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  df.corr()[\"Price\"]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Ram              0.743007\n",
+       "Weight           0.210370\n",
+       "Price            1.000000\n",
+       "Touchscreen      0.191226\n",
+       "IPS              0.252208\n",
+       "ppi              0.473487\n",
+       "HDD             -0.096441\n",
+       "SSD              0.670799\n",
+       "Hybrid           0.007989\n",
+       "Flash_Storage   -0.040511\n",
+       "Name: Price, dtype: float64"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()[\"Price\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "e6cfc1d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=[\"Hybrid\"],inplace= True) #dropped column hybrid  \n",
+    "df.drop(columns=[\"Flash_Storage\"],inplace= True)  #dropped column Flash_Storage\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "ab67cfe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Intel Iris Plus Graphics 640</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Intel HD Graphics 6000</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Intel HD Graphics 620</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>AMD Radeon Pro 455</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Intel Iris Plus Graphics 650</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
+       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
+       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
+       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
+       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
+       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
+       "\n",
+       "         Price  Touchscreen  IPS         ppi      Cpu brand  HDD  SSD  \n",
+       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128  \n",
+       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0  \n",
+       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256  \n",
+       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512  \n",
+       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256  "
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5451c5a4",
+   "metadata": {},
+   "source": [
+    "# Column GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "3d00793b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intel HD Graphics 620      281\n",
+       "Intel HD Graphics 520      185\n",
+       "Intel UHD Graphics 620      68\n",
+       "Nvidia GeForce GTX 1050     66\n",
+       "Nvidia GeForce GTX 1060     48\n",
+       "                          ... \n",
+       "AMD Radeon R5 520            1\n",
+       "AMD Radeon R7                1\n",
+       "Intel HD Graphics 540        1\n",
+       "AMD Radeon 540               1\n",
+       "ARM Mali T860 MP4            1\n",
+       "Name: Gpu, Length: 110, dtype: int64"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Gpu\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "834d01dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       Intel\n",
+       "1       Intel\n",
+       "2       Intel\n",
+       "3         AMD\n",
+       "4       Intel\n",
+       "        ...  \n",
+       "1298    Intel\n",
+       "1299    Intel\n",
+       "1300    Intel\n",
+       "1301      AMD\n",
+       "1302    Intel\n",
+       "Name: Gpu, Length: 1303, dtype: object"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Gpu\"].apply(lambda x:x.split()[0]) #fetched the 0th index value from the graphics card that is company name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "51a6d4eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Gpu brand\"] = df[\"Gpu\"].apply(lambda x:x.split()[0]) #made a new column of name Gpu Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "8adfa3b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intel     722\n",
+       "Nvidia    400\n",
+       "AMD       180\n",
+       "ARM         1\n",
+       "Name: Gpu brand, dtype: int64"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Gpu brand\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "99512b35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Gpu</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1191</th>\n",
+       "      <td>Samsung</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>4</td>\n",
+       "      <td>ARM Mali T860 MP4</td>\n",
+       "      <td>Chrome OS</td>\n",
+       "      <td>1.15</td>\n",
+       "      <td>35111.52</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>234.5074</td>\n",
+       "      <td>AMD Processor</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ARM</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company            TypeName  Ram                Gpu      OpSys  Weight  \\\n",
+       "1191  Samsung  2 in 1 Convertible    4  ARM Mali T860 MP4  Chrome OS    1.15   \n",
+       "\n",
+       "         Price  Touchscreen  IPS       ppi      Cpu brand  HDD  SSD Gpu brand  \n",
+       "1191  35111.52            1    1  234.5074  AMD Processor    0    0       ARM  "
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Gpu brand\"]== \"ARM\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "5b083a8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=df[df[\"Gpu brand\"]!= \"ARM\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "56e031b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intel     722\n",
+       "Nvidia    400\n",
+       "AMD       180\n",
+       "Name: Gpu brand, dtype: int64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Gpu brand\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "0dde0c7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Analysis of GPU with the price\n",
+    "sns.barplot(x=df[\"Gpu brand\"],y=df[\"Price\"])\n",
