classifier diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import missingno as mn\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "import pickle\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
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       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>39</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
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       "      <td>48</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>Positive</td>\n",
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       "      <th>517</th>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Positive</td>\n",
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       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>32</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0     40    Male       No        Yes                 No      Yes         No   \n",
       "1     58    Male       No         No                 No      Yes         No   \n",
       "2     41    Male      Yes         No                 No      Yes        Yes   \n",
       "3     45    Male       No         No                Yes      Yes        Yes   \n",
       "4     60    Male      Yes        Yes                Yes      Yes        Yes   \n",
       "..   ...     ...      ...        ...                ...      ...        ...   \n",
       "515   39  Female      Yes        Yes                Yes       No        Yes   \n",
       "516   48  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "517   58  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "518   32  Female       No         No                 No      Yes         No   \n",
       "519   42    Male       No         No                 No       No         No   \n",
       "\n",
       "    Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0               No              No     Yes           No             Yes   \n",
       "1               No             Yes      No           No              No   \n",
       "2               No              No     Yes           No             Yes   \n",
       "3              Yes              No     Yes           No             Yes   \n",
       "4               No             Yes     Yes          Yes             Yes   \n",
       "..             ...             ...     ...          ...             ...   \n",
       "515             No              No     Yes           No             Yes   \n",
       "516             No              No     Yes          Yes             Yes   \n",
       "517             No             Yes      No           No              No   \n",
       "518             No             Yes     Yes           No             Yes   \n",
       "519             No              No      No           No              No   \n",
       "\n",
       "    partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0                No              Yes      Yes     Yes  Positive  \n",
       "1               Yes               No      Yes      No  Positive  \n",
       "2                No              Yes      Yes      No  Positive  \n",
       "3                No               No       No      No  Positive  \n",
       "4               Yes              Yes      Yes     Yes  Positive  \n",
       "..              ...              ...      ...     ...       ...  \n",
       "515             Yes               No       No      No  Positive  \n",
       "516             Yes               No       No      No  Positive  \n",
       "517             Yes              Yes       No     Yes  Positive  \n",
       "518              No               No      Yes      No  Negative  \n",
       "519              No               No       No      No  Negative  \n",
       "\n",
       "[520 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('diabetes_data_upload.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn.matrix(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>520.0</td>\n",
       "      <td>48.028846</td>\n",
       "      <td>12.151466</td>\n",
       "      <td>16.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count       mean        std   min   25%   50%   75%   max\n",
       "Age  520.0  48.028846  12.151466  16.0  39.0  47.5  57.0  90.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
       "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
       "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
       "       'muscle stiffness', 'Alopecia', 'Obesity', 'class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert object types to categorical\n",
    "df.iloc[:,1:17] = df.iloc[:,1:17].apply(lambda x:pd.Categorical(x) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                      int64\n",
       "Gender                category\n",
       "Polyuria              category\n",
       "Polydipsia            category\n",
       "sudden weight loss    category\n",
       "weakness              category\n",
       "Polyphagia            category\n",
       "Genital thrush        category\n",
       "visual blurring       category\n",
       "Itching               category\n",
       "Irritability          category\n",
       "delayed healing       category\n",
       "partial paresis       category\n",
       "muscle stiffness      category\n",
       "Alopecia              category\n",
       "Obesity               category\n",
       "class                 category\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>39</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>58</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>32</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n",
       "0     40    Male       No        Yes                 No      Yes         No   \n",
       "1     58    Male       No         No                 No      Yes         No   \n",
       "2     41    Male      Yes         No                 No      Yes        Yes   \n",
       "3     45    Male       No         No                Yes      Yes        Yes   \n",
       "4     60    Male      Yes        Yes                Yes      Yes        Yes   \n",
       "..   ...     ...      ...        ...                ...      ...        ...   \n",
       "515   39  Female      Yes        Yes                Yes       No        Yes   \n",
       "516   48  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "517   58  Female      Yes        Yes                Yes      Yes        Yes   \n",
       "518   32  Female       No         No                 No      Yes         No   \n",
       "519   42    Male       No         No                 No       No         No   \n",
       "\n",
