{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score,RocCurveDisplay,PrecisionRecallDisplay,classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1421 entries, 0 to 1420\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Gender      1421 non-null   int64  \n",
      " 1   Hemoglobin  1421 non-null   float64\n",
      " 2   MCH         1421 non-null   float64\n",
      " 3   MCHC        1421 non-null   float64\n",
      " 4   MCV         1421 non-null   float64\n",
      " 5   Result      1421 non-null   int64  \n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 66.7 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"anemia.csv\")\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.520760</td>\n",
       "      <td>13.412738</td>\n",
       "      <td>22.905630</td>\n",
       "      <td>30.251232</td>\n",
       "      <td>85.523786</td>\n",
       "      <td>0.436312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499745</td>\n",
       "      <td>1.974546</td>\n",
       "      <td>3.969375</td>\n",
       "      <td>1.400898</td>\n",
       "      <td>9.636701</td>\n",
       "      <td>0.496102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>69.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>77.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>85.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>101.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gender   Hemoglobin          MCH         MCHC          MCV  \\\n",
       "count  1421.000000  1421.000000  1421.000000  1421.000000  1421.000000   \n",
       "mean      0.520760    13.412738    22.905630    30.251232    85.523786   \n",
       "std       0.499745     1.974546     3.969375     1.400898     9.636701   \n",
       "min       0.000000     6.600000    16.000000    27.800000    69.400000   \n",
       "25%       0.000000    11.700000    19.400000    29.000000    77.300000   \n",
       "50%       1.000000    13.200000    22.700000    30.400000    85.300000   \n",
       "75%       1.000000    15.000000    26.200000    31.400000    94.200000   \n",
       "max       1.000000    16.900000    30.000000    32.500000   101.600000   \n",
       "\n",
       "            Result  \n",
       "count  1421.000000  \n",
       "mean      0.436312  \n",
       "std       0.496102  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>71.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Hemoglobin   MCH  MCHC   MCV  Result\n",
       "0       1        14.9  22.7  29.1  83.7       0\n",
       "1       0        15.9  25.4  28.3  72.0       0\n",
       "2       0         9.0  21.5  29.6  71.2       1\n",
       "3       0        14.9  16.0  31.4  87.5       0\n",
       "4       1        14.7  22.0  28.2  99.5       0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>0</td>\n",
       "      <td>10.6</td>\n",
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       "      <td>28.2</td>\n",
       "      <td>82.9</td>\n",
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       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>28.3</td>\n",
       "      <td>30.4</td>\n",
       "      <td>86.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>28.1</td>\n",
       "      <td>80.7</td>\n",
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       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>16.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>95.2</td>\n",
       "      <td>0</td>\n",
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       "      <th>1420</th>\n",
       "      <td>0</td>\n",
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       "      <td>98.1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Hemoglobin   MCH  MCHC   MCV  Result\n",
       "1416       0        10.6  25.4  28.2  82.9       1\n",
       "1417       1        12.1  28.3  30.4  86.9       1\n",
       "1418       1        13.1  17.7  28.1  80.7       1\n",
       "1419       0        14.3  16.2  29.5  95.2       0\n",
       "1420       0        11.8  21.2  28.4  98.1       1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Gender, Hemoglobin, MCH, MCHC, MCV, Result]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data[\"Hemoglobin\"] < 1) | (data[\"MCV\"] < 1) | (data[\"MCH\"] < 1) | (data[\"MCHC\"] < 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Distribution:\n",
      "Result\n",
      "0    0.537453\n",
      "1    0.462547\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "class_distribution = data['Result'].value_counts(normalize=True)\n",
    "print(\"Class Distribution:\")\n",
    "print(class_distribution)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "1    279\n",
       "0    255\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender        0\n",
       "Hemoglobin    0\n",
       "MCH           0\n",
       "MCHC          0\n",
       "MCV           0\n",
       "Result        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
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       "      <td>88</td>\n",
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       "      <th>1</th>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "               Hemoglobin  MCH  MCHC  MCV\n",
       "Gender Result                            \n",
       "0      0              167  167   167  167\n",
       "       1               88   88    88   88\n",
       "1      0              120  120   120  120\n",
       "       1              159  159   159  159"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby([\"Gender\",\"Result\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>71.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>21.2</td>\n",
       "      <td>28.4</td>\n",
       "      <td>98.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>1</td>\n",
       "      <td>15.1</td>\n",
       "      <td>21.3</td>\n",
       "      <td>32.4</td>\n",
       "      <td>100.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>19.5</td>\n",
       "      <td>32.2</td>\n",
       "      <td>72.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>20.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>97.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>82.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>534 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Hemoglobin   MCH  MCHC    MCV  Result\n",
       "0          1        14.9  22.7  29.1   83.7       0\n",
       "1          0        15.9  25.4  28.3   72.0       0\n",
       "2          0         9.0  21.5  29.6   71.2       1\n",
       "3          0        14.9  16.0  31.4   87.5       0\n",