+    "plt.xticks(rotation=\"vertical\")\n",
+    "plt.show()\n",
+    "\n",
+    "#it shows that GPU brands affects the price "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "19c2176e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Since we have extracted the useful values from GPU lets drop the GPU colun from the database \n",
+    "df.drop(columns=[\"Gpu\"],inplace= True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "0a65a640",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>AMD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram  OpSys  Weight        Price  Touchscreen  IPS  \\\n",
+       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
+       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
+       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
+       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
+       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \n",
+       "\n",
+       "          ppi      Cpu brand  HDD  SSD Gpu brand  \n",
+       "0  226.983005  Intel Core i5    0  128     Intel  \n",
+       "1  127.677940  Intel Core i5    0    0     Intel  \n",
+       "2  141.211998  Intel Core i5    0  256     Intel  \n",
+       "3  220.534624  Intel Core i7    0  512       AMD  \n",
+       "4  226.983005  Intel Core i5    0  256     Intel  "
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3785c0f",
+   "metadata": {},
+   "source": [
+    "# OpSys (Operating System)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "1c3aa67b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Windows 10      1072\n",
+       "No OS             66\n",
+       "Linux             62\n",
+       "Windows 7         45\n",
+       "Chrome OS         26\n",
+       "macOS             13\n",
+       "Mac OS X           8\n",
+       "Windows 10 S       8\n",
+       "Android            2\n",
+       "Name: OpSys, dtype: int64"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"OpSys\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "6545af86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#To reduce we can reduce the categories  , \n",
+    "# for example we can store Windows !0, windwos 7 in a single windows category "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "5e602e61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"OpSys\"], y = df[\"Price\"])\n",
+    "plt.xticks(rotation=\"vertical\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "fe6c0c4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Windows 10      1072\n",
+       "No OS             66\n",
+       "Linux             62\n",
+       "Windows 7         45\n",
+       "Chrome OS         26\n",
+       "macOS             13\n",
+       "Mac OS X           8\n",
+       "Windows 10 S       8\n",
+       "Android            2\n",
+       "Name: OpSys, dtype: int64"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"OpSys\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "56eb8be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cat_os(inp):\n",
+    "    if inp== \"Windows 10\" or inp == \"Windows 7\" or inp == \"Windows 10 S\":\n",
+    "        return \"Windows\"\n",
+    "    elif inp == \"macOS\" or inp == \"Mac OS X\":\n",
+    "        return \"Mac\"\n",
+    "    else:\n",
+    "        return \"Other/No OS/ Linux\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "b15eaf3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Os\"] = df[\"OpSys\"].apply(cat_os) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "dff3d73d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>OpSys</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "      <th>Os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>No OS</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Other/No OS/ Linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>macOS</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram  OpSys  Weight        Price  Touchscreen  IPS  \\\n",
+       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
+       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
+       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
+       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
+       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \n",
+       "\n",
+       "          ppi      Cpu brand  HDD  SSD Gpu brand                  Os  \n",
+       "0  226.983005  Intel Core i5    0  128     Intel                 Mac  \n",
+       "1  127.677940  Intel Core i5    0    0     Intel                 Mac  \n",
+       "2  141.211998  Intel Core i5    0  256     Intel  Other/No OS/ Linux  \n",
+       "3  220.534624  Intel Core i7    0  512       AMD                 Mac  \n",
+       "4  226.983005  Intel Core i5    0  256     Intel                 Mac  "
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "169c8747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Since we have extracted the useful values from Operating SYstem lets drop the OpSys colun from the database \n",
+    "df.drop(columns=[\"OpSys\"],inplace= True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "8354b22b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=df[\"Os\"], y = df[\"Price\"])\n",
+    "plt.xticks(rotation=\"vertical\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e5dc91e",
+   "metadata": {},
+   "source": [