       "    Genital thrush visual blurring Itching Irritability delayed healing  \\\n",
       "0               No              No     Yes           No             Yes   \n",
       "1               No             Yes      No           No              No   \n",
       "2               No              No     Yes           No             Yes   \n",
       "3              Yes              No     Yes           No             Yes   \n",
       "4               No             Yes     Yes          Yes             Yes   \n",
       "..             ...             ...     ...          ...             ...   \n",
       "515             No              No     Yes           No             Yes   \n",
       "516             No              No     Yes          Yes             Yes   \n",
       "517             No             Yes      No           No              No   \n",
       "518             No             Yes     Yes           No             Yes   \n",
       "519             No              No      No           No              No   \n",
       "\n",
       "    partial paresis muscle stiffness Alopecia Obesity     class  \n",
       "0                No              Yes      Yes     Yes  Positive  \n",
       "1               Yes               No      Yes      No  Positive  \n",
       "2                No              Yes      Yes      No  Positive  \n",
       "3                No               No       No      No  Positive  \n",
       "4               Yes              Yes      Yes     Yes  Positive  \n",
       "..              ...              ...      ...     ...       ...  \n",
       "515             Yes               No       No      No  Positive  \n",
       "516             Yes               No       No      No  Positive  \n",
       "517             Yes              Yes       No     Yes  Positive  \n",
       "518              No               No      Yes      No  Negative  \n",
       "519              No               No       No      No  Negative  \n",
       "\n",
       "[520 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "<p>520 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender_Female  Gender_Male  Polyuria_No  Polyuria_Yes  \\\n",
       "0     40              0            1            1             0   \n",
       "1     58              0            1            1             0   \n",
       "2     41              0            1            0             1   \n",
       "3     45              0            1            1             0   \n",
       "4     60              0            1            0             1   \n",
       "..   ...            ...          ...          ...           ...   \n",
       "515   39              1            0            0             1   \n",
       "516   48              1            0            0             1   \n",
       "517   58              1            0            0             1   \n",
       "518   32              1            0            1             0   \n",
       "519   42              0            1            1             0   \n",
       "\n",
       "     Polydipsia_No  Polydipsia_Yes  sudden weight loss_No  \\\n",
       "0                0               1                      1   \n",
       "1                1               0                      1   \n",
       "2                1               0                      1   \n",
       "3                1               0                      0   \n",
       "4                0               1                      0   \n",
       "..             ...             ...                    ...   \n",
       "515              0               1                      0   \n",
       "516              0               1                      0   \n",
       "517              0               1                      0   \n",
       "518              1               0                      1   \n",
       "519              1               0                      1   \n",
       "\n",
       "     sudden weight loss_Yes  weakness_No  ...  partial paresis_No  \\\n",
       "0                         0            0  ...                   1   \n",
       "1                         0            0  ...                   0   \n",
       "2                         0            0  ...                   1   \n",
       "3                         1            0  ...                   1   \n",
       "4                         1            0  ...                   0   \n",
       "..                      ...          ...  ...                 ...   \n",
       "515                       1            1  ...                   0   \n",
       "516                       1            0  ...                   0   \n",
       "517                       1            0  ...                   0   \n",
       "518                       0            0  ...                   1   \n",
       "519                       0            1  ...                   1   \n",
       "\n",
       "     partial paresis_Yes  muscle stiffness_No  muscle stiffness_Yes  \\\n",
       "0                      0                    0                     1   \n",
       "1                      1                    1                     0   \n",
       "2                      0                    0                     1   \n",
       "3                      0                    1                     0   \n",
       "4                      1                    0                     1   \n",
       "..                   ...                  ...                   ...   \n",
       "515                    1                    1                     0   \n",
       "516                    1                    1                     0   \n",
       "517                    1                    0                     1   \n",
       "518                    0                    1                     0   \n",
       "519                    0                    1                     0   \n",
       "\n",
       "     Alopecia_No  Alopecia_Yes  Obesity_No  Obesity_Yes  class_Negative  \\\n",
       "0              0             1           0            1               0   \n",
       "1              0             1           1            0               0   \n",
       "2              0             1           1            0               0   \n",
       "3              1             0           1            0               0   \n",
       "4              0             1           0            1               0   \n",
       "..           ...           ...         ...          ...             ...   \n",
       "515            1             0           1            0               0   \n",
