Diabetes Project with Taha.ipynb

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  {
   "cell_type": "markdown",
   "id": "9135da8f",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f431ac5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "import pandas_profiling\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d0ba70e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Diabetes.csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fa8a0bd",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <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",
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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",
       "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]"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09918088",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e63509a0",
   "metadata": {},
   "outputs": [
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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",
       "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]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df=pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bde055ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['SkinThickness'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa3ce76b",
   "metadata": {},
   "outputs": [
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       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  Insulin   BMI  \\\n",
       "0              6      148             72        0  33.6   \n",
       "1              1       85             66        0  26.6   \n",
       "2              8      183             64        0  23.3   \n",
       "3              1       89             66       94  28.1   \n",
       "4              0      137             40      168  43.1   \n",
       "..           ...      ...            ...      ...   ...   \n",
       "763           10      101             76      180  32.9   \n",
       "764            2      122             70        0  36.8   \n",
       "765            5      121             72      112  26.2   \n",
       "766            1      126             60        0  30.1   \n",
       "767            1       93             70        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 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "769cf4e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "476ec2f9",
   "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>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>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure     Insulin         BMI  \\\n",
       "count   768.000000  768.000000     768.000000  768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469   79.799479   31.992578   \n",
       "std       3.369578   31.972618      19.355807  115.244002    7.884160   \n",
       "min       0.000000    0.000000       0.000000    0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000    0.000000   27.300000   \n",
       "50%       3.000000  117.000000      72.000000   30.500000   32.000000   \n",
       "75%       6.000000  140.250000      80.000000  127.250000   36.600000   \n",
       "max      17.000000  199.000000     122.000000  846.000000   67.100000   \n",
       "\n",
       "       DiabetesPedigreeFunction         Age     Outcome  \n",
       "count                768.000000  768.000000  768.000000  \n",
       "mean                   0.471876   33.240885    0.348958  \n",
       "std                    0.331329   11.760232    0.476951  \n",
       "min                    0.078000   21.000000    0.000000  \n",
       "25%                    0.243750   24.000000    0.000000  \n",
       "50%                    0.372500   29.000000    0.000000  \n",
       "75%                    0.626250   41.000000    1.000000  \n",
       "max                    2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "552f6499",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5bda0b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(df,hue='Outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd4aee95",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7e90e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(['Outcome'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34e31a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e32b077d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y,\n",
    "                                                test_size=0.3, random_state=132)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e65ed5e",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "018fea4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a220737b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "207f3f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddb6dfc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = r2_score(ytest,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7c0132d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24560609298458502"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56ceacb",
   "metadata": {},
   "source": [
    "# Naive Bayes Guassian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "036cd34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0880bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef5f55f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes score:  0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "print(\"Naive Bayes score: \",gaussian.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786d1150",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e4f9e041",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=DecisionTreeClassifier(criterion='gini',max_depth=40,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "56f2e8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=40, random_state=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f7e2d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_model_pred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e6ec9613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train_model=model.predict(xtrain)\n",
    "y_pred_train_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5e46c086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model accuracy score with criterion gini Index : 0.7056\n"
     ]
    }
   ],
   "source": [
    "print('model accuracy score with criterion gini Index : {0:0.4f}'.format(accuracy_score(ytest,y_model_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a353aab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.7056\n"
     ]
    }
   ],
   "source": [
    "print('Test set score: {:.4f}'.format(model.score(xtest, ytest)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab23b5e",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15f2b0ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier(n_estimators=100,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3cc6a12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9654c480",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(xtest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "65d01c37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with 10 decision trees :0.7446\n"
     ]
    }
   ],
   "source": [
    "print('Model accuracy score with 10 decision trees :{0:0.4f}'.format(accuracy_score(ytest,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d4fd19c",
