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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b> Malayka Iftikhar _ 015 <br> \n",
+    "Insia Sharafat _010 <br>\n",
+    "Laiba Butt _ 014<br>\n",
+    "Data Science Semester Project </b>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Happiness and Corruption dataset Analysis, EDA, Modeling \n",
+    "This dataset is collected to find a relationship between freedom, happiness score, generosity and the CPI score.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Necessary Libraries "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import plotly.express as px\n",
+    "from sklearn.preprocessing import LabelEncoder, RobustScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from plotly import graph_objects as go\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Activation\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### About Dataset<br>\n",
+    "gdp_per_capita: The  GDP per capita, is a measure of a country's economic output that accounts for its number of people. <br>\n",
+    "dystopia_residual: The Dystopia Residual metric actually is the Dystopia Happiness Score, the Residual value for each country.<br>\n",
+    "cpi_score: The Corruption Perceptions Index (CPI) is an index which ranks countries by their perceived levels of public sector corruption.\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Norway</td>\n",
+       "      <td>7.537</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>7.522</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>7.504</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>7.494</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Finland</td>\n",
+       "      <td>7.469</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Country  happiness_score  gdp_per_capita    family    health   freedom  \\\n",
+       "0       Norway            7.537        1.616463  1.533524  0.796667  0.635423   \n",
+       "1      Denmark            7.522        1.482383  1.551122  0.792566  0.626007   \n",
+       "2      Iceland            7.504        1.480633  1.610574  0.833552  0.627163   \n",
+       "3  Switzerland            7.494        1.564980  1.516912  0.858131  0.620071   \n",
+       "4      Finland            7.469        1.443572  1.540247  0.809158  0.617951   \n",
+       "\n",
+       "   generosity  government_trust  dystopia_residual continent  Year  \\\n",
+       "0    0.362012          0.315964           2.277027    Europe  2015   \n",
+       "1    0.355280          0.400770           2.313707    Europe  2015   \n",
+       "2    0.475540          0.153527           2.322715    Europe  2015   \n",
+       "3    0.290549          0.367007           2.276716    Europe  2015   \n",
+       "4    0.245483          0.382612           2.430182    Europe  2015   \n",
+       "\n",
+       "   social_support  cpi_score  \n",
+       "0             0.0         88  \n",
+       "1             0.0         91  \n",
+       "2             0.0         79  \n",
+       "3             0.0         86  \n",
+       "4             0.0         90  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"E:\\BSE\\WorldHappiness_Corruption_2015_2020.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cleaning Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country            0\n",
+       "happiness_score    0\n",
+       "gdp_per_capita     0\n",
+       "family             0\n",
+       "health             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum().head()  # to find missing values in dataset \n",
+    "#Answer: No missing values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.2030</td>\n",
+       "      <td>0.350000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.361000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.158000</td>\n",
+       "      <td>0.025000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>0.517000</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>657</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.3600</td>\n",
+       "      <td>0.382270</td>\n",
+       "      <td>0.110370</td>\n",
+       "      <td>0.173440</td>\n",
+       "      <td>0.164300</td>\n",
+       "      <td>0.312680</td>\n",
+       "      <td>0.071120</td>\n",
+       "      <td>2.145580</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.6320</td>\n",
+       "      <td>0.332000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.085000</td>\n",
+       "      <td>0.191000</td>\n",
+       "      <td>0.036000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>0.537000</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.5750</td>\n",
+       "      <td>0.319820</td>\n",
+       "      <td>0.302850</td>\n",
+       "      <td>0.303350</td>\n",
+       "      <td>0.234140</td>\n",
+       "      <td>0.365100</td>\n",
+       "      <td>0.097190</td>\n",
+       "      <td>1.952100</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>3.7940</td>\n",
+       "      <td>0.401477</td>\n",
+       "      <td>0.581543</td>\n",
+       "      <td>0.180747</td>\n",
+       "      <td>0.106180</td>\n",
+       "      <td>0.311871</td>\n",
+       "      <td>0.061158</td>\n",
+       "      <td>2.150801</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11</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",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>4.6100</td>\n",
+       "      <td>0.271000</td>\n",
+       "      <td>1.032760</td>\n",
+       "      <td>0.334750</td>\n",
+       "      <td>0.258610</td>\n",
+       "      <td>0.189870</td>\n",
+       "      <td>0.080790</td>\n",
+       "      <td>2.441910</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.8750</td>\n",
+       "      <td>0.375847</td>\n",
+       "      <td>1.083096</td>\n",
+       "      <td>0.196764</td>\n",
+       "      <td>0.336384</td>\n",
+       "      <td>0.189143</td>\n",
+       "      <td>0.095375</td>\n",
+       "      <td>1.597970</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>639</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>4.1930</td>\n",
