Diabetes Prediction Project1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  #read the dataset\n",
    "import numpy as np  #numerical python\n",
    "import matplotlib.pyplot as plt  #Plot the Graph\n",
    "import seaborn as sns   #plot grapical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the dataset\n",
    "df = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>33.6</td>\n",
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       "      <td>43.1</td>\n",
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       "      <td>1</td>\n",
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      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()  #to display a output first five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <th>Outcome</th>\n",
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       "   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",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)  #if u write in function 3 than we get first 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aim : to predict weather diabetes have or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.shape #how many rows and column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()  #see the information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "<p>768 rows × 9 columns</p>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  \\\n",
       "0          False    False          False          False    False  False   \n",
       "1          False    False          False          False    False  False   \n",
       "2          False    False          False          False    False  False   \n",
       "3          False    False          False          False    False  False   \n",
       "4          False    False          False          False    False  False   \n",
       "..           ...      ...            ...            ...      ...    ...   \n",
       "763        False    False          False          False    False  False   \n",
       "764        False    False          False          False    False  False   \n",
       "765        False    False          False          False    False  False   \n",
       "766        False    False          False          False    False  False   \n",
       "767        False    False          False          False    False  False   \n",
       "\n",
       "     DiabetesPedigreeFunction    Age  Outcome  \n",
       "0                       False  False    False  \n",
       "1                       False  False    False  \n",
       "2                       False  False    False  \n",
       "3                       False  False    False  \n",
       "4                       False  False    False  \n",
       "..                        ...    ...      ...  \n",
       "763                     False  False    False  \n",
       "764                     False  False    False  \n",
       "765                     False  False    False  \n",
       "766                     False  False    False  \n",
       "767                     False  False    False  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()  #to check the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  #to check t he null values   #df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split   #data #label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,df.columns!='Outcome']   #data\n",
    "y = df.iloc[:,df.columns=='Outcome']   #outcome or Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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",
      "763           10      101             76             48      180  32.9   \n",
      "764            2      122             70             27        0  36.8   \n",
      "765            5      121             72             23      112  26.2   \n",
      "766            1      126             60              0        0  30.1   \n",
      "767            1       93             70             31        0  30.4   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  \n",
      "0                       0.627   50  \n",
      "1                       0.351   31  \n",
      "2                       0.672   32  \n",
      "3                       0.167   21  \n",
      "4                       2.288   33  \n",
      "..                        ...  ...  \n",
      "763                     0.171   63  \n",
      "764                     0.340   27  \n",
      "765                     0.245   30  \n",
      "766                     0.349   47  \n",
      "767                     0.315   23  \n",
      "\n",
      "[768 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Outcome\n",
      "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",
      "\n",
      "[768 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=0.2)  #20%test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>5</td>\n",
       "      <td>139</td>\n",
       "      <td>80</td>\n",
       "      <td>35</td>\n",
       "      <td>160</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.361</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.2</td>\n",
       "      <td>0.237</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>84</td>\n",
       "      <td>48</td>\n",
       "      <td>237</td>\n",
       "      <td>37.6</td>\n",
       "      <td>1.001</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>27.8</td>\n",
       "      <td>0.559</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>84</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0.299</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "189            5      139             80             35      160  31.6   \n",
       "350            4       92             80              0        0  42.2   \n",
       "458           10      148             84             48      237  37.6   \n",
       "597            1       89             24             19       25  27.8   \n",
       "376            0       98             82             15       84  25.2   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "189                     0.361   25  \n",
       "350                     0.237   29  \n",
       "458                     1.001   51  \n",
       "597                     0.559   21  \n",
       "376                     0.299   22  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Outcome\n",
       "189        1\n",
       "350        0\n",
       "458        1\n",
       "597        0\n",
       "376        0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-653a8bc0acff>:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  model.fit(xtrain,ytrain)   #to train the algorithm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)   #to train the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain.values.ravel())  #to remove warning & to train the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1\n",
      " 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1\n",
      " 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 0\n",
      " 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0\n",
      " 0 0 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "predict_output = model.predict(xtest)  #to test the algorithm\n",
    "print(predict_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for RF: 0.6818181818181818\n"
     ]
    }
   ],
   "source": [
    "acc=accuracy_score(predict_output,ytest)\n",
    "print('The accuracy score for RF:',acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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