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       "517            1             0           0            1               0   \n",
       "518            0             1           1            0               1   \n",
       "519            1             0           1            0               1   \n",
       "\n",
       "     class_Positive  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  \n",
       "..              ...  \n",
       "515               1  \n",
       "516               1  \n",
       "517               1  \n",
       "518               0  \n",
       "519               0  \n",
       "\n",
       "[520 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Gender_Male','Polyuria_Yes','Polydipsia_Yes',\n",
    "              'sudden weight loss_Yes','weakness_No','Polyphagia_No',\n",
    "              'Genital thrush_No','visual blurring_No','Itching_Yes',\n",
    "              'Irritability_No','delayed healing_No','partial paresis_No',\n",
    "              'muscle stiffness_No','Alopecia_No','Obesity_No','class_Negative'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['Age','Gender_Female', 'Polyuria', 'Polydipsia', 'sudden weight loss',\n",
    "       'weakness', 'Polyphagia', 'Genital thrush', 'visual blurring',\n",
    "       'Itching', 'Irritability', 'delayed healing', 'partial paresis',\n",
    "       'muscle stiffness', 'Alopecia', 'Obesity', 'outcome']"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Polyuria</th>\n",
       "      <th>Polydipsia</th>\n",
       "      <th>sudden weight loss</th>\n",
       "      <th>weakness</th>\n",
       "      <th>Polyphagia</th>\n",
       "      <th>Genital thrush</th>\n",
       "      <th>visual blurring</th>\n",
       "      <th>Itching</th>\n",
       "      <th>Irritability</th>\n",
       "      <th>delayed healing</th>\n",
       "      <th>partial paresis</th>\n",
       "      <th>muscle stiffness</th>\n",
       "      <th>Alopecia</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>45</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>60</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>520 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender_Female  Polyuria  Polydipsia  sudden weight loss  weakness  \\\n",
       "0     40              0         1           0                   1         1   \n",
       "1     58              0         1           1                   1         1   \n",
       "2     41              0         0           1                   1         1   \n",
       "3     45              0         1           1                   0         1   \n",
       "4     60              0         0           0                   0         1   \n",
       "..   ...            ...       ...         ...                 ...       ...   \n",
       "515   39              1         0           0                   0         0   \n",
       "516   48              1         0           0                   0         1   \n",
       "517   58              1         0           0                   0         1   \n",
       "518   32              1         1           1                   1         1   \n",
       "519   42              0         1           1                   1         0   \n",
       "\n",
       "     Polyphagia  Genital thrush  visual blurring  Itching  Irritability  \\\n",
       "0             0               0                0        0             0   \n",
       "1             0               0                1        1             0   \n",
       "2             1               0                0        0             0   \n",
       "3             1               1                0        0             0   \n",
       "4             1               0                1        0             1   \n",
       "..          ...             ...              ...      ...           ...   \n",
       "515           1               0                0        0             0   \n",
       "516           1               0                0        0             1   \n",
       "517           1               0                1        1             0   \n",
       "518           0               0                1        0             0   \n",
       "519           0               0                0        1             0   \n",
       "\n",
       "     delayed healing  partial paresis  muscle stiffness  Alopecia  Obesity  \\\n",
       "0                  1                0                 1         1        1   \n",
       "1                  0                1                 0         1        0   \n",
       "2                  1                0                 1         1        0   \n",
       "3                  1                0                 0         0        0   \n",
       "4                  1                1                 1         1        1   \n",
       "..               ...              ...               ...       ...      ...   \n",
       "515                1                1                 0         0        0   \n",
       "516                1                1                 0         0        0   \n",
       "517                0                1                 1         0        1   \n",
       "518                1                0                 0         1        0   \n",
       "519                0                0                 0         0        0   \n",
       "\n",
       "     outcome  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "..       ...  \n",
       "515        1  \n",
       "516        1  \n",
       "517        1  \n",
       "518        0  \n",
       "519        0  \n",
       "\n",
       "[520 rows x 17 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    320\n",
       "0    200\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['outcome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['outcome'].value_counts().plot.barh();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['outcome']\n",
    "X = df.drop(['outcome'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.91743119 0.95566502]\n",
      "Acc score: 0.9423076923076923\n"
     ]
    }
   ],
   "source": [
    "log = LogisticRegression(max_iter = 10000,solver='liblinear')\n",
    "log_model = log.fit(X_train,y_train)\n",
    "y_pred_log = log_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,log_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_log))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.93      0.92        54\n",
      "           1       0.96      0.95      0.96       102\n",
      "\n",
