Diabetes_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68e67cb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6      148             72             35        0  33.6   \n",
      "\n",
      "   DiabetesPedigreeFunction  Age  Outcome  \n",
      "0                     0.627   50        1  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "os.getcwd()\n",
    "fname = \"diabetes.csv\"\n",
    "patients = pd.read_csv(fname, low_memory = False)\n",
    "df = pd.DataFrame(patients)\n",
    "print(df.head(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f3508c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6912\n"
     ]
    }
   ],
   "source": [
    "print(df.size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d86235a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b6760c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c7d789f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.describe of      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  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",
      "..                        ...  ...      ...  \n",
      "763                     0.171   63        0  \n",
      "764                     0.340   27        0  \n",
      "765                     0.245   30        0  \n",
      "766                     0.349   47        1  \n",
      "767                     0.315   23        0  \n",
      "\n",
      "[768 rows x 9 columns]>\n",
      "<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": [
    "print(df.describe)\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1e0ad3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.845052\n",
       "Glucose                     120.894531\n",
       "BloodPressure                69.105469\n",
       "SkinThickness                20.536458\n",
       "Insulin                      79.799479\n",
       "BMI                          31.992578\n",
       "DiabetesPedigreeFunction      0.471876\n",
       "Age                          33.240885\n",
       "Outcome                       0.348958\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec0ce504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.369578\n",
       "Glucose                      31.972618\n",
       "BloodPressure                19.355807\n",
       "SkinThickness                15.952218\n",
       "Insulin                     115.244002\n",
       "BMI                           7.884160\n",
       "DiabetesPedigreeFunction      0.331329\n",
       "Age                          11.760232\n",
       "Outcome                       0.476951\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45069ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                  17.00\n",
       "Glucose                     199.00\n",
       "BloodPressure               122.00\n",
       "SkinThickness                99.00\n",
       "Insulin                     846.00\n",
       "BMI                          67.10\n",
       "DiabetesPedigreeFunction      2.42\n",
       "Age                          81.00\n",
       "Outcome                       1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ceb754cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                  0.000\n",
       "Glucose                      0.000\n",
       "BloodPressure                0.000\n",
       "SkinThickness                0.000\n",
       "Insulin                      0.000\n",
       "BMI                          0.000\n",
       "DiabetesPedigreeFunction     0.078\n",
       "Age                         21.000\n",
       "Outcome                      0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1220f527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   9.000000\n",
       "Glucose                     210.375000\n",
       "BloodPressure               120.000000\n",
       "SkinThickness                48.000000\n",
       "Insulin                     190.875000\n",
       "BMI                          54.900000\n",
       "DiabetesPedigreeFunction      0.939375\n",
       "Age                          61.500000\n",
       "Outcome                       1.500000\n",
       "Name: 0.75, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(0.75)\n",
    "df.quantile(0.25)*1.5\n",
    "df.quantile(0.75)*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c40e624b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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.boxplot(figsize=(12, 8))\n",
    "#Vertical Boxplot chart\n",
    "df.plot.box(vert=False)\n",
    "#Boxplot by selecting only the required columns\n",
    "df.boxplot(column=['Pregnancies','Glucose','BloodPressure','SkinThickness',\n",
    "'Insulin','BMI','DiabetesPedigreeFunction','Age', 'Outcome'],figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36b95740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0.901674\n",
       "Glucose                     0.173754\n",
       "BloodPressure              -1.843608\n",
       "SkinThickness               0.109372\n",
       "Insulin                     2.272251\n",
       "BMI                        -0.428982\n",
       "DiabetesPedigreeFunction    1.919911\n",
       "Age                         1.129597\n",
       "Outcome                     0.635017\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "750cef5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Pregnancies'}>,\n",
       "        <AxesSubplot:title={'center':'Glucose'}>,\n",
       "        <AxesSubplot:title={'center':'BloodPressure'}>],\n",
       "       [<AxesSubplot:title={'center':'SkinThickness'}>,\n",
       "        <AxesSubplot:title={'center':'Insulin'}>,\n",
       "        <AxesSubplot:title={'center':'BMI'}>],\n",
       "       [<AxesSubplot:title={'center':'DiabetesPedigreeFunction'}>,\n",
       "        <AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'Outcome'}>]], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a158f560",
   "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>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "263d107c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Insulin', ylabel='DiabetesPedigreeFunction'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot.scatter(x='Insulin', y='DiabetesPedigreeFunction',s=df['Insulin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d18721d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}