+       "      <td>0.350410</td>\n",
+       "      <td>0.714780</td>\n",
+       "      <td>0.159500</td>\n",
+       "      <td>0.254290</td>\n",
+       "      <td>0.185030</td>\n",
+       "      <td>0.085820</td>\n",
+       "      <td>2.442700</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.6920</td>\n",
+       "      <td>0.357000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.248000</td>\n",
+       "      <td>0.406000</td>\n",
+       "      <td>0.132000</td>\n",
+       "      <td>0.099000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1.094000</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>3.2992</td>\n",
+       "      <td>0.425564</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375038</td>\n",
+       "      <td>0.377405</td>\n",
+       "      <td>0.151349</td>\n",
+       "      <td>0.080929</td>\n",
+       "      <td>0.841031</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.047835</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>792 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Country  happiness_score  gdp_per_capita    family    health  \\\n",
+       "395  Afghanistan           3.2030        0.350000  0.000000  0.361000   \n",
+       "657  Afghanistan           3.3600        0.382270  0.110370  0.173440   \n",
+       "519  Afghanistan           3.6320        0.332000  0.000000  0.255000   \n",
+       "259  Afghanistan           3.5750        0.319820  0.302850  0.303350   \n",
+       "120  Afghanistan           3.7940        0.401477  0.581543  0.180747   \n",
+       "..           ...              ...             ...       ...       ...   \n",
+       "229     Zimbabwe           4.6100        0.271000  1.032760  0.334750   \n",
+       "119     Zimbabwe           3.8750        0.375847  1.083096  0.196764   \n",
+       "639     Zimbabwe           4.1930        0.350410  0.714780  0.159500   \n",
+       "518     Zimbabwe           3.6920        0.357000  0.000000  0.248000   \n",
+       "790     Zimbabwe           3.2992        0.425564  0.000000  0.375038   \n",
+       "\n",
+       "      freedom  generosity  government_trust  dystopia_residual continent  \\\n",
+       "395  0.000000    0.158000          0.025000           0.000000      Asia   \n",
+       "657  0.164300    0.312680          0.071120           2.145580      Asia   \n",
+       "519  0.085000    0.191000          0.036000           0.000000      Asia   \n",
+       "259  0.234140    0.365100          0.097190           1.952100      Asia   \n",
+       "120  0.106180    0.311871          0.061158           2.150801      Asia   \n",
+       "..        ...         ...               ...                ...       ...   \n",
+       "229  0.258610    0.189870          0.080790           2.441910    Africa   \n",
+       "119  0.336384    0.189143          0.095375           1.597970    Africa   \n",
+       "639  0.254290    0.185030          0.085820           2.442700    Africa   \n",
+       "518  0.406000    0.132000          0.099000           0.000000    Africa   \n",
+       "790  0.377405    0.151349          0.080929           0.841031    Africa   \n",
+       "\n",
+       "     Year  social_support  cpi_score  \n",
+       "395  2017        0.517000         15  \n",
+       "657  2019        0.000000         16  \n",
+       "519  2018        0.537000         16  \n",
+       "259  2016        0.000000         15  \n",
+       "120  2015        0.000000         11  \n",
+       "..    ...             ...        ...  \n",
+       "229  2016        0.000000         22  \n",
+       "119  2015        0.000000         21  \n",
+       "639  2019        0.000000         24  \n",
+       "518  2018        1.094000         22  \n",
+       "790  2020        1.047835         24  \n",
+       "\n",
+       "[792 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sorting according country names\n",
+    "data.drop_duplicates(subset=['Country'], keep='last')\n",
+    "data.sort_values(['Country'])\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Understanding Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 792 entries, 395 to 790\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Country            792 non-null    object \n",
+      " 1   happiness_score    792 non-null    float64\n",
+      " 2   gdp_per_capita     792 non-null    float64\n",
+      " 3   family             792 non-null    float64\n",
+      " 4   health             792 non-null    float64\n",
+      " 5   freedom            792 non-null    float64\n",
+      " 6   generosity         792 non-null    float64\n",
+      " 7   government_trust   792 non-null    float64\n",
+      " 8   dystopia_residual  792 non-null    float64\n",
+      " 9   continent          792 non-null    object \n",
+      " 10  Year               792 non-null    int64  \n",
+      " 11  social_support     792 non-null    float64\n",
+      " 12  cpi_score          792 non-null    int64  \n",
+      "dtypes: float64(9), int64(2), object(2)\n",
+      "memory usage: 86.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(792, 13)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "      <td>792.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.473310</td>\n",
+       "      <td>0.929217</td>\n",
+       "      <td>0.504998</td>\n",
+       "      <td>0.648691</td>\n",
+       "      <td>0.427016</td>\n",
+       "      <td>0.212440</td>\n",
+       "      <td>0.125720</td>\n",
+       "      <td>1.379277</td>\n",
+       "      <td>2017.500000</td>\n",
+       "      <td>0.609302</td>\n",
+       "      <td>44.327020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.124726</td>\n",
+       "      <td>0.385774</td>\n",
+       "      <td>0.552736</td>\n",
+       "      <td>0.232261</td>\n",
+       "      <td>0.148003</td>\n",
+       "      <td>0.121660</td>\n",
+       "      <td>0.109032</td>\n",
+       "      <td>1.075276</td>\n",
+       "      <td>1.708904</td>\n",
+       "      <td>0.640754</td>\n",
+       "      <td>19.508833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.566900</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.590750</td>\n",