+    "# Column Weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "148c9c50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Weight', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df[\"Weight\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "185cddb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Weight', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x=df[\"Weight\"], y=df[\"Price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "3ae9db94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#minor corealton , scatter plot shows that as the weight increases the price also increase "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "dd54d508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\1734695575.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  df.corr()[\"Price\"]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Ram            0.742905\n",
+       "Weight         0.209867\n",
+       "Price          1.000000\n",
+       "Touchscreen    0.192917\n",
+       "IPS            0.253320\n",
+       "ppi            0.475368\n",
+       "HDD           -0.096891\n",
+       "SSD            0.670660\n",
+       "Name: Price, dtype: float64"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()[\"Price\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "0aa9df91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\1134722465.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  df.corr()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Ram</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.383362</td>\n",
+       "      <td>0.742905</td>\n",
+       "      <td>0.118875</td>\n",
+       "      <td>0.207949</td>\n",
+       "      <td>0.305688</td>\n",
+       "      <td>0.095808</td>\n",
+       "      <td>0.603379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Weight</th>\n",
+       "      <td>0.383362</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.209867</td>\n",
+       "      <td>-0.293004</td>\n",
+       "      <td>0.018643</td>\n",
+       "      <td>-0.321883</td>\n",
+       "      <td>0.514147</td>\n",
+       "      <td>-0.063818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Price</th>\n",
+       "      <td>0.742905</td>\n",
+       "      <td>0.209867</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.192917</td>\n",
+       "      <td>0.253320</td>\n",
+       "      <td>0.475368</td>\n",
+       "      <td>-0.096891</td>\n",
+       "      <td>0.670660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <td>0.118875</td>\n",
+       "      <td>-0.293004</td>\n",
+       "      <td>0.192917</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.148026</td>\n",
+       "      <td>0.458571</td>\n",
+       "      <td>-0.208766</td>\n",
+       "      <td>0.257577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IPS</th>\n",
+       "      <td>0.207949</td>\n",
+       "      <td>0.018643</td>\n",
+       "      <td>0.253320</td>\n",
+       "      <td>0.148026</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.299142</td>\n",
+       "      <td>-0.093588</td>\n",
+       "      <td>0.225311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ppi</th>\n",
+       "      <td>0.305688</td>\n",
+       "      <td>-0.321883</td>\n",
+       "      <td>0.475368</td>\n",
+       "      <td>0.458571</td>\n",
+       "      <td>0.299142</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.294698</td>\n",
+       "      <td>0.509437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HDD</th>\n",
+       "      <td>0.095808</td>\n",
+       "      <td>0.514147</td>\n",
+       "      <td>-0.096891</td>\n",
+       "      <td>-0.208766</td>\n",
+       "      <td>-0.093588</td>\n",
+       "      <td>-0.294698</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.400750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SSD</th>\n",
+       "      <td>0.603379</td>\n",
+       "      <td>-0.063818</td>\n",
+       "      <td>0.670660</td>\n",
+       "      <td>0.257577</td>\n",
+       "      <td>0.225311</td>\n",
+       "      <td>0.509437</td>\n",
+       "      <td>-0.400750</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Ram    Weight     Price  Touchscreen       IPS       ppi  \\\n",
+       "Ram          1.000000  0.383362  0.742905     0.118875  0.207949  0.305688   \n",
+       "Weight       0.383362  1.000000  0.209867    -0.293004  0.018643 -0.321883   \n",
+       "Price        0.742905  0.209867  1.000000     0.192917  0.253320  0.475368   \n",
+       "Touchscreen  0.118875 -0.293004  0.192917     1.000000  0.148026  0.458571   \n",
+       "IPS          0.207949  0.018643  0.253320     0.148026  1.000000  0.299142   \n",
+       "ppi          0.305688 -0.321883  0.475368     0.458571  0.299142  1.000000   \n",
+       "HDD          0.095808  0.514147 -0.096891    -0.208766 -0.093588 -0.294698   \n",
+       "SSD          0.603379 -0.063818  0.670660     0.257577  0.225311  0.509437   \n",
+       "\n",
+       "                  HDD       SSD  \n",
+       "Ram          0.095808  0.603379  \n",
+       "Weight       0.514147 -0.063818  \n",
+       "Price       -0.096891  0.670660  \n",
+       "Touchscreen -0.208766  0.257577  \n",
+       "IPS         -0.093588  0.225311  \n",
+       "ppi         -0.294698  0.509437  \n",
+       "HDD          1.000000 -0.400750  \n",
+       "SSD         -0.400750  1.000000  "
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "8459bd0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\58359773.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  sns.heatmap(df.corr())\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.corr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "94087042",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\3798988075.py:2: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(df[\"Price\"])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Our target column that is price is skewed\n",
+    "sns.distplot(df[\"Price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "ca7bc042",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_9696\\1611883942.py:1: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(np.log(df[\"Price\"]))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Price', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(np.log(df[\"Price\"]))\n",
