Diabetes.ipynb

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      "source": [
        "# **Diabetes Prediction Using Machine Learning**"
      ],
      "metadata": {
        "id": "EjNXsmEK5_PG"
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      "source": [
        "**We will be predicting that whether the patient has diabetes or not on the basis of the features we will provide to our machine learning model, and for that, we will be using the famous Pima Indians Diabetes Database.**"
      ],
      "metadata": {
        "id": "4VQkkaqn6HB7"
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      "source": [
        "**Would be performing:**            \n",
        "1. **Data analysis**: Here one will get to know about how the data analysis part is done in a data science life cycle.\n",
        "2. **Exploratory data analysis**: EDA is one of the most important steps in the data science project life cycle and here one will need to know that how to make inferences from the visualizations and data analysis\n",
        "3. **Model building:** Here we will be using 4 ML models and then we will choose the best performing model.\n",
        "4. **Saving model:** Saving the best model using pickle to make the prediction from real data.                                                      "
      ],
      "metadata": {
        "id": "vfNNTYEf6fPS"
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      "cell_type": "markdown",
      "source": [
        "### **Importing Libraries and Dataset:**"
      ],
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      "source": [
        "import numpy as np #for linear algebra  \n",
        "import pandas as pd #for data processing, CSV file I/O (e.g. pd.read_csv)  \n",
        "\n",
        "import matplotlib.pyplot as plt #to plot data visualization charts  \n",
        "import seaborn as sns #for data visualization  \n",
        "sns.set()\n",
        "\n",
        "from collections import Counter  \n",
        "import os  "
      ],
      "metadata": {
        "id": "Yn80NKTa7FQI"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "#Importing Libraries-\n",
        "\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "import missingno as msno\n",
        "\n",
        "from pandas.plotting import scatter_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score,  classification_report\n",
        "\n",
        "from sklearn.preprocessing import QuantileTransformer  \n",
        "from sklearn.linear_model import LogisticRegression  \n",
        "from sklearn.neighbors import KNeighborsClassifier  \n",
        "from sklearn.tree import DecisionTreeClassifier  \n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier  \n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve, train_test_split  \n",
        "from sklearn.svm import SVC "
      ],
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        "id": "wIpzjTut7XFM"
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      "execution_count": 5,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "#For warnings-\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ],
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        "id": "44znoTz_7Xl8"
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      "execution_count": 4,
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      "cell_type": "code",
      "source": [
        "#Importing Dataset-\n",
        "\n",
        "df = pd.read_csv('/diabetes.csv')"
      ],
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        "id": "IN9FzFC39VjQ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **EDA:**"
      ],
      "metadata": {
        "id": "VEUvwTSJ92Wv"
      }
    },
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      "source": [
        "#First step is getting familiar with the structure of the dataset  \n",
        "df.info()  "
      ],
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            "<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"
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      "source": [
        "#Showing the top 5 rows of the dataset  \n",
        "df.head()  "
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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",
              "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",
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        "#Showing the last 5 rows of the dataset  \n",
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              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
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              "     DiabetesPedigreeFunction  Age  Outcome  \n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a3c97195-a020-4629-863f-473721f528cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a3c97195-a020-4629-863f-473721f528cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let’ see that what are columns available in our dataset.\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJhylAr7-THY",
        "outputId": "0c7d9fec-0138-4429-e8cb-ea5aecf1c9d4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To know more about the dataset with transpose – here T is for the transpose\n",
        "\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "3zK14uzQ_J-K",
        "outputId": "1ba35a3f-d8de-4687-c0db-6a6ee08e9b72"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          count        mean         std     min       25%  \\\n",
              "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
              "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
              "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
              "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