+       "      <td>0.644242</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.510070</td>\n",
+       "      <td>0.325433</td>\n",
+       "      <td>0.125784</td>\n",
+       "      <td>0.052862</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.485500</td>\n",
+       "      <td>0.994544</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.685397</td>\n",
+       "      <td>0.439621</td>\n",
+       "      <td>0.197000</td>\n",
+       "      <td>0.089000</td>\n",
+       "      <td>1.731948</td>\n",
+       "      <td>2017.500000</td>\n",
+       "      <td>0.176214</td>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.300500</td>\n",
+       "      <td>1.228677</td>\n",
+       "      <td>1.040410</td>\n",
+       "      <td>0.815641</td>\n",
+       "      <td>0.546280</td>\n",
+       "      <td>0.273250</td>\n",
+       "      <td>0.154250</td>\n",
+       "      <td>2.236894</td>\n",
+       "      <td>2019.000000</td>\n",
+       "      <td>1.268250</td>\n",
+       "      <td>57.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.808700</td>\n",
+       "      <td>2.096000</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>1.141000</td>\n",
+       "      <td>0.724000</td>\n",
+       "      <td>0.838075</td>\n",
+       "      <td>0.551910</td>\n",
+       "      <td>3.602140</td>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>1.644000</td>\n",
+       "      <td>91.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       happiness_score  gdp_per_capita      family      health     freedom  \\\n",
+       "count       792.000000      792.000000  792.000000  792.000000  792.000000   \n",
+       "mean          5.473310        0.929217    0.504998    0.648691    0.427016   \n",
+       "std           1.124726        0.385774    0.552736    0.232261    0.148003   \n",
+       "min           2.566900        0.000000    0.000000    0.000000    0.000000   \n",
+       "25%           4.590750        0.644242    0.000000    0.510070    0.325433   \n",
+       "50%           5.485500        0.994544    0.000000    0.685397    0.439621   \n",
+       "75%           6.300500        1.228677    1.040410    0.815641    0.546280   \n",
+       "max           7.808700        2.096000    1.610574    1.141000    0.724000   \n",
+       "\n",
+       "       generosity  government_trust  dystopia_residual         Year  \\\n",
+       "count  792.000000        792.000000         792.000000   792.000000   \n",
+       "mean     0.212440          0.125720           1.379277  2017.500000   \n",
+       "std      0.121660          0.109032           1.075276     1.708904   \n",
+       "min      0.000000          0.000000           0.000000  2015.000000   \n",
+       "25%      0.125784          0.052862           0.000000  2016.000000   \n",
+       "50%      0.197000          0.089000           1.731948  2017.500000   \n",
+       "75%      0.273250          0.154250           2.236894  2019.000000   \n",
+       "max      0.838075          0.551910           3.602140  2020.000000   \n",
+       "\n",
+       "       social_support   cpi_score  \n",
+       "count      792.000000  792.000000  \n",
+       "mean         0.609302   44.327020  \n",
+       "std          0.640754   19.508833  \n",
+       "min          0.000000   11.000000  \n",
+       "25%          0.000000   30.000000  \n",
+       "50%          0.176214   38.000000  \n",
+       "75%          1.268250   57.000000  \n",
+       "max          1.644000   91.000000  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA (Univariate and Multivariate Analysis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total COuntries = 132\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Total COuntries = {}\".format(data[\"Country\"].nunique()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc = {'figure.figsize':(10,5)})            #heat map to see correlation\n",
+    "sns.heatmap(data.corr(), annot= True,  cmap='viridis')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='cpi_score', ylabel='Country'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Countries with height curroption rate\n",
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.barplot(\n",
+    "    y='Country',\n",
+    "    x='cpi_score',\n",
+    "    data=data.nlargest(10, 'cpi_score')\n",
+    ")\n",
+    "#Ans: Denmark "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Country', ylabel='happiness_score'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#the happiest country?\n",
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.barplot(\n",
+    "    x='Country',\n",
+    "    y='happiness_score',\n",
+    "    data=data.nlargest(10, 'happiness_score'), palette='cubehelix'\n",
+    ")\n",
+    "#Ans: Finland"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='happiness_score', ylabel='cpi_score'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Correlation b/w Happiness and corruption rate\n",
+    "sns.scatterplot(x=\"happiness_score\", y = \"cpi_score\", data = data)\n",
+    "\n",
+    "#Ans: There is a positive or direct relatinship "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1878b90f880>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Country that provides Family Care opportunities \n",
+    "sns.set(rc = {'figure.figsize':(20,20)})\n",
+    "sns.catplot(x = \"Country\", y = \"family\", palette = \"summer\", data = data.nlargest(4, 'family'))\n",
+    "\n",
+    "#Ans: Iceland "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='family', ylabel='cpi_score'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#relation between family care and corruption rate\n",
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.scatterplot(x=\"family\", y = \"cpi_score\", data = data, color = \"red\")\n",
+    "#Ans: Direct relation, increase in one and the other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Country', ylabel='gdp_per_capita'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Highest GDP \n",