+    "#converted it from skewed to normal with the help of log transfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "d406d197",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df.drop(columns=[\"Price\"])\n",
+    "y = np.log(df[\"Price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "f7682e34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "      <th>Os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Other/No OS/ Linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>157.350512</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>276.053530</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111.935204</td>\n",
+       "      <td>Some Other Processor</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>Asus</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>Some Other Processor</td>\n",
+       "      <td>500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1302 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Company            TypeName  Ram  Weight  Touchscreen  IPS         ppi  \\\n",
+       "0      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
+       "1      Apple           Ultrabook    8    1.34            0    0  127.677940   \n",
+       "2         HP            Notebook    8    1.86            0    0  141.211998   \n",
+       "3      Apple           Ultrabook   16    1.83            0    1  220.534624   \n",
+       "4      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
+       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
+       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
+       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
+       "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
+       "1301      HP            Notebook    6    2.19            0    0  100.454670   \n",
+       "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
+       "\n",
+       "                 Cpu brand   HDD  SSD Gpu brand                  Os  \n",
+       "0            Intel Core i5     0  128     Intel                 Mac  \n",
+       "1            Intel Core i5     0    0     Intel                 Mac  \n",
+       "2            Intel Core i5     0  256     Intel  Other/No OS/ Linux  \n",
+       "3            Intel Core i7     0  512       AMD                 Mac  \n",
+       "4            Intel Core i5     0  256     Intel                 Mac  \n",
+       "...                    ...   ...  ...       ...                 ...  \n",
+       "1298         Intel Core i7     0  128     Intel             Windows  \n",
+       "1299         Intel Core i7     0  512     Intel             Windows  \n",
+       "1300  Some Other Processor     0    0     Intel             Windows  \n",
+       "1301         Intel Core i7  1000    0       AMD             Windows  \n",
+       "1302  Some Other Processor   500    0     Intel             Windows  \n",
+       "\n",
+       "[1302 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "9fb85ec0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       11.175755\n",
+       "1       10.776777\n",
+       "2       10.329931\n",
+       "3       11.814476\n",
+       "4       11.473101\n",
+       "          ...    \n",
+       "1298    10.433899\n",
+       "1299    11.288115\n",
+       "1300     9.409283\n",
+       "1301    10.614129\n",
+       "1302     9.886358\n",
+       "Name: Price, Length: 1302, dtype: float64"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "d892dfba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "0506e95e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.15, random_state=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "6f1644a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "      <th>Os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>Toshiba</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1141</th>\n",
+       "      <td>MSI</td>\n",
+       "      <td>Gaming</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Nvidia</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1049</th>\n",
+       "      <td>Asus</td>\n",
+       "      <td>Netbook</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>135.094211</td>\n",
+       "      <td>Some Other Processor</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Other/No OS/ Linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1020</th>\n",
+       "      <td>Dell</td>\n",
+       "      <td>2 in 1 Convertible</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.08</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i3</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>878</th>\n",
+       "      <td>Dell</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Nvidia</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>466</th>\n",
+       "      <td>Acer</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>Intel Core i3</td>\n",
+       "      <td>500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Nvidia</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>Asus</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.63</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>Nvidia</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>Acer</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>AMD Processor</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>Windows</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>Lenovo</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.454670</td>\n",
+       "      <td>Intel Core i3</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Nvidia</td>\n",