      "    accuracy                           0.94       156\n",
      "   macro avg       0.93      0.94      0.94       156\n",
      "weighted avg       0.94      0.94      0.94       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_log))\n",
    "cm = confusion_matrix(y_test, y_pred_log)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8916666666666668"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(log_model, X_test, y_test, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.87037037 0.93137255]\n",
      "Acc score: 0.9102564102564102\n"
     ]
    }
   ],
   "source": [
    "nb = GaussianNB()\n",
    "nb_model = nb.fit(X_train,y_train)\n",
    "y_pred_nb = nb_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,nb_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_nb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87        54\n",
      "           1       0.93      0.93      0.93       102\n",
      "\n",
      "    accuracy                           0.91       156\n",
      "   macro avg       0.90      0.90      0.90       156\n",
      "weighted avg       0.91      0.91      0.91       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_nb))\n",
    "cm = confusion_matrix(y_test, y_pred_nb)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8920833333333335"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(nb_model, X_test, y_test, cv = 10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K-nearest neigbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.796875   0.85869565]\n",
      "Acc score: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn_model = knn.fit(X_train,y_train)\n",
    "y_pred_knn = knn_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,knn_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.94      0.80        54\n",
      "           1       0.96      0.77      0.86       102\n",
      "\n",
      "    accuracy                           0.83       156\n",
      "   macro avg       0.83      0.86      0.83       156\n",
      "weighted avg       0.87      0.83      0.84       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_knn))\n",
    "cm = confusion_matrix(y_test, y_pred_knn)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_params = {'n_neighbors':np.arange(1,50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn_cv = GridSearchCV(knn,knn_params,cv=10)\n",
    "knn_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 1}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.89655172 0.93877551]\n",
      "Acc score: 0.9230769230769231\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 1)\n",
    "knn_model = knn.fit(X_train,y_train)\n",
    "y_pred_knn = knn_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,knn_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.96      0.90        54\n",
      "           1       0.98      0.90      0.94       102\n",
      "\n",
      "    accuracy                           0.92       156\n",
      "   macro avg       0.91      0.93      0.92       156\n",
      "weighted avg       0.93      0.92      0.92       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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WI/SknqyZWU/To4YLzMx6nDoMsnmejCBJn5H0vbS+naR9im+amVmFeuiTES4C9gM+ldZXAhcW1iIzsy5Q5F+6U57hgvdFxF6SHgaIiBXp0eBmZvWlh95dsE5SE6mTLWlrcqdYMDPrPvV44SvPcMHPyDLQjJT0X2RpDn9YaKvMzLqiDsdk8+QuuEzSQ2TzdQUcExGPF94yM7NK1GC8NY88Sbu3A1YDfywti4gFRTbMzKxiPTHIkj2Ztu2Bin2BHYAngXcW2C4zs4qpDq8W5RkueFfpesrO9cWN7G5mZiXyXPh6k5Ti8L0FtMXMbNNU+cKXpCZJD0v6U1ofJmm6pPnpdWi5OvKMyf57yeoWwF7Ai/mbaWbWDYq58PUV4HFgUFqfAMyIiImSJqT1Th8JnqcnO7Bk6UM2Rnt0V1tsZlaYKvZkJW0L/AtwcUnx0cDk9H4ycEy5ejrtyaZJCAMi4j/yNcvMrIby92RHSJpZsj4pIia12+enwDfIOphtRkXEIoCIWJQeF96pzh4/0ysi1nf2GBozs3ohKrq7YGlEjNloXdKHgSUR8ZCksZvSrs56sg+Qjb/OljQVuBp4tW1jRFy3KSc2M6uq6o7JHgB8VNJRZLeuDpL0e2CxpObUi20GlpSrKM+Y7DBgGdkzvT4MfCS9mpnVlyqNyUbEtyJi24jYHjgeuD0iPgNMBcan3cYDN5Srq7Oe7Mh0Z8E83piMUPpRzMzqS/GRaSIwRdKJwAJgXLkDOguyTcAA3hxc2zjImlndKSJ3QUTcCdyZ3i8jy+OSW2dBdlFEnN3llpmZdbc67P51FmTrL/utmdnGRM/LXVBRl9jMrOZ6Uk82IpZ3Z0PMzDZVj8wna2bWYzjImpkVpAaPlsnDQdbMGoLwcIGZWaEcZM3MiuQga2ZWIAdZM7OC9NRHgpuZ9RgOsmZmxelp02rNzHoUDxeYmRWlTicj5HkygplZz1ClJyNI6ivpAUlzJD0q6fupfJik6ZLmp9eh5epykDWzhtA24yvPksMa4OCI2BMYDRwhaV9gAjAjInYGZqT1TjnImlnDUGvkWsqJzKq02jstARwNTE7lk4FjytXlIGtmjSHvUEEWY0dImlmynNy+OklNkmaTPZF2ekTcD4yKiEUA6XVkuWb5wpeZNYwK7i5YGhFjOtshIlqA0ZKGANdL2qMrbXJP1swaR5UufL2pyoiXyB6keASwWFIzQHpdUu54B1kzaxjVuvAlaevUg0XSVsChwBPAVGB82m08cEO5ujxcYGaNo3r3yTYDkyU1kXVGp0TEnyT9FZgi6URgATCuXEUOsmbWGKr4tNqImAu8p4PyZVT4kFkHWTNrCH4ygplZ0aL+oqyDrJk1DPdkrWa+8p057LP/Yl5a0Ycvf+aDAAwYtJYJP5jFyObVLFnUj4nf3YtVK7escUutzfUXj+Dmy4YTAUeesJxjv/Aivz57G+6bPojeWwbNb1vD13+ykAGDW2rd1PrgBDFvkNQiabakeZKultSvwuO3kXRNej9a0lEl2z4qqex84s3NbTduy/e+9r43lY377FPMmTmCkz95MHNmjmDcZ/+3Rq2z9p55oi83Xzacn934N35525PcP30Q//j7lux14Eom3fEEv5zxJG/ZcQ1X/rzshKPNilrzLd2pVvfJvhYRoyNiD2AtcEolB0fE8xHxibQ6GjiqZNvUiJhYvaY2hkdnD2flK73fVLbvBxZz203bAnDbTduy74Ev1KJp1oEF8/uw216r6dsvaOoF795vFffePIS9x66kKX3/3G3v1Sxd1LvzijYzDrIduxt4e0oh9gdJcyXdJ+ndAJI+mHq9syU9LGmgpO1TL3hL4GzguLT9OEmfk3SBpMGSnpG0Raqnn6SFknpL2knSNEkPSbpb0q41/Pw1M2TYGlYs6wvAimV9GTJ0bY1bZG223/V1Hrm/P68sb+L11eLB2wfx4vNvDqi3XDGM9x68skYtrENBduErz9KNajomK6kXcCQwDfg+8HBEHCPpYOB3ZL3UM4AvR8S9kgYAr7cdHxFrJX0PGBMRp6Y6P5e2vSxpDvBB4A7gI8AtEbFO0iTglIiYL+l9wEXAwe3adjJwMkDfpoGF/QzMOrLdzmv45L8t4VvH70Tf/q3ssPtrNPV6Izhcfv4omnoFBx+7ooatrD++8PWGrVJ2G8h6spcA9wMfB4iI2yUNlzQYuBc4T9JlwHUR8ZykvOe5CjiOLMgeD1yUAvX+wNUl9fRpf2BETAImAQzuM6oOf3Wb7qXlfRg6/HVWLOvL0OGv89IKX/SqJ0d8ejlHfHo5AL85p5mtm7NvGtOnDOWB2wYx8aqnyP+nsJmow7/UWo/Jjo6I0yJiLdm9xO1FGl89CdgKuK/Cr/ZTgSMlDQP2Bm4n+8wvlZx/dETstomfp0e6/55RHHrUcwAcetRz3Hf3qBq3yEq9tDTrAy15rjf33jSYsce8xIN3DGTKhaM469K/07dfHUaUGqpy0u6qqadbuO4CTgB+IGksWSqyVyTtFBGPAI9I2g/YFZhdctxKoMPv8xGxStIDwPnAn1LqslckPS1pXERcraw7++6ImFPgZ6u5b3x/Fu/aaxmDhqxl8g23cdnFu3D1797OhP96iA99ZAEvLt6Kc76zd62baSXOPml7Vq7oRVPv4NQfPsfAIS1c+J1tWbdGfOu4twOw696v8pUfPVfjltaJyJeQu7vVU5A9C/itpLnAat7IdPNVSQcBLcBjwM1kyRva3AFMSMMP53RQ71XA1cDYkrITgF9I+i5ZxvMrgYYOsv995l4dln/ntP26uSWW13l/eGqDskv/8ngNWtKD1F+MrU2QjYgBHZQtJ3u0Q/vy0zqo4hlgj5Lj3ttu+6Ulx19Du6GIiHiaLDekmTUQX/gyMytKAB4uMDMrUP3F2LqYjGBmVhVVfDLCWyXdIelxSY9K+koqHyZpuqT56XVoubocZM2sYVTrkeDAeuDr6fbOfYEvS9odmADMiIidgRlpvVMOsmbWGCp7JHjnVUUsiohZ6f1K4HHgLWQX5yen3SYDx5Sry2OyZtYQsskIuQdlR0iaWbI+Kc3y3LBeaXuyR9HcD4yKiEWQBWJJZdOgOciaWePIn2FraUSMKbdTmoZ/LfDVNDmq4iZ5uMDMGoYici256pJ6kwXYyyLiulS8WFJz2t4MLClXj4OsmTWGKo7Jpun2lwCPR8R5JZum8sZs1PHADeXq8nCBmTWIquYuOAD4LFnOlLZcKd8GJgJTJJ0ILADGlavIQdbMGkeVEnJHxD10nBkQ4JBK6nKQNbPGEN3/aJk8HGTNrHF086Nl8nCQNbPGUX8x1kHWzBqHWutvvMBB1swaQ1DJZIRu4yBrZg1B5J9o0J0cZM2scTjImpkVyEHWzKwgHpM1MyuW7y4wMytMeLjAzKwwgYOsmVmh6m+0wEHWzBqH75M1MytSHQZZPxnBzBpDBLS05lvKkPQbSUskzSspGyZpuqT56XVonmY5yJpZ44jIt5R3KXBEu7IJwIyI2BmYkdbLcpA1s8ZRpSAbEXcBy9sVHw1MTu8nA8fkaZLHZM2sMQSQ/xlfIyTNLFmfFBGTyhwzKiIWAUTEIkkj85zIQdbMGkRA5L6Ha2lEjCmyNW0cZM2sMQS5LmptgsWSmlMvthlYkucgj8maWeOo3oWvjkwFxqf344Eb8hzkIGtmjaNKQVbSFcBfgXdIek7SicBE4EOS5gMfSutlebjAzBpE9RLERMSnNrLpkErrcpA1s8YQgFMdmpkVqA6n1TrImlmDiKLvLugSB1kzawwBkf8+2W7jIGtmjSP/jK9u4yBrZo3DY7JmZgWJ8N0FZmaFck/WzKwoQbS01LoRG3CQNbPGUFmqw27jIGtmjcO3cJmZFSOAcE/WzKwgUVHS7m7jIGtmDaMeL3wp6vCWh3oj6UXg2Vq3owAjgKW1boRVpJF/Z2+LiK27erCkaWQ/nzyWRkT7p9EWwkF2MyZpZnc958iqw7+znsdPRjAzK5CDrJlZgRxkN2/lnjNv9ce/sx7GY7JmZgVyT9bMrEAOsmZmBXKQ7SEkhaRzS9bPkHRWAef5drv1v1T7HJsjSS2SZkuaJ+lqSf0qPH4bSdek96MlHVWy7aOSJlS7zVYdDrI9xxrgWEl5b7buqjcF2YjYv+DzbS5ei4jREbEHsBY4pZKDI+L5iPhEWh0NHFWybWpETKxeU62aHGR7jvVkV5a/1n6DpK0lXSvpwbQcUFI+XdIsSb+S9GxbkJb0B0kPSXpU0smpbCKwVepxXZbKVqXXq9r1ni6V9HFJTZJ+nM47V9IXC/9J9Hx3A2+XNCz9HuZKuk/SuwEkfTD9DmZLeljSQEnbp17wlsDZwHFp+3GSPifpAkmDJT0jaYtUTz9JCyX1lrSTpGnpd363pF1r+Pk3LxHhpQcswCpgEPAMMBg4AzgrbbsceH96vx3weHp/AfCt9P4IskRFI9L6sPS6FTAPGN52nvbnTa8fAyan91sCC9OxJwPfTeV9gJnADrX+edXbUvJz7AXcAHwJ+DlwZio/GJid3v8ROCC9H5CO2R6Yl8o+B1xQUvc/11PdB6X3xwEXp/czgJ3T+/cBt9f6Z7K5LE4Q04NExCuSfgecDrxWsulQYHdJbeuDJA0E3k8WHImIaZJWlBxzuqSPpfdvBXYGlnVy+puBn0nqQxaw74qI1yQdBrxbUttX2cGprqe7+jkb1FaSZqf3dwOXAPcDHweIiNslDZc0GLgXOC99m7guIp4r+d2WcxVZcL0DOB64SNIAYH/g6pJ6+lThM1kODrI9z0+BWcBvS8q2APaLiNLAizbylylpLFlg3i8iVku6E+jb2Ukj4vW03+Fkf8RXtFUHnBYRt1T8STYvr0XE6NKCjfx+IiImSrqRbNz1PkmHAq/nPM9U4BxJw4C9gduB/sBL7c9v3cNjsj1MRCwHpgAnlhTfCpzatiKp7Y/pHuCTqewwYGgqHwysSAF2V2DfkrrWSeq9kdNfCXwe+ADQFlRvAb7UdoykXST17+LH29zcBZwA//yHb2n6trJTRDwSET8iG35pP366EhjYUYURsQp4ADgf+FNEtETEK8DTksalc0nSnoV8ItuAg2zPdC5vTul2OjAmXUB5jDeuXH8fOEzSLOBIYBHZH+g0oJekucAPgPtK6poEzG278NXOrcCBwG0RsTaVXQw8BsySNA/4Ff6GlNdZpN8bMBEYn8q/mi5yzSEbFrq53XF3kA0PzZZ0XAf1XgV8Jr22OQE4MdX5KHB09T6GdcbTahtYGj9tiYj1kvYDfuGvjGbdyz2OxrYdMCXd0rMW+EKN22O22XFP1sysQB6TNTMrkIOsmVmBHGTNzArkIGtVsalZptrVdWnbDDJJF0vavZN9x0qqOIlNmuO/QbKdjZW322dVhec6S9IZlbbRGoODrFVLp1mmJDV1pdKIOCkiHutkl7FkU0bN6pKDrBWhLcvUWEl3SLoceGRjGbvSDKQLJD2WppOObKtI0p2SxqT3RyjLKDZH0gxJ25MF86+lXvQHtPGMZMMl3ZqyWv2KbDpwp9RBprKSbeemtsyQtHUqc6Yr24Dvk7WqktSLbHbZtFS0D7BHRDydAtXLEfHeNFHiXkm3Au8B3gG8CxhFNoPsN+3q3Rr4NXBgqmtYRCyX9EuyDFf/k/a7HPhJRNwjaTuyab+7AWcC90TE2ZL+hSx7WDn/ms6xFfCgpGsjYhlZLoBZEfF1Sd9LdZ9KNlvulIiYL+l9wEVk2bVsM+Yga9XSUZap/YEHIqItI9fGMnYdCFwRES3A85Ju76D+fckyfz0N/8zh0JGNZSQ7EDg2HXuj3pyRbGM2lqmslTemrP4euM6ZrmxjHGStWjrKMgXwamkRHWTsUpYMvNysGOXYBzaekYycx7ftP5b8mcoindeZrmwDHpO17rSxjF13AcenMdtm4KAOjv0r8EFJO6Rjh6Xy9hmpNpaRrDTj1ZG8kZFsYzrLVLYF0NYb/zTZMIQzXVmHHGStO20sY9f1wHzgEeAXwJ/bHxgRL5KNo16XMkm1fV3/I/CxtgtfdJ6R7MCUkewwYEGZtnaWqexV4J2SHiIbcz07lTvTlW3AuQvMzArknqyZWYEcZM3MCuQga2ZWIAdZM7MCOciamZXSHrkAAAASSURBVBXIQdbMrEAOsmZmBfr/L9uVh/6QtKYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_knn))\n",
    "cm = confusion_matrix(y_test, y_pred_knn)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### with linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.90740741 0.95098039]\n",
      "Acc score: 0.9358974358974359\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel='linear')\n",
    "svc_model_l = svc.fit(X_train,y_train)\n",
    "y_pred_svc_l = svc_model_l.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,svc_model_l.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_svc_l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.91      0.91        54\n",
      "           1       0.95      0.95      0.95       102\n",
      "\n",
      "    accuracy                           0.94       156\n",
      "   macro avg       0.93      0.93      0.93       156\n",
      "weighted avg       0.94      0.94      0.94       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_svc_l))\n",
    "cm = confusion_matrix(y_test, y_pred_svc_l)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(kernel='linear'),\n",
       "             param_grid={'C': array([1, 2, 3, 4, 5, 6, 7, 8, 9])})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_params = {'C':np.arange(1,10)}\n",
    "svc = SVC(kernel='linear')\n",
    "svc_cv_l = GridSearchCV(svc,svc_params,cv=10)\n",
    "svc_cv_l.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_cv_l.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.93577982 0.96551724]\n",
      "Acc score: 0.9551282051282052\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel='linear',C = 8)\n",
    "svc_model_l = svc.fit(X_train,y_train)\n",
    "y_pred_svc_l = svc_model_l.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,svc_model_l.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_svc_l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.94        54\n",
      "           1       0.97      0.96      0.97       102\n",
      "\n",
      "    accuracy                           0.96       156\n",
      "   macro avg       0.95      0.95      0.95       156\n",
      "weighted avg       0.96      0.96      0.96       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_svc_l))\n",
    "cm = confusion_matrix(y_test, y_pred_svc_l)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### with rbf kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.         0.79069767]\n",
      "Acc score: 0.6538461538461539\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel='rbf')  #p.s - default kernel is rbf\n",
    "svc_model_rbf = svc.fit(X_train,y_train)\n",
    "y_pred_svc_rbf = svc_model_rbf.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,svc_model_rbf.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_svc_rbf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        54\n",
      "           1       0.65      1.00      0.79       102\n",
      "\n",
      "    accuracy                           0.65       156\n",
      "   macro avg       0.33      0.50      0.40       156\n",
      "weighted avg       0.43      0.65      0.52       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_svc_rbf))\n",
    "cm = confusion_matrix(y_test, y_pred_svc_rbf)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': [0.0001, 0.001, 0.1, 1, 5, 10],\n",
       "                         'gamma': [0.0001, 0.001, 0.1, 1, 5, 10]})"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_params = {\"C\": [0.0001, 0.001, 0.1, 1, 5, 10],\n",
    "             \"gamma\": [0.0001, 0.001, 0.1, 1, 5, 10]}\n",
    "svc = SVC(kernel='rbf')\n",
    "svc_cv_rbf = GridSearchCV(svc,svc_params,cv=10)\n",
    "svc_cv_rbf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.1}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_cv_rbf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.96296296 0.98039216]\n",
      "Acc score: 0.9743589743589743\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel='rbf',C=10,gamma=0.1)\n",