              "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
              "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
              "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
              "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
              "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
              "\n",
              "                               50%        75%     max  \n",
              "Pregnancies                 3.0000    6.00000   17.00  \n",
              "Glucose                   117.0000  140.25000  199.00  \n",
              "BloodPressure              72.0000   80.00000  122.00  \n",
              "SkinThickness              23.0000   32.00000   99.00  \n",
              "Insulin                    30.5000  127.25000  846.00  \n",
              "BMI                        32.0000   36.60000   67.10  \n",
              "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
              "Age                        29.0000   41.00000   81.00  \n",
              "Outcome                     0.0000    1.00000    1.00  "
            ],
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              "      <th>Insulin</th>\n",
              "      <td>768.0</td>\n",
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              "      <td>0.000</td>\n",
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              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>768.0</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.000</td>\n",
              "      <td>27.30000</td>\n",
              "      <td>32.0000</td>\n",
              "      <td>36.60000</td>\n",
              "      <td>67.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>768.0</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.24375</td>\n",
              "      <td>0.3725</td>\n",
              "      <td>0.62625</td>\n",
              "      <td>2.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>768.0</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>21.000</td>\n",
              "      <td>24.00000</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>41.00000</td>\n",
              "      <td>81.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>768.0</td>\n",
              "      <td>0.348958</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-0d730b82-29e7-4a4c-8d82-bf7defa177a1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let’s check that if our dataset have null values or not\n",
        "\n",
        "df.isnull().head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "zuoJ1rCL_TsZ",
        "outputId": "1c0ea4d4-5019-4e38-e8b2-59b8cb7b7cdb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "5        False    False          False          False    False  False   \n",
              "6        False    False          False          False    False  False   \n",
              "7        False    False          False          False    False  False   \n",
              "8        False    False          False          False    False  False   \n",
              "9        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",
              "5                     False  False    False  \n",
              "6                     False  False    False  \n",
              "7                     False  False    False  \n",
              "8                     False  False    False  \n",
              "9                     False  False    False  "
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9691d4b9-efb3-4d64-91e4-1ac865cd4ce5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9691d4b9-efb3-4d64-91e4-1ac865cd4ce5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-RVwFrBAMAR",
        "outputId": "44d9fcde-28df-4f9a-e800-751626eda152"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here from the above code we first checked that is there any null values from the IsNull() function then we are going to take the sum of all those missing values from the sum() function and the inference we now get is that there are no missing values but that is actually not a true story as in this particular dataset all the missing values were given the 0 as a value which is not good for the authenticity of the dataset. Hence we will first replace the 0 value with the NAN value then start the imputation process."
      ],
      "metadata": {
        "id": "-fc2oH8hBP9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_copy = df.copy(deep = True)\n",
        "df_copy[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']] = df_copy[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].replace(0,np.NaN)\n",
        "\n",
        "# Showing the Count of NANs\n",
        "print(df_copy.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4MQqfhQBKq-",
        "outputId": "1d1150c4-96b6-48a9-8c53-c0930fd9a6ec"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pregnancies                   0\n",
            "Glucose                       5\n",
            "BloodPressure                35\n",
            "SkinThickness               227\n",
            "Insulin                     374\n",
            "BMI                          11\n",
            "DiabetesPedigreeFunction      0\n",
            "Age                           0\n",
            "Outcome                       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting the data distribution plots before removing null values\n",
        "\n",
        "p = df.hist(figsize = (20,20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zIgMeV0eBnc1",
        "outputId": "ab59cd3e-e7fc-47ae-e7b0-9d245c14d878"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 9 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now we will be imputing the mean value of the column to each missing value of that particular column.\n",
