naivebays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import math \n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CsvLoader(filename):\n",
    "    lines=csv.reader(open(r'C:\\Users\\RAHUL K R\\Desktop\\diabetes.csv'))\n",
    "    dataset=list(lines)\n",
    "    for i in range (len(dataset)):\n",
    "        dataset[i]=[float(x)for x in dataset[i]]\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('diabetes.csv')\n",
    "data.head(10)\n",
    "#data[\"Outcome\"].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.iloc[:,:-1].corr(method='pearson')\n",
    "cmap= sns.diverging_palette(250,354,80,60,center='dark',as_cmap=True)\n",
    "sns.heatmap(corr,vmax=1,vmin=-0.5,cmap=cmap,square=True,linewidths=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Glucose</th>\n",
       "      <th>Age</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>50</td>\n",
       "      <td>33.6</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>31</td>\n",
       "      <td>26.6</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183</td>\n",
       "      <td>32</td>\n",
       "      <td>23.3</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>21</td>\n",
       "      <td>28.1</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>33</td>\n",
       "      <td>43.1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>116</td>\n",
       "      <td>30</td>\n",
       "      <td>25.6</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78</td>\n",
       "      <td>26</td>\n",
       "      <td>31.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>115</td>\n",
       "      <td>29</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>197</td>\n",
       "      <td>53</td>\n",
       "      <td>30.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>125</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Glucose  Age   BMI  BloodPressure  Outcome\n",
       "0      148   50  33.6             72        1\n",
       "1       85   31  26.6             66        0\n",
       "2      183   32  23.3             64        1\n",
       "3       89   21  28.1             66        0\n",
       "4      137   33  43.1             40        1\n",
       "5      116   30  25.6             74        0\n",
       "6       78   26  31.0             50        1\n",
       "7      115   29  35.3              0        0\n",
       "8      197   53  30.5             70        1\n",
       "9      125   54   0.0             96        1"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data[[\"Glucose\",\"Age\",\"BMI\",\"BloodPressure\",\"Outcome\"]]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='BloodPressure', ylabel='Count'>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(1,4,figsize=(18,6),sharey=True)\n",
    "sns.histplot(data,ax=axes[0],x=\"Glucose\",kde=True,color='r')\n",
    "sns.histplot(data,ax=axes[1],x=\"Age\",kde=True,color='b')\n",
    "sns.histplot(data,ax=axes[2],x=\"BMI\",kde=True,color='y')\n",
    "sns.histplot(data,ax=axes[3],x=\"BloodPressure\",kde=True,color='g')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "eliminating the age to fit the normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data=data[[\"Glucose\",\"BMI\",\"BloodPressure\",\"Outcome\"]]\n",
    "data.head(10)\n",
    "def prior_f(data,y):\n",
    "    classes=sorted(list(data[y].unique()))#unique avoids repeation\n",
    "    prior=[]\n",
    "    for i in classes:\n",
    "        prior.append(len(data[data[y]==i])/len(data))\n",
    "    return prior\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      0\n",
      "2      1\n",
      "3      0\n",
      "4      1\n",
      "      ..\n",
      "763    0\n",
      "764    0\n",
      "765    0\n",
      "766    1\n",
      "767    0\n",
      "Name: Outcome, Length: 768, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def calc_gaussian(data,feat_name,feat_value,y,label):\n",
    "    feat=list(data.columns)\n",
    "    data=data[data[y]==label]\n",
    "    mean,std=data[feat_name].mean(),data[feat_name].std()\n",
    "    p_xgiven_y=(1/(np.sqrt(2*np.pi)*std))*np.exp(-((feat_value-mean)**2/(2*std**2)))\n",
    "    return p_xgiven_y\n",
    "\n",
    "target=data[\"Outcome\"]\n",
    "data.drop([\"Outcome\",\"Age\"],axis='columns',inplace=True)\n",
    "print(target)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another Appraoch From Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "693    1\n",
       "599    0\n",
       "50     0\n",
       "131    1\n",
       "767    0\n",
       "      ..\n",
       "329    0\n",
       "362    0\n",
       "232    0\n",
       "450    0\n",
       "595    1\n",
       "Name: Outcome, Length: 537, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(data,target,test_size=0.3)#20\n",
    "len(X_train)\n",
    "len(X_test)\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7748917748917749"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model=GaussianNB()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193    1\n",
       "92     0\n",
       "553    0\n",
       "220    1\n",
       "146    0\n",
       "521    0\n",
       "384    0\n",
       "101    0\n",
       "134    0\n",
       "756    0\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12637167, 0.87362833],\n",
       "       [0.74716231, 0.25283769],\n",
       "       [0.92838433, 0.07161567],\n",
       "       [0.13037777, 0.86962223],\n",
       "       [0.95281773, 0.04718227],\n",
       "       [0.673227  , 0.326773  ],\n",
       "       [0.82226809, 0.17773191],\n",
       "       [0.55923636, 0.44076364],\n",
       "       [0.94949974, 0.05050026],\n",
       "       [0.51677252, 0.48322748]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test[:10])#first column is for 0 "
   ]
  }
 ],
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