       "4          1        14.7  22.0  28.2   99.5       0\n",
       "...      ...         ...   ...   ...    ...     ...\n",
       "946        0        11.8  21.2  28.4   98.1       1\n",
       "1156       1        15.1  21.3  32.4  100.6       0\n",
       "1160       1        14.8  19.5  32.2   72.1       0\n",
       "1376       1        13.2  20.4  28.0   97.4       1\n",
       "1396       1        13.0  26.0  31.4   82.8       1\n",
       "\n",
       "[534 rows x 6 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data[\"Result\"]\n",
    "x = data.drop([\"Result\",\"MCH\",\"MCHC\"],axis=1)\n",
    "# x_dropped = data.drop([\"MCH\",\"MCHC\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(xtrain)\n",
    "X_test = scaler.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(427, 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models to be evaluated\n",
    "models = [\n",
    "    ('RandomForest', RandomForestClassifier()),\n",
    "    ('GradientBoosting', GradientBoostingClassifier()),\n",
    "    ('SVM', SVC()),\n",
    "    (\"Logistic Regression\",LogisticRegression(class_weight='balanced'))\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Pipeline for preprocessing and model training\n",
    "results = {}\n",
    "roc_auc_scores = {}\n",
    "for name, model in models:\n",
    "    pipe = Pipeline(steps=[('classifier', model)])\n",
    "    # Define hyperparameters grid for GridSearchCV\n",
    "    param_grid = {}\n",
    "    if name == 'RandomForest':\n",
    "        param_grid = {\n",
    "            'classifier__n_estimators': [5,10,20,50, 100, 200],\n",
    "            'classifier__max_depth': [None, 5, 10,20,30],\n",
    "            'classifier__min_samples_split': [2, 5, 10],\n",
    "            'classifier__class_weight': ['balanced']\n",
    "        }\n",
    "    elif name == 'GradientBoosting':\n",
    "        param_grid = {\n",
    "            'classifier__n_estimators': [5,10,20,50, 100, 200],\n",
    "            'classifier__learning_rate': [0.1, 0.01,0.001],\n",
    "            'classifier__max_depth': [3, 5, 7],\n",
    "        }\n",
    "    elif name == 'SVM':\n",
    "        param_grid = {\n",
    "            'classifier__C': [0.1, 1, 10],\n",
    "            'classifier__kernel': ['linear', 'rbf'],\n",
    "            'classifier__class_weight': ['balanced']\n",
    "        }\n",
    "    \n",
    "    \n",
    "    # Perform GridSearchCV\n",
    "    grid_search = GridSearchCV(pipe, param_grid, cv=5)\n",
    "    grid_search.fit(X_train, ytrain)\n",
    "    \n",
    "    # Save results\n",
    "    results[name] = {\n",
    "        'best_params': grid_search.best_params_,\n",
    "        'best_score': grid_search.best_score_,\n",
    "        'classification_report': classification_report(ytest, grid_search.predict(X_test))\n",
    "    }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest:\n",
      "  Best Parameters: {'classifier__class_weight': 'balanced', 'classifier__max_depth': None, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 10}\n",
      "  Best CV Score: 0.9976744186046511\n",
      "GradientBoosting:\n",
      "  Best Parameters: {'classifier__learning_rate': 0.1, 'classifier__max_depth': 3, 'classifier__n_estimators': 5}\n",
      "  Best CV Score: 0.9953488372093023\n",
      "SVM:\n",
      "  Best Parameters: {'classifier__C': 10, 'classifier__class_weight': 'balanced', 'classifier__kernel': 'linear'}\n",
      "  Best CV Score: 0.9906703146374829\n",
      "Logistic Regression:\n",
      "  Best Parameters: {}\n",
      "  Best CV Score: 0.9812859097127223\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Print results\n",
    "for name, result in results.items():\n",
    "    print(f\"{name}:\")\n",
    "    print(\"  Best Parameters:\", result['best_params'])\n",
    "    print(\"  Best CV Score:\", result['best_score'])\n",
    "    print(\"classification_report\", result[\"classification_report\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=5)\n",
    "rf.fit(X_train,ytrain)\n",
    "rf_y_preds = rf.predict(X_test)\n",
    "print(accuracy_score(ytest,rf_y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,ytrain)\n",
    "lr_y_preds = rf.predict(X_test)\n",
    "print(accuracy_score(ytest,lr_y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(C= 10,kernel= 'linear')\n",
    "svm.fit(X_train,ytrain)\n",
    "svm_y_preds = rf.predict(X_test)\n",
    "print(accuracy_score(ytest,svm_y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(n_estimators=50)\n",
    "gb.fit(X_train,ytrain)\n",
    "gb_y_preds = rf.predict(X_test)\n",
    "print(accuracy_score(ytest,gb_y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7aa4ec671150>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PrecisionRecallDisplay.from_estimator(gb,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7aa4ec670a60>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PrecisionRecallDisplay.from_estimator(lr,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7aa4ec614e80>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PrecisionRecallDisplay.from_estimator(svm,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7aa4ec62ef80>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PrecisionRecallDisplay.from_estimator(rf,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/johanan/.local/lib/python3.10/site-packages/sklearn/base.py:486: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7aa4ec52ef20>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(lr,xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7aa4ea358670>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(lr,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7aa4ea2a3dc0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(rf,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7aa4ea0bba00>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(svm,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7aa4ec7b3460>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(gb,X_test,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        61\n",
      "           1       1.00      1.00      1.00        46\n",
      "\n",
      "    accuracy                           1.00       107\n",
      "   macro avg       1.00      1.00      1.00       107\n",
      "weighted avg       1.00      1.00      1.00       107\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,lr_y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/svm.pkl']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "joblib.dump(svm,\"models/svm.pkl\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}