        "\n",
        "df_copy['Glucose'].fillna(df_copy['Glucose'].mean(), inplace = True)\n",
        "df_copy['BloodPressure'].fillna(df_copy['BloodPressure'].mean(), inplace = True)\n",
        "df_copy['SkinThickness'].fillna(df_copy['SkinThickness'].median(), inplace = True)\n",
        "df_copy['Insulin'].fillna(df_copy['Insulin'].median(), inplace = True)\n",
        "df_copy['BMI'].fillna(df_copy['BMI'].median(), inplace = True)\n",
        "\n",
        "p = df_copy.hist(figsize = (20,20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tMWOTaH3B7Mt",
        "outputId": "bf3fbf04-ba0f-41da-c4e1-56321ae00216"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 9 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting Null Count Analysis Plot\n",
        "\n",
        "p = msno.bar(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "id": "HWKPapFBCTjT",
        "outputId": "840bc357-6858-4849-f405-765120623741"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let’s check that how well our outcome column is balanced\n",
        "\n",
        "color_wheel = {1: \"#0392cf\", 2: \"#7bc043\"}\n",
        "colors = df[\"Outcome\"].map(lambda x: color_wheel.get(x + 1))\n",
        "print(df.Outcome.value_counts())\n",
        "p=df.Outcome.value_counts().plot(kind=\"bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "6TEYpLgkCdbG",
        "outputId": "c9430ca9-07bc-4a05-e908-1e073ad79df2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    500\n",
            "1    268\n",
            "Name: Outcome, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(121), sns.distplot(df['Insulin'])\n",
        "plt.subplot(122), df['Insulin'].plot.box(figsize=(16,5))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "O4X-uXMPCjgU",
        "outputId": "2972cff9-5d5b-4b25-c830-108a51a1c19c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploring the Glucose and the Target variables together  \n",
        "\n",
        "plt.figure(figsize = (10, 8))  \n",
        "sns.violinplot(data = df, x = \"Outcome\", y = \"Glucose\",  \n",
        "               split = True, inner = \"quart\", linewidth = 2)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "hh8PODG9DWCb",
        "outputId": "32b2980e-66cf-4e00-fb2f-9bfcf4ea7aba"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Outcome', ylabel='Glucose'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploring Pregnancy and target variables together  \n",
        "  \n",
        "plt.figure(figsize = (10, 8))  \n",
        "  \n",
        "# Plotting density function graph of the pregnancies and the target variable  \n",
        "kde = sns.kdeplot(df[\"Pregnancies\"][df[\"Outcome\"] == 1], color = \"Red\", shade = True)  \n",
        "kde = sns.kdeplot(df[\"Pregnancies\"][df[\"Outcome\"] == 0], ax = kde, color = \"Blue\", shade= True)  \n",
        "\n",
        "kde.set_xlabel(\"Pregnancies\")  \n",
        "kde.set_ylabel(\"Density\")  \n",
        "kde.legend([\"Positive Result\", \"Negative Result\"])  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "wISRapb3DKkj",
        "outputId": "8a2e0977-7a7a-4a73-83f0-634f8fedb01b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f90715cbac0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation between all the features before cleaning\n",
        "\n",
        "plt.figure(figsize=(12,10))\n",
        "\n",
        "#seaborn has an easy method to showcase heatmap\n",
        "p = sns.heatmap(df.corr(), annot=True,cmap ='RdYlGn')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dp_eGfhqDCY7",
        "outputId": "18a0ee28-bb5b-4512-a6f9-eca7d50a460d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling the Data\n",
        "\n",
        "sc_X = StandardScaler()\n",
        "X =  pd.DataFrame(sc_X.fit_transform(df_copy.drop([\"Outcome\"],axis = 1),), columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', \n",
        "                                                                  'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'])\n",
        "\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kLiGy7ZfDlFa",
        "outputId": "774b2f3a-a07e-442c-eb5a-ddc18b001d94"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
              "0     0.639947  0.865108      -0.033518       0.670643 -0.181541  0.166619   \n",
              "1    -0.844885 -1.206162      -0.529859      -0.012301 -0.181541 -0.852200   \n",
              "2     1.233880  2.015813      -0.695306      -0.012301 -0.181541 -1.332500   \n",
              "3    -0.844885 -1.074652      -0.529859      -0.695245 -0.540642 -0.633881   \n",
              "4    -1.141852  0.503458      -2.680669       0.670643  0.316566  1.549303   \n",
              "\n",
              "   DiabetesPedigreeFunction       Age  \n",
              "0                  0.468492  1.425995  \n",
              "1                 -0.365061 -0.190672  \n",
              "2                  0.604397 -0.105584  \n",
              "3                 -0.920763 -1.041549  \n",
              "4                  5.484909 -0.020496  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb42809a-4831-4f9f-8fe2-1ecfc93e5b68\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.639947</td>\n",
              "      <td>0.865108</td>\n",
              "      <td>-0.033518</td>\n",
              "      <td>0.670643</td>\n",
              "      <td>-0.181541</td>\n",
              "      <td>0.166619</td>\n",
              "      <td>0.468492</td>\n",
              "      <td>1.425995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.844885</td>\n",
              "      <td>-1.206162</td>\n",