+       "      <td>Other/No OS/ Linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1193</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.415547</td>\n",
+       "      <td>Some Other Processor</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1106 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Company            TypeName  Ram  Weight  Touchscreen  IPS         ppi  \\\n",
+       "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
+       "1141      MSI              Gaming    8    2.40            0    0  141.211998   \n",
+       "1049     Asus             Netbook    4    1.20            0    0  135.094211   \n",
+       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  141.211998   \n",
+       "878      Dell            Notebook    4    2.18            0    0  141.211998   \n",
+       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
+       "466      Acer            Notebook    4    2.20            0    0  100.454670   \n",
+       "299      Asus           Ultrabook   16    1.63            0    0  141.211998   \n",
+       "493      Acer            Notebook    8    2.20            0    0  100.454670   \n",
+       "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
+       "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
+       "\n",
+       "                 Cpu brand   HDD  SSD Gpu brand                  Os  \n",
+       "183          Intel Core i5     0  128     Intel             Windows  \n",
+       "1141         Intel Core i7  1000  128    Nvidia             Windows  \n",
+       "1049  Some Other Processor     0    0     Intel  Other/No OS/ Linux  \n",
+       "1020         Intel Core i3  1000    0     Intel             Windows  \n",
+       "878          Intel Core i5  1000  128    Nvidia             Windows  \n",
+       "...                    ...   ...  ...       ...                 ...  \n",
+       "466          Intel Core i3   500    0    Nvidia             Windows  \n",
+       "299          Intel Core i7     0  512    Nvidia             Windows  \n",
+       "493          AMD Processor  1000    0       AMD             Windows  \n",
+       "527          Intel Core i3  2000    0    Nvidia  Other/No OS/ Linux  \n",
+       "1193  Some Other Processor     0    0     Intel                 Mac  \n",
+       "\n",
+       "[1106 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38eebad9",
+   "metadata": {},
+   "source": [
+    "# Importing all the best algorithms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "5014a7a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.metrics import r2_score,mean_absolute_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "id": "f2151e11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
+    "from sklearn.svm import SVR\n",
+    "# from xgboost import XGBRegressor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af5b75a6",
+   "metadata": {},
+   "source": [
+    "# Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "id": "fee51532",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.8073277448418734\n",
+      "MAE 0.21017827976428724\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = LinearRegression()\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score',r2_score(y_test,y_pred))\n",
+    "print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "ce4ec864",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2336780599567432"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(0.21)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b60e96",
+   "metadata": {},
+   "source": [
+    "# RidgeRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "0005b554",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score is:  0.8127331031311809\n",
+      "Mean Absolute Error is:  0.20926802242582965\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = Ridge(alpha=10)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score is: ',r2_score(y_test,y_pred))\n",
+    "print('Mean Absolute Error is: ',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d15b3c6",
+   "metadata": {},
+   "source": [
+    "# LassoRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "id": "9ec47334",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score is:  0.8071853945317105\n",
+      "Mean Absolute Error is:  0.21114361613472565\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = Lasso(alpha=0.001)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score is: ',r2_score(y_test,y_pred))\n",
+    "print('Mean Absolute Error is: ',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c11fc091",
+   "metadata": {},
+   "source": [
+    "#  KNN (k nearest neighbor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "046facef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score is:  0.8027675291937718\n",
+      "Mean Absolute Error is:  0.193456293739003\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = KNeighborsRegressor(n_neighbors=3)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score is: ',r2_score(y_test,y_pred))\n",
+    "print('Mean Absolute Error is: ',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e61be1",
+   "metadata": {},
+   "source": [
+    "#  Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "id": "412c8454",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score is:  0.8366862340483114\n",
+      "Mean Absolute Error is:  0.18450460098777566\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = DecisionTreeRegressor(max_depth=8)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score is: ',r2_score(y_test,y_pred))\n",
+    "print('Mean Absolute Error is: ',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2519358",
+   "metadata": {},
+   "source": [
+    "# SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "id": "30f3ce7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score is:  0.8083180902257614\n",