+    "sns.barplot(y= \"gdp_per_capita\", x = \"Country\", data = data.nlargest(8, 'gdp_per_capita'))\n",
+    "\n",
+    "#Ans: United Arab Emrites has the highest GDP per Capita "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1878be13970>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#relation b/w GPD and CPI \n",
+    "sns.set(rc = {'figure.figsize':(30,10)})\n",
+    "sns.relplot(data=data, x=\"gdp_per_capita\", y=\"cpi_score\", kind=\"line\", color = \"Purple\")\n",
+    "\n",
+    "#Ans: Directly proprotional "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "hovertemplate": "Country=%{label}<br>health=%{value}<extra></extra>",
+         "labels": [
+          "Singapore",
+          "Singapore",
+          "Japan",
+          "Japan",
+          "Spain"
+         ],
+         "legendgroup": "",
+         "name": "",
+         "showlegend": true,
+         "type": "pie",
+         "values": [
+          1.141,
+          1.137814283,
+          1.088,
+          1.072881341,
+          1.062
+         ]
+        }
+       ],
+       "layout": {
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "piecolorway": [
+         "rgb(103,0,31)",
+         "rgb(178,24,43)",
+         "rgb(214,96,77)",
+         "rgb(244,165,130)",
+         "rgb(253,219,199)",
+         "rgb(247,247,247)",
+         "rgb(209,229,240)",
+         "rgb(146,197,222)",
+         "rgb(67,147,195)",
+         "rgb(33,102,172)",
+         "rgb(5,48,97)"
+        ],
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
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+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
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+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
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+          "hoverlabel": {
+           "align": "left"
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+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
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+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Countries With The Highest Health Rates"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#heighest Health Care Rate \n",
+    "fig = px.pie(data.nlargest(5, 'health'), values='health', names='Country', title='Countries With The Highest Health Rates', color_discrete_sequence=px.colors.sequential.RdBu)\n",
+    "fig.show()\n",
+    "\n",
+    "#Ans: Singapore "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1878d9f0d00>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Effect of healthccare on corruption rate \n",
+    "sns.relplot(\n",
+    "    data=data,\n",
+    "    x=\"health\", y=\"cpi_score\",\n",
+    "    palette=\"ch:r=-.5,l=.75\"\n",
+    ")\n",
+    "\n",
+    "#Ans: direct relation "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Country=%{x}<br>freedom=%{y}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "",
+         "offsetgroup": "",
+         "orientation": "v",
+         "showlegend": false,
+         "textposition": "auto",
+         "type": "bar",
+         "x": [
+          "Uzbekistan",
+          "Cambodia",
+          "Uzbekistan",
+          "Norway",
+          "Denmark"
+         ],
+         "xaxis": "x",
+         "y": [
+          0.724,
+          0.696,
+          0.693270326,
+          0.686,
+          0.683
+         ],
+         "yaxis": "y"
+        }
+       ],
+       "layout": {
+        "barmode": "relative",
+        "height": 400,
+        "legend": {
+         "tracegroupgap": 0
+        },
+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
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+            "showbackground": true,
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+            "ticks": "",
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+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
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+          "ternary": {
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+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Country"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "freedom"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.bar(data.nlargest(5, 'freedom'), x='Country', y='freedom', height=400)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Country', ylabel='gdp_per_capita'>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2160x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Most generous state\n",
+    "sns.barplot(y= \"gdp_per_capita\", x = \"Country\", data = data.nlargest(8, 'generosity'))\n",
+    "\n",
+    "#ans: malta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "continent=Asia<br>generosity=%{x}<br>cpi_score=%{y}<extra></extra>",
+         "legendgroup": "Asia",
+         "marker": {
+          "color": "#636efa",
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "Asia",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "scatter",
+         "x": [
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+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "generosity"
+         },
+         "type": "log"
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "cpi_score"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.scatter(data, x=\"generosity\", y=\"cpi_score\", color=\"continent\", log_x=True, size_max=60)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outliers in the Data <br>\n",
+    "Scatter plots often have a pattern. We call a data point an outlier if it doesn't fit the pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='happiness_score', ylabel='cpi_score'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.scatterplot(x = \"happiness_score\", y = \"cpi_score\", data = data, color = \"green\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    792.000000\n",
+       "mean       0.212440\n",
+       "std        0.121660\n",
+       "min        0.000000\n",
+       "25%        0.125784\n",
+       "50%        0.197000\n",
+       "75%        0.273250\n",
+       "max        0.838075\n",
