code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Pima Indian Diabetes data, predict with 70% or greater accuracy which people are likely to develop diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data\n",
    "df = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correlation matrix for each pair\n",
    "def co_relation(df,size=11):\n",
    "    corr=df.corr()\n",
    "    fig,ax=plt.subplots(figsize=(size,size))\n",
    "    ax.matshow(corr)\n",
    "    plt.xticks(range(len(corr.columns)),corr.columns,rotation=50)\n",
    "    plt.yticks(range(len(corr.columns)),corr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "co_relation(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check diabetes ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True cases: 268 (34.90%)\n",
      "False cases: 500 (65.10%)\n"
     ]
    }
   ],
   "source": [
    "num_true = len(df.loc[df['Outcome']==True])\n",
    "num_false = len(df.loc[df['Outcome']==False])\n",
    "print(\"True cases: {0} ({1:2.2f}%)\".format(num_true,(num_true/(num_true+num_false))*100))\n",
    "print(\"False cases: {0} ({1:2.2f}%)\".format(num_false,(num_false/(num_true+num_false))*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  }
 ],
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