+      "Mean Absolute Error is:  0.20239059427481307\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = SVR(kernel='rbf',C=10000,epsilon=0.1)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score is: ',r2_score(y_test,y_pred))\n",
+    "print('Mean Absolute Error is: ',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34c30330",
+   "metadata": {},
+   "source": [
+    "# Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "id": "cc57a7ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score is:  0.8873402378382488\n",
+      "Mean Absolute Error is:  0.15860130110457718\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = RandomForestRegressor(n_estimators=100,\n",
+    "                              random_state=3,\n",
+    "                              max_samples=0.5,\n",
+    "                              max_features=0.75,\n",
+    "                              max_depth=15)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score is: ',r2_score(y_test,y_pred))\n",
+    "print('Mean Absolute Error is: ',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f01ad3f",
+   "metadata": {},
+   "source": [
+    "# Extra Trees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "id": "c59ae054",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.8753793123440623\n",
+      "MAE 0.15979519126758127\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = ExtraTreesRegressor(n_estimators=100,\n",
+    "                              random_state=3,\n",
+    "                              max_samples=None,\n",
+    "                              max_features=0.75,\n",
+    "                              max_depth=15)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score',r2_score(y_test,y_pred))\n",
+    "print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90abb77f",
+   "metadata": {},
+   "source": [
+    "# AdaBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "id": "2080eabb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.793331570243383\n",
+      "MAE 0.22772195087368882\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = AdaBoostRegressor(n_estimators=15,learning_rate=1.0)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score',r2_score(y_test,y_pred))\n",
+    "print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc54d464",
+   "metadata": {},
+   "source": [
+    "# GradientBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "687b431b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.8832259431228083\n",
+      "MAE 0.15899186930352127\n"
+     ]
+    }
+   ],
+   "source": [
+    "step1 = ColumnTransformer(transformers=[\n",
+    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "],remainder='passthrough')\n",
+    "\n",
+    "step2 = GradientBoostingRegressor(n_estimators=500)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "    ('step1',step1),\n",
+    "    ('step2',step2)\n",
+    "])\n",
+    "pipe.fit(x_train,y_train)\n",
+    "\n",
+    "y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "print('R2 score',r2_score(y_test,y_pred))\n",
+    "print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70ff336e",
+   "metadata": {},
+   "source": [
+    "# XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "37851136",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.8811773435850243\n",
+      "MAE 0.16496203512600974\n"
+     ]
+    }
+   ],
+   "source": [
+    "# step1 = ColumnTransformer(transformers=[\n",
+    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "# ],remainder='passthrough')\n",
+    "\n",
+    "# step2 = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
+    "\n",
+    "# pipe = Pipeline([\n",
+    "#     ('step1',step1),\n",
+    "#     ('step2',step2)\n",
+    "# ])\n",
+    "\n",
+    "# pipe.fit(x_train,y_train)\n",
+    "\n",
+    "# y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "# print('R2 score',r2_score(y_test,y_pred))\n",
+    "# print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b26eb63e",
+   "metadata": {},
+   "source": [
+    "# Voting Regressor\n",
+    "### combining the best performing models/algorithms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "51ded058",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.8904828063373843\n",
+      "MAE 0.15805080886686704\n"
+     ]
+    }
+   ],
+   "source": [
+    "# from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
+    "\n",
+    "# step1 = ColumnTransformer(transformers=[\n",
+    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "# ],remainder='passthrough')\n",
+    "\n",
+    "\n",
+    "# rf = RandomForestRegressor(n_estimators=350,random_state=3,max_samples=0.5,max_features=0.75,max_depth=15)\n",
+    "# gbdt = GradientBoostingRegressor(n_estimators=100,max_features=0.5)\n",
+    "# xgb = XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5)\n",
+    "# et = ExtraTreesRegressor(n_estimators=100,random_state=3,max_samples=None,max_features=0.75,max_depth=10)\n",
+    "\n",
+    "# step2 = VotingRegressor([('rf', rf), ('gbdt', gbdt), ('xgb',xgb), ('et',et)],weights=[5,1,1,1])\n",
+    "\n",
+    "# pipe = Pipeline([\n",
+    "#     ('step1',step1),\n",
+    "#     ('step2',step2)\n",
+    "# ])\n",
+    "\n",
+    "# pipe.fit(x_train,y_train)\n",
+    "\n",
+    "# y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "# print('R2 score',r2_score(y_test,y_pred))\n",
+    "# print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "380e5e94",
+   "metadata": {},
+   "source": [
+    "# Stacking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "id": "4f7a1692",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score 0.879816517413725\n",
+      "MAE 0.16716128617209366\n"
+     ]
+    }
+   ],
+   "source": [
+    "# from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
