BOOST CLASSIFIERS ON DIEBETICS BY MS.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05850b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# boosting algorithms \n",
    "# Ada boost\n",
    "# gradient boosting\n",
    "# XGboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e7bfb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 3,
   "id": "e7976bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\dell\\Desktop\\diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f07e1ca4",
   "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",
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       "      <td>168</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              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": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b4e8468",
   "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",
       "   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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89b09b3b",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ade1aa69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   int64\n",
       "Glucose                       int64\n",
       "BloodPressure                 int64\n",
       "SkinThickness                 int64\n",
       "Insulin                       int64\n",
       "BMI                         float64\n",
       "DiabetesPedigreeFunction    float64\n",
       "Age                           int64\n",
       "Outcome                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01b3f765",
   "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",
       "   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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7cf66e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling \n",
    "x = df.drop(['Glucose','BloodPressure','SkinThickness','Insulin','DiabetesPedigreeFunction'],axis=1)\n",
    "y = df[['Glucose','BloodPressure','SkinThickness','Insulin','DiabetesPedigreeFunction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb79c680",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Glucose  BloodPressure  SkinThickness  Insulin  DiabetesPedigreeFunction\n",
       "0      148             72             35        0                     0.627\n",
       "1       85             66             29        0                     0.351\n",
       "2      183             64              0        0                     0.672\n",
       "3       89             66             23       94                     0.167\n",
       "4      137             40             35      168                     2.288"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b961a534",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "minmax = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3cffa564",
   "metadata": {},
   "outputs": [],
   "source": [
    "newx = minmax.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7892c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.DataFrame(newx,columns=['Glucose','BloodPressure','SkinThickness','Insulin','DiabetesPedigreeFunction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1dda3d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.743719</td>\n",
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       "      <td>0.234415</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.116567</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
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       "      <td>0.253629</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.038002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.943638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Glucose  BloodPressure  SkinThickness   Insulin  DiabetesPedigreeFunction\n",
       "0  0.743719       0.590164       0.353535  0.000000                  0.234415\n",
       "1  0.427136       0.540984       0.292929  0.000000                  0.116567\n",
       "2  0.919598       0.524590       0.000000  0.000000                  0.253629\n",
       "3  0.447236       0.540984       0.232323  0.111111                  0.038002\n",
       "4  0.688442       0.327869       0.353535  0.198582                  0.943638"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f3b1fb91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([y,x],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "271fdee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Age</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>33.6</td>\n",
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       "      <td>23.3</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>21</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.038002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>33</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.943638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Outcome  Pregnancies   BMI  Age   Glucose  BloodPressure  SkinThickness  \\\n",
       "0        1            6  33.6   50  0.743719       0.590164       0.353535   \n",
       "1        0            1  26.6   31  0.427136       0.540984       0.292929   \n",
       "2        1            8  23.3   32  0.919598       0.524590       0.000000   \n",
       "3        0            1  28.1   21  0.447236       0.540984       0.232323   \n",
       "4        1            0  43.1   33  0.688442       0.327869       0.353535   \n",
       "\n",
       "    Insulin  DiabetesPedigreeFunction  \n",
       "0  0.000000                  0.234415  \n",
       "1  0.000000                  0.116567  \n",
       "2  0.000000                  0.253629  \n",
       "3  0.111111                  0.038002  \n",
       "4  0.198582                  0.943638  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e5e3b101",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('Outcome',axis=1)\n",
    "x1 = pd.DataFrame(x)\n",
    "y = df['Outcome']\n",
    "y1 = pd.DataFrame(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b82a6a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x1_train,x1_test,y1_train,y1_test = train_test_split(x1,y1,test_size=0.25,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b23fa75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ada BOOSTER WITH CLASSIFICATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ebad11a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1184: 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",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_model = AdaBoostClassifier()\n",
    "ada_model.fit(x1_train,y1_train)\n",
    "y1_pred= ada_model.predict(x1_test)\n",
    "y1_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "53d9ba57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the ac score is: 0.7239583333333334\n"
     ]
    }
   ],
   "source": [
    "# validating \n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"the ac score is:\", accuracy_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5adc10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADIENT BOOSTING CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3349d666",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:424: 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",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "grd_model = GradientBoostingClassifier()\n",
    "grd_model.fit(x1_train,y1_train)\n",
    "y1_predga = grd_model.predict(x1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c5efa17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the acc score is: 0.7447916666666666\n"
     ]
    }
   ],
   "source": [
    "print(\"the acc score is:\" , accuracy_score(y1_test,y1_predga))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51148472",
   "metadata": {},
   "outputs": [],
   "source": [
    "#XGBOOST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2ad9c990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Obtaining dependency information for xgboost from https://files.pythonhosted.org/packages/24/ec/ad387100fa3cc2b9b81af0829b5ecfe75ec5bb19dd7c19d4fea06fb81802/xgboost-2.0.3-py3-none-win_amd64.whl.metadata\n",
      "  Downloading xgboost-2.0.3-py3-none-win_amd64.whl.metadata (2.0 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\dell\\anaconda3\\lib\\site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\dell\\anaconda3\\lib\\site-packages (from xgboost) (1.11.1)\n",
      "Downloading xgboost-2.0.3-py3-none-win_amd64.whl (99.8 MB)\n",
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      "   ---------------------------------------- 99.8/99.8 MB 15.2 MB/s eta 0:00:00\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ae8cca29",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from xgboost import XGBClassifier\n",
    "XG_model = XGBClassifier()\n",
    "XG_model.fit(x1_train,y1_train)\n",
    "y1_predxg = XG_model.predict(x1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d4dec47a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the acc score is: 0.7135416666666666\n"
     ]
    }
   ],
   "source": [
    "print(\"the acc score is:\", accuracy_score(y1_test,y1_predxg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "491e93c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Age</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>50</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.234415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>31</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.116567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>23.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>21</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.038002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>33</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.943638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Outcome  Pregnancies   BMI  Age   Glucose  BloodPressure  SkinThickness  \\\n",
       "0        1            6  33.6   50  0.743719       0.590164       0.353535   \n",
       "1        0            1  26.6   31  0.427136       0.540984       0.292929   \n",
       "2        1            8  23.3   32  0.919598       0.524590       0.000000   \n",
       "3        0            1  28.1   21  0.447236       0.540984       0.232323   \n",
       "4        1            0  43.1   33  0.688442       0.327869       0.353535   \n",
       "\n",
       "    Insulin  DiabetesPedigreeFunction  \n",
       "0  0.000000                  0.234415  \n",
       "1  0.000000                  0.116567  \n",
       "2  0.000000                  0.253629  \n",
       "3  0.111111                  0.038002  \n",
       "4  0.198582                  0.943638  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3e151963",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_model.predict([[6,\t33.6,\t50,\t0.743719,\t0.590164,\t0.353535,\t0.000000,\t0.234415]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "70446e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XG_model.predict([[1,\t26.6,\t31,\t0.427136,\t0.540984,\t0.292929,\t0.000000,\t0.116567]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b74eaf2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grd_model.predict([[8,\t23.3,\t32,\t0.919598,\t0.524590,\t0.000000,\t0.000000,\t0.253629]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c969a19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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