   "metadata": {},
   "source": [
    "# PCA + Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c0151a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a811bc52",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain = pd.DataFrame(scaler.fit_transform(xtrain), columns = x.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d65f57ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtest = pd.DataFrame(scaler.transform(xtest), columns = x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "16f44bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8cff25fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(xtrain, ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f50e2f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e918899f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy score with all the features:\n",
      " 0.7489\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression accuracy score with all the features:\\n {0:0.4f}'. format(accuracy_score(ytest, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bc4925",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0dde1d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9b5d2cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5352de98",
   "metadata": {},
   "outputs": [],
   "source": [
    "predknn = knn.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e247839b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[117  33]\n",
      " [ 41  40]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(ytest,predknn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be61d457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.78      0.76       150\n",
      "           1       0.55      0.49      0.52        81\n",
      "\n",
      "    accuracy                           0.68       231\n",
      "   macro avg       0.64      0.64      0.64       231\n",
      "weighted avg       0.67      0.68      0.68       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,predknn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "edc4939d",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,40):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(xtrain,ytrain)\n",
    "    pred_i = knn.predict(xtest)\n",
    "    error_rate.append(np.mean(pred_i != ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "39f4fed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4cf95c43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=36\n",
      "\n",
      "\n",
      "[[142   8]\n",
      " [ 51  30]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.95      0.83       150\n",
      "           1       0.79      0.37      0.50        81\n",
      "\n",
      "    accuracy                           0.74       231\n",
      "   macro avg       0.76      0.66      0.67       231\n",
      "weighted avg       0.75      0.74      0.71       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# NOW WITH K=36\n",
    "knn = KNeighborsClassifier(n_neighbors=36)\n",
    "\n",
    "knn.fit(xtrain,ytrain)\n",
    "predknn2 = knn.predict(xtest)\n",
    "\n",
    "print('WITH K=36')\n",
    "print('\\n')\n",
    "print(confusion_matrix(ytest,predknn2))\n",
    "print('\\n')\n",
    "print(classification_report(ytest,predknn2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ccf5a0",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0cc404f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d41a723529f94e8ca28c01b91b57cb99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(HTML(value='<div id=\"overview-content\" class=\"row variable spacing\">\\n    <div class=\"row\">\\n   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Report generated with <a href=\"https://github.com/pandas-profiling/pandas-profiling\">pandas-profiling</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = data.profile_report(title='Diabetes Profiling Report')\n",
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f07e8070",
   "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>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</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>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</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>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  Insulin   BMI  \\\n",
       "0            6      148             72        0  33.6   \n",
       "1            1       85             66        0  26.6   \n",
       "2            8      183             64        0  23.3   \n",
       "3            1       89             66       94  28.1   \n",
       "4            0      137             40      168  43.1   \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  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:, :7]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "43b78977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"Outcome\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6e386bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(576, 7)\n",
      "(576,)\n",
      "(192, 7)\n",
      "(192,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=0)\n",
    "    \n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "45ecc145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize Features\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eb44ee1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear\n",
      "0.765625\n",
      "poly\n",
      "0.78125\n",
      "rbf\n",
      "0.8211805555555556\n",
      "sigmoid\n",
      "0.6527777777777778\n"
     ]
    }
   ],
   "source": [
    "for k in ('linear', 'poly', 'rbf', 'sigmoid'):\n",
    "    model = svm.SVC(kernel=k)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_train)\n",
    "    print(k)\n",
    "    print(accuracy_score(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e62a35c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the best model\n",
    "model = svm.SVC(kernel='rbf')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cef54650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.82986389  0.91063462  0.0166341  -0.33430854  0.97958623  3.10583154\n",
      "  -0.71570071]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient = np.array([[ 1., 150., 70., 45., 40., 1.5, 25]])\n",
    "    \n",
    "# Normalize the data with the values used in the training set\n",
    "patient = scaler.transform(patient)\n",
    "print(patient)    \n",
    "predbest=model.predict(patient)\n",
    "predbest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e3a3e5da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.78125\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on Testing Set\n",
    "X_test = scaler.transform(X_test)\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a47f1521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.91      0.85       130\n",
      "           1       0.73      0.52      0.60        62\n",
      "\n",
      "    accuracy                           0.78       192\n",
      "   macro avg       0.76      0.71      0.73       192\n",
      "weighted avg       0.77      0.78      0.77       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18201cc9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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