+       "Name: generosity, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"generosity\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='family', ylabel='cpi_score'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.scatterplot(x = \"family\", y = \"cpi_score\", data = data, color = \"purple\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    792.000000\n",
+       "mean       0.609302\n",
+       "std        0.640754\n",
+       "min        0.000000\n",
+       "25%        0.000000\n",
+       "50%        0.176214\n",
+       "75%        1.268250\n",
+       "max        1.644000\n",
+       "Name: social_support, dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"social_support\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='social_support', ylabel='cpi_score'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.scatterplot(x = \"social_support\", y = \"cpi_score\", data = data, color = \"black\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='social_support', ylabel='cpi_score'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc = {'figure.figsize':(8,6)})\n",
+    "sns.scatterplot(x = \"social_support\", y = \"cpi_score\", data = data, color = \"black\")\n",
+    "\n",
+    "# All the points are following the pattern of Scatter plot so there are no outliers. The data is clean. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_cpi_score(Country):        #Extra Answer\n",
+    "    mask = data['Country'] == Country\n",
+    "    return data[mask].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_cpi_score('Europe')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Engineering "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>7.537</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>7.522</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>49</td>\n",
+       "      <td>7.504</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>112</td>\n",
+       "      <td>7.494</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>7.469</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country  happiness_score  gdp_per_capita    family    health   freedom  \\\n",
+       "0       90            7.537        1.616463  1.533524  0.796667  0.635423   \n",
+       "1       30            7.522        1.482383  1.551122  0.792566  0.626007   \n",
+       "2       49            7.504        1.480633  1.610574  0.833552  0.627163   \n",
+       "3      112            7.494        1.564980  1.516912  0.858131  0.620071   \n",
+       "4       37            7.469        1.443572  1.540247  0.809158  0.617951   \n",
+       "\n",
+       "   generosity  government_trust  dystopia_residual continent  Year  \\\n",
+       "0    0.362012          0.315964           2.277027    Europe  2015   \n",
+       "1    0.355280          0.400770           2.313707    Europe  2015   \n",
+       "2    0.475540          0.153527           2.322715    Europe  2015   \n",
+       "3    0.290549          0.367007           2.276716    Europe  2015   \n",
+       "4    0.245483          0.382612           2.430182    Europe  2015   \n",
+       "\n",
+       "   social_support  cpi_score  \n",
+       "0             0.0         88  \n",
+       "1             0.0         91  \n",
+       "2             0.0         79  \n",
+       "3             0.0         86  \n",
+       "4             0.0         90  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Country Scaling \n",
+    "Malayka_Country = LabelEncoder()\n",
+    "data[\"Country\"] = Malayka_Country.fit_transform(data[\"Country\"])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>Country</th>\n",
+       "      <th>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>7.537</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>7.522</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>49</td>\n",
+       "      <td>7.504</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>112</td>\n",
+       "      <td>7.494</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>7.469</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country  happiness_score  gdp_per_capita    family    health   freedom  \\\n",
+       "0       90            7.537        1.616463  1.533524  0.796667  0.635423   \n",
+       "1       30            7.522        1.482383  1.551122  0.792566  0.626007   \n",
+       "2       49            7.504        1.480633  1.610574  0.833552  0.627163   \n",
+       "3      112            7.494        1.564980  1.516912  0.858131  0.620071   \n",
+       "4       37            7.469        1.443572  1.540247  0.809158  0.617951   \n",
+       "\n",
+       "   generosity  government_trust  dystopia_residual  continent  Year  \\\n",
+       "0    0.362012          0.315964           2.277027          3  2015   \n",
+       "1    0.355280          0.400770           2.313707          3  2015   \n",
+       "2    0.475540          0.153527           2.322715          3  2015   \n",
+       "3    0.290549          0.367007           2.276716          3  2015   \n",
+       "4    0.245483          0.382612           2.430182          3  2015   \n",
+       "\n",
+       "   social_support  cpi_score  \n",
+       "0             0.0         88  \n",
+       "1             0.0         91  \n",
+       "2             0.0         79  \n",
+       "3             0.0         86  \n",
+       "4             0.0         90  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Continent Encoding \n",
+    "continento = LabelEncoder()\n",
+    "data[\"continent\"] = continento.fit_transform(data[\"continent\"])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>happiness_score</th>\n",
+       "      <th>gdp_per_capita</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "      <th>government_trust</th>\n",
+       "      <th>dystopia_residual</th>\n",
+       "      <th>social_support</th>\n",
+       "      <th>cpi_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.537</td>\n",
+       "      <td>1.616463</td>\n",
+       "      <td>1.533524</td>\n",
+       "      <td>0.796667</td>\n",
+       "      <td>0.635423</td>\n",
+       "      <td>0.362012</td>\n",
+       "      <td>0.315964</td>\n",