              "      <td>-0.529859</td>\n",
              "      <td>-0.012301</td>\n",
              "      <td>-0.181541</td>\n",
              "      <td>-0.852200</td>\n",
              "      <td>-0.365061</td>\n",
              "      <td>-0.190672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.233880</td>\n",
              "      <td>2.015813</td>\n",
              "      <td>-0.695306</td>\n",
              "      <td>-0.012301</td>\n",
              "      <td>-0.181541</td>\n",
              "      <td>-1.332500</td>\n",
              "      <td>0.604397</td>\n",
              "      <td>-0.105584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.844885</td>\n",
              "      <td>-1.074652</td>\n",
              "      <td>-0.529859</td>\n",
              "      <td>-0.695245</td>\n",
              "      <td>-0.540642</td>\n",
              "      <td>-0.633881</td>\n",
              "      <td>-0.920763</td>\n",
              "      <td>-1.041549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.141852</td>\n",
              "      <td>0.503458</td>\n",
              "      <td>-2.680669</td>\n",
              "      <td>0.670643</td>\n",
              "      <td>0.316566</td>\n",
              "      <td>1.549303</td>\n",
              "      <td>5.484909</td>\n",
              "      <td>-0.020496</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb42809a-4831-4f9f-8fe2-1ecfc93e5b68')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb42809a-4831-4f9f-8fe2-1ecfc93e5b68 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb42809a-4831-4f9f-8fe2-1ecfc93e5b68');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model building:**"
      ],
      "metadata": {
        "id": "W8t00hhQEFhC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the dataset\n",
        "\n",
        "X = df.drop('Outcome', axis=1)\n",
        "y = df['Outcome']"
      ],
      "metadata": {
        "id": "D6hR2zJsEMW7"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.33, random_state=7)"
      ],
      "metadata": {
        "id": "A1DAh7-bEaGZ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest-**"
      ],
      "metadata": {
        "id": "o-WZczDNErYB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier(n_estimators=200)\n",
        "rfc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "khCNtsnrEzPY",
        "outputId": "280ab985-8915-4ef4-b37e-758d4dbea791"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=200)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#let’s check the accuracy of the model on the training dataset.\n",
        "\n",
        "rfc_train = rfc.predict(X_train)\n",
        "from sklearn import metrics\n",
        "\n",
        "print(\"Accuracy_Score =\", format(metrics.accuracy_score(y_train, rfc_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixOuIHalE3H0",
        "outputId": "bb635156-bab4-41d9-8da2-5fb88753b319"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy_Score = 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the accuracy score for Random Forest\n",
        "\n",
        "from sklearn import metrics\n",
        "\n",
        "predictions = rfc.predict(X_test)\n",
        "print(\"Accuracy_Score =\", format(metrics.accuracy_score(y_test, predictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRQBd3PJFccW",
        "outputId": "584da1fc-9bfd-4e0d-8caf-9e46d76d5ed2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy_Score = 0.7716535433070866\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification_report =\\n\", format(metrics.classification_report(y_test, predictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PILzq9-dFrjf",
        "outputId": "b7d0edb8-f7dd-40b7-bcb3-d811e37e3387"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification_report =\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.85      0.83       162\n",
            "           1       0.70      0.64      0.67        92\n",
            "\n",
            "    accuracy                           0.77       254\n",
            "   macro avg       0.75      0.74      0.75       254\n",
            "weighted avg       0.77      0.77      0.77       254\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nConfusion_matrix =\\n\", format(metrics.confusion_matrix(y_test, predictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B-J8nliF2T8",
        "outputId": "0604006b-a9e0-48ad-f63c-f5a5b74d3aaa"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion_matrix =\n",
            " [[137  25]\n",
            " [ 33  59]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**XgBoost classifier-**"
      ],
      "metadata": {
        "id": "7z5KuD0FGfUP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Building model using XGBoost\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb_model = XGBClassifier(gamma=0)\n",
        "xgb_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "_w-Yy9FUGl4N",
        "outputId": "381fa9ce-28d1-4de1-d800-6ee7390050a9"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=0, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=0, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=0, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "xgb_pred = xgb_model.predict(X_test)\n",
        "print(\"Accuracy Score =\", format(metrics.accuracy_score(y_test, xgb_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9-NmgooGrcM",
        "outputId": "686c192e-c185-4ed2-f892-387f801d983d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score = 0.7401574803149606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification_report =\\n\", format(metrics.classification_report(y_test, xgb_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uVrjXriGvd2",