    "svc_model_rbf = svc.fit(X_train,y_train)\n",
    "y_pred_svc_rbf = svc_model_rbf.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,svc_model_rbf.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_svc_rbf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96        54\n",
      "           1       0.98      0.98      0.98       102\n",
      "\n",
      "    accuracy                           0.97       156\n",
      "   macro avg       0.97      0.97      0.97       156\n",
      "weighted avg       0.97      0.97      0.97       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_svc_rbf))\n",
    "cm = confusion_matrix(y_test, y_pred_svc_rbf)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standartizating\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.54877215, -0.75429803,  0.97289468, ..., -0.80439967,\n",
       "        -0.75429803, -0.41736501],\n",
       "       [-0.97932461,  1.32573593, -1.02786048, ..., -0.80439967,\n",
       "        -0.75429803, -0.41736501],\n",
       "       [ 0.729018  , -0.75429803,  0.97289468, ...,  1.24316312,\n",
       "        -0.75429803, -0.41736501],\n",
       "       ...,\n",
       "       [-0.65392602,  1.32573593, -1.02786048, ...,  1.24316312,\n",
       "        -0.75429803, -0.41736501],\n",
       "       [ 0.729018  , -0.75429803, -1.02786048, ..., -0.80439967,\n",
       "        -0.75429803, -0.41736501],\n",
       "       [ 3.41355641,  1.32573593,  0.97289468, ...,  1.24316312,\n",
       "         1.32573593, -0.41736501]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.96363636 0.98019802]\n",
      "Acc score: 0.9743589743589743\n"
     ]
    }
   ],
   "source": [
    "mlpc = MLPClassifier()\n",
    "mlpc_model = mlpc.fit(X_train_scaled,y_train)\n",
    "y_pred_mlpc = mlpc_model.predict(X_test_scaled)\n",
    "print('f1_score: ',f1_score(y_test,mlpc_model.predict(X_test_scaled),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_mlpc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.98      0.96        54\n",
      "           1       0.99      0.97      0.98       102\n",
      "\n",
      "    accuracy                           0.97       156\n",
      "   macro avg       0.97      0.98      0.97       156\n",
      "weighted avg       0.97      0.97      0.97       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_mlpc))\n",
    "cm = confusion_matrix(y_test, y_pred_mlpc)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlpc_params = {\"alpha\": [0.1, 0.01, 0.02],\n",
    "              \"hidden_layer_sizes\": [(10,10,10),\n",
    "                                     (100,100)],\n",
    "              \"activation\": [\"relu\",\"logistic\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=MLPClassifier(),\n",
       "             param_grid={'activation': ['relu', 'logistic'],\n",
       "                         'alpha': [0.1, 0.01, 0.02],\n",
       "                         'hidden_layer_sizes': [(10, 10, 10), (100, 100)]})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc = MLPClassifier()\n",
    "mlpc_cv_model = GridSearchCV(mlpc, mlpc_params,cv = 10)\n",
    "mlpc_cv_model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'activation': 'relu', 'alpha': 0.1, 'hidden_layer_sizes': (100, 100)}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlpc_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.98148148 0.99019608]\n",
      "Acc score: 0.9871794871794872\n"
     ]
    }
   ],
   "source": [
    "mlpc = MLPClassifier(activation='relu', alpha=0.01,hidden_layer_sizes=(100, 100))\n",
    "mlpc_model = mlpc.fit(X_train_scaled,y_train)\n",
    "y_pred_mlpc = mlpc_model.predict(X_test_scaled)\n",
    "print('f1_score: ',f1_score(y_test,mlpc_model.predict(X_test_scaled),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_mlpc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98        54\n",
      "           1       0.99      0.99      0.99       102\n",
      "\n",
      "    accuracy                           0.99       156\n",
      "   macro avg       0.99      0.99      0.99       156\n",
      "weighted avg       0.99      0.99      0.99       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_mlpc))\n",
    "cm = confusion_matrix(y_test, y_pred_mlpc)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.93693694 0.96517413]\n",
      "Acc score: 0.9551282051282052\n"
     ]
    }
   ],
   "source": [
    "cart = DecisionTreeClassifier()\n",
    "cart_model = cart.fit(X_train,y_train)\n",
    "y_pred_cart = cart_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,cart_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_cart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.94        54\n",
      "           1       0.98      0.95      0.97       102\n",
      "\n",
      "    accuracy                           0.96       156\n",
      "   macro avg       0.95      0.96      0.95       156\n",
      "weighted avg       0.96      0.96      0.96       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEGCAYAAADPKub5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbwUlEQVR4nO3deZRV1Zn38e+PAizmWRcYCWroOEWJjVHUVowup047RYMd7aVpEzWtYga7Q3w7cchKQr9pbTVqIq3t0DHOc0cRAxqHtAMgAk7BNyqoREBQGYWqet4/zim9FjXcW9xT99xbv89aZ917pn2eW0U97LvP3vsoIjAzs2z0qHQAZma1zEnWzCxDTrJmZhlykjUzy5CTrJlZhnpWOoBqUNe/X/QcNrTSYVgJtlq8ttIhWIlWs2pFRIzo7PmHHdQv3lvZWNSxc+Z/9HBEHN7Za5XCSbYIPYcNZeSUcysdhpVg7FnPVDoEK9Hv4843t+T891Y28uzDo4s6tm7kouFbcq1SOMmaWU0IoImmSoexGSdZM6sJQbApimsu6EpOsmZWM1yTNTPLSBA05nCaACdZM6sZTTjJmpllIoBGJ1kzs+y4JmtmlpEANrlN1swsG0G4ucDMLDMBjfnLsU6yZlYbkhFf+eMka2Y1QjSiSgexGSdZM6sJyY0vJ1kzs0wk/WSdZM3MMtPkmqyZWTZckzUzy1AgGnP4RC0nWTOrGW4uMDPLSCA2Rl2lw9iMk6yZ1YRkMIKbC8zMMuMbX2ZmGYkQjeGarJlZZppckzUzy0Zy4yt/KS1/EZmZdYJvfJmZZazR/WTNzLLhEV9mZhlrcu8CM7NsJBPEOMmamWUiEJs8rNbMLBsReDCCmVl25MEIZmZZCVyTNTPLVB5vfOUvIjOzTghEUxS3FEPSdyW9KGmhpFsk1UsaKukRSYvS1yEdleMka2Y1IXkkeM+ilo5I2haYDIyPiN2AOuBEYAowMyLGAjPT9XY5yZpZjRCNRS5F6gn0kdQT6Au8AxwN3JjuvxE4pphCzMyqXlDSiK/hkmYXrE+LiGkflxXxtqR/BxYD64EZETFD0jYRsTQ9ZqmkrTu6kJOsmdWMEmqpKyJifFs707bWo4HtgfeBOySd3JmYnGTNrCZEqJxzFxwCvB4RywEk3Q3sC7wraWRaix0JLOuoICdZM6sJyY2vsg2rXQzsI6kvSXPBwcBsYC1wCjA1fb2vo4KcZM2sRpTvGV8R8YykO4G5QAPwPDAN6A/cLuk0kkR8QkdlOcmaWU1IbnyVb1htRFwAXNBi80cktdqiOcmaWc3I44gvJ1kzqwnNI77yxknWzGqGH6RoZpaRCNjU5CRrZpaJpLnASdbMLDMljPjqMk6y3ciYHz1PU30dSESdWPKD3Rh+92L6LVxF1IlNI+p59+QdaOrrfxZ5M2LURv758sUM2bqBaIIHfzOMe68bUemwcqXcXbjKJbO/JkkBXBoR30/XzwP6R8SFZb7O+RHxs4L1P0bEvuW8Ri1569ydaerf6+P1dTsPZMXR20GdGHbvYobMeIf3jhldwQitNY0NYtrFo3htQV/69Gvkyul/Yu7jA1i8qL7SoeVIPpsLsozoI+A4ScMzvAbA+YUrTrClWbfzYKhL/vffMKY/PVdtrHBE1pqVy3rx2oK+AKxfW8eS1+oZPnJThaPKn6b0OV8dLV0pyyTbQDIM7bstd0gaIekuSc+ly34F2x+RNFfSNZLebE7Sku6VNCedqfz0dNtUkvke50m6Od22Jn29TdKRBde8QdJXJdVJ+kV63fmSzsjwZ5AvEtte+QrbTV3AwCc3n9di4P8uZ92ugysQmJVim89sZMfd1vPK3L6VDiVXkt4FdUUtXSnrxrergPmS/m+L7ZcD/xERT0oaDTwM7EwyhG1WRPxc0uHA6QXn/GNErJTUB3hO0l0RMUXS2RExrpVr3wpMAh6U1JtkKNy3gdOADyJiL0lbAU9JmhERrxeenCby0wHqhtZG4lnyvV1oHNybutWb2PaXr7Bxm3o2jB0IwJDpb0OdWL3XsApHae2p79vIj659g1//eBTr1nRtssi7bjkYISI+lHQTyWMc1hfsOgTYRfr4BzJQ0gBgf+DY9NzpklYVnDNZ0rHp++2AscB77Vz+IeCKNJEeDjweEeslHQrsLun49LhBaVmfSrLpBL7TALb67HZRwsfOrcbBvZPXAb1Ys8cQ6t9cy4axAxnw9HL6LXyftyfvBMrfP1JL1PUMfnTtG8y6ewhPPVQb//GXW3d9JPhlJDPZXF+wrQcwISIKEy9S63/hkiaSJOYJEbFO0mNAuy3+EbEhPe4wkhrtLc3FAedExMMlf5Iqpo8aISDq69BHjfR9+QNWHrEtfV98nyGPvMPb39mF6O2aUX4F37tkCUsW1XP3NPcqaE23613QLP2KfzvJ1/T/SjfPAM4GfgEgaVxEzAOeBL4G/Fta42x+EuQgYFWaYHcC9im4xCZJvSKitbsAtwLfBMYDp6bbHga+LWlWRGyS9FfA2xGxtkwfOZfqVm9i1LRFyUpjsHqvYazbdTCfvWAeagi2/eUrAGzYvj/L/n77CkZqrdn1S2s55IRV/Pmleq5+5FUArv/5SJ6bNbDCkeVLHnsXdFWHyEtIkmqzycBVkuanMTwOnAlcBNwiaRLwB2ApsBqYDpyZHv8q8HRBWdNI2n3nRsRJLa47A7gJuD8imm+bXwuMAeamNeflFPEwtGrXMLyexed/YbPtb17UWnO25c2Lz/bnsFF7VDqMXIsQDd0pyUZE/4L375I87bF5fQXJV/iWPgAOi4gGSROAgyLio3TfEW1c5wfAD9q47iZgWIvjm0i6fX2q65eZVb9u2VxQotEks473ADYC36pwPGZWJbptm2wpImIR8MVKx2Fm1clJ1swsI92yn6yZWVfqrv1kzcwyFwENnrTbzCw7bi4wM8uI22TNzDIWTrJmZtnxjS8zs4xEuE3WzCxDotG9C8zMsuM2WTOzjHjuAjOzLEXSLps3TrJmVjPcu8DMLCPhG19mZtlyc4GZWYbcu8DMLCMRTrJmZplyFy4zswzlsU02f7fizMw6IRBNTT2KWoohabCkOyW9IullSRMkDZX0iKRF6euQjspxkjWzmhFFLkW6HJgeETsBewAvA1OAmRExFpiZrrfLSdbMakN646uYpSOSBgIHANcBRMTGiHgfOBq4MT3sRuCYjspykjWz2lF8VXa4pNkFy+ktStoBWA5cL+l5SddK6gdsExFLAdLXrTsKyTe+zKxmlNCFa0VEjG9nf09gT+CciHhG0uUU0TTQVkGtkvRL2mm+iIjJnbmgmVkWAmhqKlsXrreAtyLimXT9TpIk+66kkRGxVNJIYFlHBbVXk5295XGamXWRAMrUTzYi/iJpiaTPR8SrwMHAS+lyCjA1fb2vo7LaTLIRcWPhuqR+EbF2iyI3M8tQmfvJngPcLKk38GfgGyT3sW6XdBqwGDiho0I6bJOVNIHkDlt/YLSkPYAzIuKftiB4M7PyK2OSjYh5QGvttgeXUk4xvQsuAw4D3ksv/AJJ1wYzsxwprvtWV89vUFTvgohYIn0qsMZswjEz2wI5HFZbTJJdImlfINK2ickkIx/MzPIjIMrXu6BsimkuOBM4C9gWeBsYl66bmeWMily6Toc12YhYAZzUBbGYmW2ZHDYXdFiTlbSDpAckLZe0TNJ9knboiuDMzEpS5hliyqGY5oLfArcDI4FRwB3ALVkGZWZWsubBCMUsXaiYJKuI+O+IaEiX35DLSrmZdXfJI2g6XrpSe3MXDE3fPippCnArSXKdBPyuC2IzMytNDnsXtHfjaw5JUm2O+oyCfQH8JKugzMw6Qzn8jt3e3AXbd2UgZmZbpAI3tYpR1IgvSbsBuwD1zdsi4qasgjIzK13X39QqRjETxFwATCRJsg8CRwBPAk6yZpYvOazJFtO74HiSWWf+EhHfIHmg2FaZRmVm1hlNRS5dqJjmgvUR0SSpIX242DKS59+YmeVHGSftLqdikuxsSYOB/yTpcbAGeDbTqMzMOqGqehc0K5ic+9eSpgMDI2J+tmGZmXVCNSVZSXu2ty8i5mYTkplZ7WivJntJO/sC+HKZY8mt+rfW8/l/WVjpMKwED70zr9IhWInqRm55GVXVXBARB3VlIGZmWySoumG1ZmbVpZpqsmZm1aaqmgvMzKpODpNsMU9GkKSTJf04XR8t6UvZh2ZmVqIqfTLC1cAE4O/T9dXAVZlFZGbWCYril65UTHPB3hGxp6TnASJiVfpocDOzfKnS3gWbJNWRVrIljaDLp1gwM+tYHm98FdNccAVwD7C1pJ+STHP4s0yjMjPrjBy2yRYzd8HNkuaQTHco4JiIeDnzyMzMSlGB9tZiFDNp92hgHfBA4baIWJxlYGZmJavGJEvyZNrmByrWA9sDrwK7ZhiXmVnJlMO7RcU0F3yhcD2dneuMNg43M7MCJY/4ioi5kvbKIhgzsy1Sjc0Fkr5XsNoD2BNYnllEZmadUa03voABBe8bSNpo78omHDOzLVBtSTYdhNA/Iv65i+IxM+u8akqyknpGREN7j6ExM8sLUX29C54laX+dJ+l+4A5gbfPOiLg749jMzIqXQZts+m1+NvB2RHxF0lDgNmAM8AbwtYhY1V4ZxQyrHQq8R/JMr68Af5e+mpnlS/mH1Z4LFI5wnQLMjIixwMx0vV3t1WS3TnsWLOSTwQjNctjyYWbdXhkzk6TPAH8L/BRo7mV1NDAxfX8j8Bjwg/bKaS/J1gH9+XRybeYka2a5U0JzwXBJswvWp0XEtBbHXAb8C5/uYbVNRCwFiIilkrbu6ELtJdmlEXFxsRGbmVVc8Ul2RUSMb2unpK8AyyJijqSJWxJSe0k2f7Pfmpm1Jcrau2A/4ChJR5LM2TJQ0m+AdyWNTGuxI4FlHRXU3o2vg8sTq5lZFynTja+I+GFEfCYixgAnArMi4mTgfuCU9LBTgPs6KqvNmmxErOw4FDOz/OiCYbVTgdslnQYsBk7o6AQ/EtzMakcGSTYiHiPpRUBEvEeJ3/KdZM2sNlTg0TLFcJI1s5ogqncWLjOzquAka2aWJSdZM7MMOcmamWWkip+MYGZWHZxkzcyyU22TdpuZVRU3F5iZZcWDEczMMuYka2aWDY/4MjPLmJryl2WdZM2sNrhN1swsW24uMDPLkpOsmVl2XJM1M8uSk6yZWUbK+7TasnGSNbOa4H6yZmZZi/xlWSdZM6sZrslabtzw6BzWra2jqQkaG8S5x+1R6ZCshXuuHc5DNw8jAo44aSXHfWs5Pz3js7z1/+oBWPthHf0GNvKr379a4UhzwoMRPiGpEViQXv9l4JSIWFfC+aOAKyLieEnjgFER8WC67yhgl4iYmkHoNWXKP+zKh6t6VToMa8Ubr9Tz0M3DuOJ3f6JX7+D8r+/I3gd/wP+55s2Pj7nmolH0G9BYwSjzJ483vnpU6LrrI2JcROwGbATOLOXkiHgnIo5PV8cBRxbsu98J1qrd4kVbsfOe66jvG9T1hN0nrOGphwZ/vD8CHr9/MAcds6qCUeaPmopbulKlkmyhJ4DPSRoq6V5J8yU9LWl3AEkHSpqXLs9LGiBpjKSFknoDFwOT0v2TJJ0q6UpJgyS9IalHWk5fSUsk9ZK0o6TpkuZIekLSThX8/BURAT+9/iWuuOcFjpj0l0qHYy2M2WkDC57px4cr69iwTjw3ayDL3/nkW8fCZ/oxZEQD2+6wsYJR5kyQ/MMuZulCFW2TldQTOAKYDlwEPB8Rx0j6MnATSS31POCsiHhKUn9gQ/P5EbFR0o+B8RFxdlrmqem+DyS9ABwIPAr8HfBwRGySNA04MyIWSdobuBr4covYTgdOB6hXv8x+BpXy/RO/wMplvRk0dCM/u+Ellvy5DwufG1TpsCw1euxHfO2flvHDE3ekvl8T2++ynrqenySHR+8dwkTXYjeTxxtflarJ9pE0D5gNLAauA/YH/hsgImYBwyQNAp4CLpU0GRgcEQ0lXOc2YFL6/kTgtjRR7wvckcZwDTCy5YkRMS0ixkfE+N6q79SHzLOVy3oD8MHK3vzxkaF8fvc1FY7IWjr86yu5asafuOSe1xgwuJFtt/8IgMYGeOrBQRx41PsVjjCHosilC1W6TXZcRJwTERtJ+hK3FGn76jeBPsDTJX61vx84QtJQ4K+BWSSf+f2C64+LiJ238PNUla36NNKnX+PH7/fc/wPe+FPfCkdlLb2/IvmiueytXjz14CAmHpMk1blPDGC7z33EiFGbKhle7jQPRihm6Up56sL1OHAS8BNJE4EVEfGhpB0jYgGwQNIEYCdgXsF5q4EBrRUYEWskPQtcDvxPRDQCH0p6XdIJEXGHJAG7R8QLGX62XBkyfBM/uuoVAOp6Bo89MII5TwypcFTW0sXfHMPqVT2p6xWc/bO3GDA4+Y/xD/e5qaBVEZ60uwMXAtdLmg+sA05Jt39H0kFAI/AS8BCf/nr/KDAl/er/81bKvQ24A5hYsO0k4FeS/hXoBdwKdJsk+5cl9Zx11LhKh2EduPTe11rdft5li7s4kiqSvxxbmSQbEf1b2bYSOLqV7ee0UsQbwG4F5+3VYv8NBeffSYumiIh4HTi8xLDNLOfyeOMrTzVZM7POC8DNBWZmGcpfjnWSNbPa4eYCM7MM5bF3QR6G1ZqZbbliByIUkYclbSfpUUkvS3pR0rnp9qGSHpG0KH3tsO+jk6yZ1YRkMEIUtRShAfh+OlBpH+AsSbsAU4CZETEWmJmut8tJ1sxqR1ORSwciYmlEzE3fryaZknVbkm6mN6aH3Qgc01FZbpM1s5pRZC21tDKlMcAXgWeAbSJiKSSJWNLWHZ3vJGtmtaG0yV+GS5pdsD4tIqa1PCidUOou4DvpMP+Sw3KSNbMaUdLcBSsiYnx7B0jqRZJgb46Iu9PN70oamdZiRwLLOrqQ22TNrHaUadLudOKo64CXI+LSgl3388m8KqcA93VUlmuyZlYboqyPltkP+AeS2f+aZ/07H5gK3C7pNJK5sE/oqCAnWTOrHWW68RURT9L6HNcAB5dSlpOsmdWO/A34cpI1s9qhpvw9E9xJ1sxqQ1DUQIOu5iRrZjVBFD1ktks5yZpZ7XCSNTPLkJOsmVlG3CZrZpYt9y4wM8tMcUNmu5qTrJnVhsBJ1swsU/lrLXCSNbPa4X6yZmZZcpI1M8tIBDTmr73ASdbMaodrsmZmGXKSNTPLSADFP+OryzjJmlmNCAi3yZqZZSPwjS8zs0y5TdbMLENOsmZmWfEEMWZm2QnAUx2amWXINVkzs6x4WK2ZWXYCwv1kzcwy5BFfZmYZcpusmVlGIty7wMwsU67JmpllJYjGxkoHsRknWTOrDZ7q0MwsY+7CZWaWjQDCNVkzs4yEJ+02M8tUHm98KXLY5SFvJC0H3qx0HBkYDqyodBBWklr+nX02IkZ09mRJ00l+PsVYERGHd/ZapXCS7cYkzY6I8ZWOw4rn31n16VHpAMzMapmTrJlZhpxku7dplQ7ASubfWZVxm6yZWYZckzUzy5CTrJlZhpxkq4SkkHRJwfp5ki7M4Drnt1j/Y7mv0R1JapQ0T9JCSXdI6lvi+aMk3Zm+HyfpyIJ9R0maUu6YrTycZKvHR8BxkortbN1Zn0qyEbFvxtfrLtZHxLiI2A3YCJxZyskR8U5EHJ+ujgOOLNh3f0RMLV+oVk5OstWjgeTO8ndb7pA0QtJdkp5Ll/0Ktj8iaa6kayS92ZykJd0raY6kFyWdnm6bCvRJa1w3p9vWpK+3tag93SDpq5LqJP0ive58SWdk/pOofk8An5M0NP09zJf0tKTdASQdmP4O5kl6XtIASWPSWnBv4GJgUrp/kqRTJV0paZCkNyT1SMvpK2mJpF6SdpQ0Pf2dPyFppwp+/u4lIrxUwQKsAQYCbwCDgPOAC9N9vwX2T9+PBl5O318J/DB9fzjJREXD0/Wh6WsfYCEwrPk6La+bvh4L3Ji+7w0sSc89HfjXdPtWwGxg+0r/vPK2FPwcewL3Ad8GfglckG7/MjAvff8AsF/6vn96zhhgYbrtVODKgrI/Xk/LPih9Pwm4Nn0/Exibvt8bmFXpn0l3WTxBTBWJiA8l3QRMBtYX7DoE2EVS8/pASQOA/UmSIxExXdKqgnMmSzo2fb8dMBZ4r53LPwRcIWkrkoT9eESsl3QosLuk5q+yg9KyXu/s56xRfSTNS98/AVwHPAN8FSAiZkkaJmkQ8BRwafpt4u6IeKvgd9uR20iS66PAicDVkvoD+wJ3FJSzVRk+kxXBSbb6XAbMBa4v2NYDmBARhYkXtfGXKWkiSWKeEBHrJD0G1