+       "      <td>2.277027</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.522</td>\n",
+       "      <td>1.482383</td>\n",
+       "      <td>1.551122</td>\n",
+       "      <td>0.792566</td>\n",
+       "      <td>0.626007</td>\n",
+       "      <td>0.355280</td>\n",
+       "      <td>0.400770</td>\n",
+       "      <td>2.313707</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.504</td>\n",
+       "      <td>1.480633</td>\n",
+       "      <td>1.610574</td>\n",
+       "      <td>0.833552</td>\n",
+       "      <td>0.627163</td>\n",
+       "      <td>0.475540</td>\n",
+       "      <td>0.153527</td>\n",
+       "      <td>2.322715</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.494</td>\n",
+       "      <td>1.564980</td>\n",
+       "      <td>1.516912</td>\n",
+       "      <td>0.858131</td>\n",
+       "      <td>0.620071</td>\n",
+       "      <td>0.290549</td>\n",
+       "      <td>0.367007</td>\n",
+       "      <td>2.276716</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.469</td>\n",
+       "      <td>1.443572</td>\n",
+       "      <td>1.540247</td>\n",
+       "      <td>0.809158</td>\n",
+       "      <td>0.617951</td>\n",
+       "      <td>0.245483</td>\n",
+       "      <td>0.382612</td>\n",
+       "      <td>2.430182</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   happiness_score  gdp_per_capita    family    health   freedom  generosity  \\\n",
+       "0            7.537        1.616463  1.533524  0.796667  0.635423    0.362012   \n",
+       "1            7.522        1.482383  1.551122  0.792566  0.626007    0.355280   \n",
+       "2            7.504        1.480633  1.610574  0.833552  0.627163    0.475540   \n",
+       "3            7.494        1.564980  1.516912  0.858131  0.620071    0.290549   \n",
+       "4            7.469        1.443572  1.540247  0.809158  0.617951    0.245483   \n",
+       "\n",
+       "   government_trust  dystopia_residual  social_support  cpi_score  \n",
+       "0          0.315964           2.277027             0.0         88  \n",
+       "1          0.400770           2.313707             0.0         91  \n",
+       "2          0.153527           2.322715             0.0         79  \n",
+       "3          0.367007           2.276716             0.0         86  \n",
+       "4          0.382612           2.430182             0.0         90  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.drop([\"Country\", \"continent\", \"Year\"], axis = 1)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Building "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data.drop(['cpi_score'], axis= 1).values  \n",
+    "#drop cpi_score column from training data because that is the target variable that we want to predict using our model \n",
+    "y = data[\"cpi_score\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(792, 9)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(792,)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape  #we only have one column as dependent or target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train , x_test , y_train , y_test = train_test_split(x,y, test_size= 0.20, random_state =42,shuffle=True) #20% test data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(633, 9)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Sequential()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#input layer\n",
+    "model.add(Dense(150, input_shape = (9,), activation = 'relu')) #in train shape we have 9 column "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#hidden layers\n",
+    "model.add(Dense(100, activation = \"linear\"))\n",
+    "model.add(Dense(95, activation = \"linear\"))\n",
+    "model.add(Dense(90, activation = \"linear\"))\n",
+    "model.add(Dense(85, activation = \"linear\"))\n",
+    "model.add(Dense(80, activation = \"linear\"))\n",
+    "model.add(Dense(75, activation = \"linear\"))\n",
+    "model.add(Dense(70, activation = \"linear\"))\n",
+    "model.add(Dense(65, activation = \"linear\"))\n",
+    "model.add(Dense(60, activation = \"linear\"))\n",
+    "model.add(Dense(55, activation = \"linear\"))\n",
+    "model.add(Dense(50, activation = \"linear\"))\n",
+    "model.add(Dense(45, activation = \"linear\"))\n",
+    "model.add(Dense(40, activation = \"linear\"))\n",
+    "model.add(Dense(35, activation = \"linear\"))\n",
+    "model.add(Dense(30, activation = \"linear\"))\n",
+    "model.add(Dense(25, activation = \"linear\"))\n",
+    "model.add(Dense(20, activation = \"linear\"))\n",
+    "model.add(Dense(15, activation = \"linear\"))\n",
+    "model.add(Dense(10, activation = \"linear\"))\n",
+    "model.add(Dense(5, activation = \"linear\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#output layer\n",
+    "model.add(Dense(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#optimizer and loss funct\n",
+    "model.compile(optimizer = \"Adam\", loss = \"mse\", metrics=\"mae\")  #we used MSE bcz this is regression problem "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/80\n",
+      "7/7 [==============================] - 0s 43ms/step - loss: 77.8656 - mae: 6.9202 - val_loss: 84.6057 - val_mae: 7.4814\n",
+      "Epoch 2/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 61.9859 - mae: 6.0949 - val_loss: 86.3431 - val_mae: 7.6217\n",
+      "Epoch 3/80\n",
+      "7/7 [==============================] - 0s 26ms/step - loss: 59.4526 - mae: 5.9610 - val_loss: 88.4333 - val_mae: 7.3047\n",
+      "Epoch 4/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 55.9433 - mae: 5.6956 - val_loss: 97.7907 - val_mae: 7.5938\n",
+      "Epoch 5/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 57.9376 - mae: 5.7782 - val_loss: 97.7293 - val_mae: 7.6094\n",
+      "Epoch 6/80\n",
+      "7/7 [==============================] - 0s 34ms/step - loss: 58.0639 - mae: 5.8954 - val_loss: 91.4090 - val_mae: 7.3864\n",
+      "Epoch 7/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 55.7976 - mae: 5.7068 - val_loss: 85.0820 - val_mae: 7.4750\n",
+      "Epoch 8/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 58.4424 - mae: 5.9083 - val_loss: 85.1645 - val_mae: 7.5536\n",