+    "\n",
+    "# step1 = ColumnTransformer(transformers=[\n",
+    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
+    "# ],remainder='passthrough')\n",
+    "\n",
+    "\n",
+    "# estimators = [\n",
+    "#     ('rf', RandomForestRegressor(n_estimators=350,random_state=3,max_samples=0.5,max_features=0.75,max_depth=15)),\n",
+    "#     ('gbdt',GradientBoostingRegressor(n_estimators=100,max_features=0.5)),\n",
+    "#     ('xgb', XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5))\n",
+    "# ]\n",
+    "\n",
+    "# step2 = StackingRegressor(estimators=estimators, final_estimator=Ridge(alpha=100))\n",
+    "\n",
+    "# pipe = Pipeline([\n",
+    "#     ('step1',step1),\n",
+    "#     ('step2',step2)\n",
+    "# ])\n",
+    "\n",
+    "# pipe.fit(x_train,y_train)\n",
+    "\n",
+    "# y_pred = pipe.predict(x_test)\n",
+    "\n",
+    "# print('R2 score',r2_score(y_test,y_pred))\n",
+    "# print('MAE',mean_absolute_error(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e3a1281",
+   "metadata": {},
+   "source": [
+    "# Exporting The Model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "id": "181ed00f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "      <th>Os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.34</td>\n",
+       "      <td>47895.5232</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>127.677940</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HP</td>\n",
+       "      <td>Notebook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.86</td>\n",
+       "      <td>30636.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>141.211998</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Other/No OS/ Linux</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1.83</td>\n",
+       "      <td>135195.3360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>220.534624</td>\n",
+       "      <td>Intel Core i7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>512</td>\n",
+       "      <td>AMD</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>96095.8080</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>256</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram  Weight        Price  Touchscreen  IPS         ppi  \\\n",
+       "0   Apple  Ultrabook    8    1.37   71378.6832            0    1  226.983005   \n",
+       "1   Apple  Ultrabook    8    1.34   47895.5232            0    0  127.677940   \n",
+       "2      HP   Notebook    8    1.86   30636.0000            0    0  141.211998   \n",
+       "3   Apple  Ultrabook   16    1.83  135195.3360            0    1  220.534624   \n",
+       "4   Apple  Ultrabook    8    1.37   96095.8080            0    1  226.983005   \n",
+       "\n",
+       "       Cpu brand  HDD  SSD Gpu brand                  Os  \n",
+       "0  Intel Core i5    0  128     Intel                 Mac  \n",
+       "1  Intel Core i5    0    0     Intel                 Mac  \n",
+       "2  Intel Core i5    0  256     Intel  Other/No OS/ Linux  \n",
+       "3  Intel Core i7    0  512       AMD                 Mac  \n",
+       "4  Intel Core i5    0  256     Intel                 Mac  "
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "id": "b4e513ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "id": "12b81e6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(df,open('df.pkl','wb')) #imported the data \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "id": "33721406",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(pipe,open('pipe.pkl','wb')) #imported the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "id": "4106f7b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company</th>\n",
+       "      <th>TypeName</th>\n",
+       "      <th>Ram</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Touchscreen</th>\n",
+       "      <th>IPS</th>\n",
+       "      <th>ppi</th>\n",
+       "      <th>Cpu brand</th>\n",
+       "      <th>HDD</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>Gpu brand</th>\n",
+       "      <th>Os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Ultrabook</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1.37</td>\n",
+       "      <td>71378.6832</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>226.983005</td>\n",
+       "      <td>Intel Core i5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128</td>\n",
+       "      <td>Intel</td>\n",
+       "      <td>Mac</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company   TypeName  Ram  Weight       Price  Touchscreen  IPS         ppi  \\\n",
+       "0   Apple  Ultrabook    8    1.37  71378.6832            0    1  226.983005   \n",
+       "\n",
+       "       Cpu brand  HDD  SSD Gpu brand   Os  \n",
+       "0  Intel Core i5    0  128     Intel  Mac  "
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "id": "1ce41b01",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The syntax of the command is incorrect.\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "id": "8ba05bbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: pandas\n",
+      "Version: 1.5.3\n",
+      "Summary: Powerful data structures for data analysis, time series, and statistics\n",
+      "Home-page: https://pandas.pydata.org\n",
+      "Author: The Pandas Development Team\n",
+      "Author-email: pandas-dev@python.org\n",
+      "License: BSD-3-Clause\n",
+      "Location: c:\\users\\user\\anaconda3\\lib\\site-packages\n",
+      "Requires: numpy, python-dateutil, pytz\n",
+      "Required-by: datashader, holoviews, hvplot, seaborn, statsmodels, xarray\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip show pandas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ac57a18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}