Ld30YjYkB53GMkf8S3NxQHnRMTDJX+S7mV9RIwr3NDG7yciYqqk35G0uz4t6RBgQ5HXuR/4uaShwF8Ds4B+wPstr29dw22yVSYiVgK3A6cVbJ4BnN28Iqn5j+lJ4GvptkOBIen2QcCqNMHuBOxTUNYmSb3auPytwDeAvwGak+rDwLebz5H0V5L6dfLjdTePAyfBx//xrUi/rewYEQsi4t9Iml9atp+uBga0VmBErAGeBS4H/iciGiPiQ+B1SSek15KkPTL5RLYZJ9nqdAmfntJtMjA+vYHyEp/cub4IOFTSXOAIYCnJH+h0oKek+cBPgKcLypoGzG++8dXCDOAA4PcRsTHddi3wEjBX0kLgGvwNqVgXkv7egKnAKen276Q3uV4gaRZ6qMV5j5I0D82TNKmVcm8DTk5fm50EnJaW+SJwdPk+hrXHw2prWNp+2hgRDZImAL/yV0azruUaR20bDdyedunZCHyrwvGYdTuuyZqZZchtsmZmGXKSNTPLkJOsmVmGnGStLLZ0lqkWZd3QPIJM0rWSdmnn2ImSSp7EJh3jv9lkO21tb3HMmhKvdaGk80qN0WqDk6yVS7uzTEmq60yhEfHNiHipnUMmkgwZNcslJ1nLQvMsUxMlPSrpt8CCtmbsSkcgXSnppXQ46dbNBUl6TNL49P3hSmYUe0HSTEljSJL5d9Na9N+o7RnJhkmakc5qdQ3JcOB2qZWZygr2XZLGMlPSiHSbZ7qyzbifrJWVpJ4ko8ump5u+BOwWEa+nieqDiNgrHSjxlKQZwBeBzwNfALYhGUH2Xy3KHQH8J3BAWtbQiFgp6dckM1z9e3rcb4H/iIgnJY0mGfa7M3AB8GREXCzpb0lmD+vIP6bX6AM8J+muiHiPZC6AuRHxfUk/Tss+m2S03JkRsUjS3sDVJLNrWTfmJGvl0tosU/sCz0ZE84xcbc3YdQBwS0Q0Au9ImtVK+fuQzPz1Onw8h0Nr2pqR7ADguPTc3+nTM5K1pa2Zypr4ZMjqb4C7PdOVtcVJ1sqltVmmANYWbqKVGbuUTAbe0agYFXEMtD0jGUWe33z8RIqfqSzS63qmK9uM22StK7U1Y9fjwIlpm+1I4KBWzv1f4EBJ26fnDk23t5yRqq0ZyQpnvDqCT2Yka0t7M5X1AJpr418naYbwTFfWKidZ60ptzdh1D7AIWAD8CvhDyxMjYjlJO+rd6UxSzV/XHwCObb7xRfszkh2Qzkh2KLC4g1jbm6lsLbCrpDkkba4Xp9s905VtxnMXmJllyDVZM7MMOcmamWXISdbMLENOsmZmGXKSNTPLkJOsmVmGnGTNzDL0/wHh26v/PVrRegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_cart))\n",
    "cm = confusion_matrix(y_test, y_pred_cart)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart_params = {\"max_depth\": range(1,10),\n",
    "            \"min_samples_split\" : list(range(2,20)) }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart = DecisionTreeClassifier()\n",
    "cart_cv = GridSearchCV(cart, cart_params, cv = 10)\n",
    "cart_cv_model = cart_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 8, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.90909091 0.95049505]\n",
      "Acc score: 0.9358974358974359\n"
     ]
    }
   ],
   "source": [
    "cart = DecisionTreeClassifier(max_depth=7,min_samples_split=2)\n",
    "cart_model = cart.fit(X_train,y_train)\n",
    "y_pred_cart = cart_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,cart_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_cart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.93      0.91        54\n",
      "           1       0.96      0.94      0.95       102\n",
      "\n",
      "    accuracy                           0.94       156\n",
      "   macro avg       0.93      0.93      0.93       156\n",
      "weighted avg       0.94      0.94      0.94       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_cart))\n",
    "cm = confusion_matrix(y_test, y_pred_cart)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score:  [0.99082569 0.99507389]\n",
      "Acc score: 0.9935897435897436\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf_model = rf.fit(X_train,y_train)\n",
    "y_pred_rf = rf_model.predict(X_test)\n",
    "print('f1_score: ',f1_score(y_test,rf_model.predict(X_test),average=None))\n",
    "print('Acc score:',accuracy_score(y_test,y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99        54\n",
      "           1       1.00      0.99      1.00       102\n",
      "\n",
      "    accuracy                           0.99       156\n",
      "   macro avg       0.99      1.00      0.99       156\n",
      "weighted avg       0.99      0.99      0.99       156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_rf))\n",
    "cm = confusion_matrix(y_test, y_pred_rf)\n",
    "ConfusionMatrixDisplay(cm,display_labels=['Negative','Positive']).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score:Logistic regression - 0.9423076923076923\n",
      "accuracy_score:Neural networks - 0.9871794871794872\n",
      "accuracy_score:Naive Bayes - 0.9102564102564102\n",
      "accuracy_score:K-nearest neighbors - 0.9230769230769231\n",
      "accuracy_score:Support vector machines(rbf kernel) - 0.9743589743589743\n",
      "accuracy_score:Support vector machines(linear kernel) - 0.9551282051282052\n",
      "accuracy_score:CART - 0.9358974358974359\n",
      "accuracy_score:Random forest - 0.9935897435897436\n"
     ]
    }
   ],
   "source": [
    "print('accuracy_score:Logistic regression -',accuracy_score(y_test,y_pred_log)),\n",
    "print('accuracy_score:Neural networks -',accuracy_score(y_test,y_pred_mlpc)),\n",
    "print('accuracy_score:Naive Bayes -',accuracy_score(y_test,y_pred_nb)),\n",
    "print('accuracy_score:K-nearest neighbors -',accuracy_score(y_test,y_pred_knn)),\n",
    "print('accuracy_score:Support vector machines(rbf kernel) -',accuracy_score(y_test,y_pred_svc_rbf)),\n",
    "print('accuracy_score:Support vector machines(linear kernel) -',accuracy_score(y_test,y_pred_svc_l)),\n",
    "print('accuracy_score:CART -',accuracy_score(y_test,y_pred_cart)),\n",
    "print('accuracy_score:Random forest -',accuracy_score(y_test,y_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Acc = pd.DataFrame()\n",
    "Acc['Algoritms'] = pd.Series(['Logistic regression',\n",
    "                                   'Neural networks',\n",
    "                                   'Naive Bayes',\n",
    "                                   'K-nearest neighbors',\n",
    "                                   'Support vector machines(rbf kernel)',\n",
    "                                   'Support vector machines(linear kernel)',\n",
    "                                   'CART',\n",
    "                                   'Random forest'])\n",
    "Acc['Accuracy'] = pd.DataFrame([accuracy_score(y_test,y_pred_log),\n",
    "                     accuracy_score(y_test,y_pred_mlpc),\n",
    "                     accuracy_score(y_test,y_pred_nb),\n",
    "                     accuracy_score(y_test,y_pred_knn),\n",
    "                     accuracy_score(y_test,y_pred_svc_rbf),\n",
    "                     accuracy_score(y_test,y_pred_svc_l),\n",
    "                     accuracy_score(y_test,y_pred_cart),\n",
    "                     accuracy_score(y_test,y_pred_rf)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style=\"whitegrid\")\n",
    "sns.barplot(y = 'Algoritms',x = 'Accuracy',data=Acc);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('diabets_class.pkl', 'wb') as f:\n",
    "    pickle.dump(rf_model, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('diabets_class.pkl','rb') as f:\n",
    "    loaded_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       1, 0], dtype=uint8)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(X_test)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}