+      "Epoch 9/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 63.2473 - mae: 6.1902 - val_loss: 86.8809 - val_mae: 7.4398\n",
+      "Epoch 10/80\n",
+      "7/7 [==============================] - 0s 34ms/step - loss: 59.6861 - mae: 5.9504 - val_loss: 86.6050 - val_mae: 7.3327\n",
+      "Epoch 11/80\n",
+      "7/7 [==============================] - 0s 22ms/step - loss: 56.0595 - mae: 5.7479 - val_loss: 84.2652 - val_mae: 7.1741\n",
+      "Epoch 12/80\n",
+      "7/7 [==============================] - 0s 42ms/step - loss: 57.1973 - mae: 5.7595 - val_loss: 106.0522 - val_mae: 7.9195\n",
+      "Epoch 13/80\n",
+      "7/7 [==============================] - 0s 33ms/step - loss: 64.1657 - mae: 6.2228 - val_loss: 92.8841 - val_mae: 7.4103\n",
+      "Epoch 14/80\n",
+      "7/7 [==============================] - 0s 22ms/step - loss: 59.9128 - mae: 6.0129 - val_loss: 96.1182 - val_mae: 7.5591\n",
+      "Epoch 15/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 65.5625 - mae: 6.2597 - val_loss: 88.8145 - val_mae: 7.6714\n",
+      "Epoch 16/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 62.2448 - mae: 6.1110 - val_loss: 84.8362 - val_mae: 7.3317\n",
+      "Epoch 17/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 56.6592 - mae: 5.7579 - val_loss: 90.4816 - val_mae: 7.3526\n",
+      "Epoch 18/80\n",
+      "7/7 [==============================] - 0s 23ms/step - loss: 60.5915 - mae: 5.9275 - val_loss: 92.1785 - val_mae: 7.4964\n",
+      "Epoch 19/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 58.8743 - mae: 5.8753 - val_loss: 85.4367 - val_mae: 7.1875\n",
+      "Epoch 20/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 56.3894 - mae: 5.7524 - val_loss: 82.9822 - val_mae: 7.3357\n",
+      "Epoch 21/80\n",
+      "7/7 [==============================] - 0s 26ms/step - loss: 60.4853 - mae: 5.9427 - val_loss: 104.0495 - val_mae: 7.8214\n",
+      "Epoch 22/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 58.0329 - mae: 5.8606 - val_loss: 91.4397 - val_mae: 7.3741\n",
+      "Epoch 23/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 58.3387 - mae: 5.8571 - val_loss: 84.2954 - val_mae: 7.4498\n",
+      "Epoch 24/80\n",
+      "7/7 [==============================] - 0s 30ms/step - loss: 59.1032 - mae: 5.9471 - val_loss: 129.3002 - val_mae: 8.9888\n",
+      "Epoch 25/80\n",
+      "7/7 [==============================] - 0s 43ms/step - loss: 68.0509 - mae: 6.4536 - val_loss: 133.6119 - val_mae: 9.1128\n",
+      "Epoch 26/80\n",
+      "7/7 [==============================] - 0s 26ms/step - loss: 75.7288 - mae: 6.8561 - val_loss: 88.6896 - val_mae: 7.3428\n",
+      "Epoch 27/80\n",
+      "7/7 [==============================] - 0s 23ms/step - loss: 55.9561 - mae: 5.7355 - val_loss: 85.7020 - val_mae: 7.2802\n",
+      "Epoch 28/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 56.8981 - mae: 5.7665 - val_loss: 93.6828 - val_mae: 7.4359\n",
+      "Epoch 29/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 59.5533 - mae: 6.0136 - val_loss: 101.1561 - val_mae: 7.7171\n",
+      "Epoch 30/80\n",
+      "7/7 [==============================] - 0s 41ms/step - loss: 62.4289 - mae: 6.1328 - val_loss: 84.5610 - val_mae: 7.5842\n",
+      "Epoch 31/80\n",
+      "7/7 [==============================] - 0s 21ms/step - loss: 60.6215 - mae: 6.0831 - val_loss: 91.1915 - val_mae: 7.3900\n",
+      "Epoch 32/80\n",
+      "7/7 [==============================] - 0s 21ms/step - loss: 57.3807 - mae: 5.8343 - val_loss: 118.5399 - val_mae: 8.4344\n",
+      "Epoch 33/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 59.9364 - mae: 5.9945 - val_loss: 92.5041 - val_mae: 7.4167\n",
+      "Epoch 34/80\n",
+      "7/7 [==============================] - 0s 26ms/step - loss: 56.2940 - mae: 5.7286 - val_loss: 86.4576 - val_mae: 7.1673\n",
+      "Epoch 35/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 57.4891 - mae: 5.8019 - val_loss: 133.8274 - val_mae: 9.0784\n",
+      "Epoch 36/80\n",
+      "7/7 [==============================] - 0s 26ms/step - loss: 76.8091 - mae: 6.8179 - val_loss: 92.8922 - val_mae: 7.9796\n",
+      "Epoch 37/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 78.1175 - mae: 6.8944 - val_loss: 88.9584 - val_mae: 7.7148\n",
+      "Epoch 38/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 73.7874 - mae: 6.6862 - val_loss: 113.8042 - val_mae: 8.2263\n",
+      "Epoch 39/80\n",
+      "7/7 [==============================] - 0s 40ms/step - loss: 62.6278 - mae: 6.0775 - val_loss: 106.5124 - val_mae: 7.9289\n",
+      "Epoch 40/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 59.3173 - mae: 5.8819 - val_loss: 98.8691 - val_mae: 7.6307\n",
+      "Epoch 41/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 59.4269 - mae: 5.8489 - val_loss: 84.8993 - val_mae: 7.1797\n",
+      "Epoch 42/80\n",
+      "7/7 [==============================] - 0s 37ms/step - loss: 56.7730 - mae: 5.7920 - val_loss: 91.5562 - val_mae: 7.4183\n",
+      "Epoch 43/80\n",
+      "7/7 [==============================] - 0s 23ms/step - loss: 54.5299 - mae: 5.6292 - val_loss: 84.3323 - val_mae: 7.1894\n",
+      "Epoch 44/80\n",
+      "7/7 [==============================] - 0s 23ms/step - loss: 54.9053 - mae: 5.6379 - val_loss: 88.5784 - val_mae: 7.2586\n",
+      "Epoch 45/80\n",
+      "7/7 [==============================] - 0s 24ms/step - loss: 55.5565 - mae: 5.6991 - val_loss: 86.2487 - val_mae: 7.1826\n",
+      "Epoch 46/80\n",
+      "7/7 [==============================] - 0s 24ms/step - loss: 54.2637 - mae: 5.6148 - val_loss: 108.4909 - val_mae: 7.9855\n",
+      "Epoch 47/80\n",
+      "7/7 [==============================] - 0s 35ms/step - loss: 58.2308 - mae: 5.8741 - val_loss: 97.0401 - val_mae: 7.5709\n",
+      "Epoch 48/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 56.1594 - mae: 5.7403 - val_loss: 91.0246 - val_mae: 7.3167\n",
+      "Epoch 49/80\n",
+      "7/7 [==============================] - 0s 23ms/step - loss: 55.4485 - mae: 5.7447 - val_loss: 116.7603 - val_mae: 8.4075\n",
+      "Epoch 50/80\n",
+      "7/7 [==============================] - 0s 37ms/step - loss: 57.5263 - mae: 5.8049 - val_loss: 89.1802 - val_mae: 7.6997\n",
+      "Epoch 51/80\n",
+      "7/7 [==============================] - 0s 32ms/step - loss: 64.8187 - mae: 6.1777 - val_loss: 89.0749 - val_mae: 7.2035\n",
+      "Epoch 52/80\n",
+      "7/7 [==============================] - 0s 22ms/step - loss: 56.9130 - mae: 5.7970 - val_loss: 107.5232 - val_mae: 7.9867\n",
+      "Epoch 53/80\n",
+      "7/7 [==============================] - 0s 24ms/step - loss: 60.3007 - mae: 5.9283 - val_loss: 84.2406 - val_mae: 7.4122\n",
+      "Epoch 54/80\n",
+      "7/7 [==============================] - 0s 23ms/step - loss: 56.4504 - mae: 5.7477 - val_loss: 86.8226 - val_mae: 7.2591\n",
+      "Epoch 55/80\n",
+      "7/7 [==============================] - 0s 22ms/step - loss: 55.2568 - mae: 5.6731 - val_loss: 100.5793 - val_mae: 7.6673\n",
+      "Epoch 56/80\n",
+      "7/7 [==============================] - 0s 36ms/step - loss: 61.2951 - mae: 5.9974 - val_loss: 87.0366 - val_mae: 7.2673\n",
+      "Epoch 57/80\n",
+      "7/7 [==============================] - 0s 21ms/step - loss: 54.0641 - mae: 5.6518 - val_loss: 100.3164 - val_mae: 7.7061\n",
+      "Epoch 58/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 60.7934 - mae: 6.0940 - val_loss: 91.1460 - val_mae: 7.7483\n",
+      "Epoch 59/80\n",
+      "7/7 [==============================] - 0s 54ms/step - loss: 63.2472 - mae: 6.1660 - val_loss: 86.3726 - val_mae: 7.2902\n",
+      "Epoch 60/80\n",
+      "7/7 [==============================] - 0s 25ms/step - loss: 55.9091 - mae: 5.6639 - val_loss: 86.8973 - val_mae: 7.2925\n",
+      "Epoch 61/80\n",
+      "7/7 [==============================] - 0s 24ms/step - loss: 53.5919 - mae: 5.5313 - val_loss: 82.9541 - val_mae: 7.1086\n",
+      "Epoch 62/80\n",
+      "7/7 [==============================] - 0s 33ms/step - loss: 55.4018 - mae: 5.6540 - val_loss: 119.5432 - val_mae: 8.5018\n",
+      "Epoch 63/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 62.9776 - mae: 6.1793 - val_loss: 87.0080 - val_mae: 7.3126\n",
+      "Epoch 64/80\n",
+      "7/7 [==============================] - 0s 26ms/step - loss: 58.7028 - mae: 5.9835 - val_loss: 89.2327 - val_mae: 7.6363\n",
+      "Epoch 65/80\n",
+      "7/7 [==============================] - 0s 22ms/step - loss: 58.5767 - mae: 5.9327 - val_loss: 102.2099 - val_mae: 7.7837\n",
+      "Epoch 66/80\n",
+      "7/7 [==============================] - 0s 24ms/step - loss: 57.4547 - mae: 5.7887 - val_loss: 104.0714 - val_mae: 7.8279\n",
+      "Epoch 67/80\n",
+      "7/7 [==============================] - 0s 43ms/step - loss: 57.8075 - mae: 5.9085 - val_loss: 89.2663 - val_mae: 7.6840\n",
+      "Epoch 68/80\n",
+      "7/7 [==============================] - 0s 38ms/step - loss: 59.1691 - mae: 5.9695 - val_loss: 83.3056 - val_mae: 7.1205\n",
+      "Epoch 69/80\n",
+      "7/7 [==============================] - 0s 24ms/step - loss: 60.8919 - mae: 6.1349 - val_loss: 178.8922 - val_mae: 10.7475\n",
+      "Epoch 70/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 84.4323 - mae: 7.1516 - val_loss: 118.0532 - val_mae: 9.0434\n",
+      "Epoch 71/80\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 97.9834 - mae: 7.8229 - val_loss: 91.5085 - val_mae: 7.6531\n",
+      "Epoch 72/80\n",
+      "7/7 [==============================] - 0s 27ms/step - loss: 74.8695 - mae: 6.6427 - val_loss: 110.2923 - val_mae: 8.1662\n",
+      "Epoch 73/80\n",
+      "7/7 [==============================] - 0s 36ms/step - loss: 62.5519 - mae: 6.0439 - val_loss: 93.4866 - val_mae: 7.4138\n",
+      "Epoch 74/80\n",
+      "7/7 [==============================] - 0s 37ms/step - loss: 61.1919 - mae: 5.9871 - val_loss: 87.1830 - val_mae: 7.5567\n",
+      "Epoch 75/80\n",
+      "7/7 [==============================] - 0s 56ms/step - loss: 61.2859 - mae: 6.0222 - val_loss: 81.9624 - val_mae: 7.1349\n",
+      "Epoch 76/80\n",
+      "7/7 [==============================] - 0s 22ms/step - loss: 58.8851 - mae: 5.8782 - val_loss: 113.0341 - val_mae: 8.2048\n",
+      "Epoch 77/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 65.2962 - mae: 6.3057 - val_loss: 83.5141 - val_mae: 7.0850\n",
+      "Epoch 78/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 58.2327 - mae: 5.8571 - val_loss: 93.1269 - val_mae: 7.9751\n",
+      "Epoch 79/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 66.5099 - mae: 6.3910 - val_loss: 109.4550 - val_mae: 8.0498\n",
+      "Epoch 80/80\n",
+      "7/7 [==============================] - 0s 28ms/step - loss: 61.8142 - mae: 6.0195 - val_loss: 103.9000 - val_mae: 7.8212\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x2bc46576850>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#training\n",
+    "\n",
+    "model.fit(x = x_train, y = y_train, validation_data = (x_test, y_test), batch_size = 100, epochs = 80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5/5 [==============================] - 1s 12ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "#testing\n",
+    "model_pred = model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.821244845600249"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Model Loss\n",
+    "mae = mean_absolute_error(y_test, model_pred)\n",
+    "mae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "103.89995478275583"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#mean squared error\n",
+    "mse4 = mean_squared_error(y_test, model_pred)\n",
+    "mse4"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualization of actual and predicted value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2bc46660040>]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test, model_pred)\n",
+    "plt.plot(y_test, y_test, \"r\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5/5 [==============================] - 0s 13ms/step - loss: 103.8999 - mae: 7.8212\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[103.89994812011719, 7.821245193481445]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.evaluate(x_test, y_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}