        "outputId": "a8e6e640-7c47-48c1-9389-ec3af924b69e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification_report =\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.81      0.80       162\n",
            "           1       0.65      0.62      0.63        92\n",
            "\n",
            "    accuracy                           0.74       254\n",
            "   macro avg       0.72      0.71      0.72       254\n",
            "weighted avg       0.74      0.74      0.74       254\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nConfusion_matrix =\\n\", format(metrics.confusion_matrix(y_test, xgb_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7QLdoT-Gvgd",
        "outputId": "07f99d7d-7cdd-49fe-9c2c-5b15763eed77"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion_matrix =\n",
            " [[131  31]\n",
            " [ 35  57]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Support Vector Machine (SVM)**"
      ],
      "metadata": {
        "id": "s1y-FEdgHJ0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Building the model using Support Vector Machine (SVM)\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svc_model = SVC()\n",
        "svc_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Xmwb6b9pGvlJ",
        "outputId": "29c24d23-a42d-4d61-d347-d7ab3a213d93"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction from support vector machine model on the testing data\n",
        "\n",
        "svc_pred = svc_model.predict(X_test)\n",
        "\n",
        "#Accuracy score for SVM\n",
        "\n",
        "print(\"Accuracy Score =\", format(metrics.accuracy_score(y_test, svc_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnvD2pbTGvnQ",
        "outputId": "63eefb72-e03a-4058-ea29-67d38998fcd8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score = 0.7480314960629921\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nConfusion_matrix =\\n\", confusion_matrix(y_test, svc_pred))\n",
        "print(\"\\nClassification_report =\\n\",classification_report(y_test,svc_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SC75F6NCGvpc",
        "outputId": "cf6d765f-54d0-45b4-c386-c612e358776f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion_matrix =\n",
            " [[145  17]\n",
            " [ 47  45]]\n",
            "\n",
            "Classification_report =\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.90      0.82       162\n",
            "           1       0.73      0.49      0.58        92\n",
            "\n",
            "    accuracy                           0.75       254\n",
            "   macro avg       0.74      0.69      0.70       254\n",
            "weighted avg       0.74      0.75      0.73       254\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Conclusion from Model Building-**"
      ],
      "metadata": {
        "id": "-6v6OZwTHz2w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Therefore Random forest is the best model for this prediction since it has an accuracy_score of 0.76."
      ],
      "metadata": {
        "id": "8tjlYcq7H8Rg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting feature importances\n",
        "\n",
        "rfc.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1aqtup5H33s",
        "outputId": "e3d0efc5-04e1-4ff1-cbb6-535de84b262d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.08088661, 0.25282999, 0.08374112, 0.073553  , 0.07276284,\n",
              "       0.16835969, 0.12737029, 0.14049644])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(pd.Series(rfc.feature_importances_, index=X.columns).plot(kind='barh'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "GZBqyX-dIJGc",
        "outputId": "cec09010-de46-46f5-9807-61505214bd50"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Saving Model - Random Forest:**"
      ],
      "metadata": {
        "id": "L5NYBEbWIQMp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "#Firstly we will be using the dump() function to save the model using pickle\n",
        "saved_model = pickle.dumps(rfc)\n",
        "\n",
        "#Then we will be loading that saved model\n",
        "rfc_from_pickle = pickle.loads(saved_model)\n",
        "\n",
        "#Lastly, after loading that model we will use this to make predictions\n",
        "rfc_from_pickle.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQ2ffWvCIVP0",
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      "execution_count": 53,
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        "Now for the last time, I’ll be looking at the head and tail of the dataset so that we can take any random set of features from both the head and tail of the data to test that if our model is good enough to give the right prediction."
      ],
      "metadata": {
        "id": "79XizMbIIfjZ"
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      "execution_count": 54,
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
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              "\n",
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              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "763           10      101             76             48      180  32.9   \n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Putting data points in the model will either return 0 or 1 i.e. person suffering from diabetes or not.\n",
        "\n",
        "rfc.predict([[0,137,40,35,168,43.1,2.228,33]]) #4th patient"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLiW8zoNIoHa",
        "outputId": "a53274db-0c2c-40e3-8f66-b694b2a11798"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Another one\n",
        "\n",
        "rfc.predict([[10,101,76,48,180,32.9,0.171,63]])  #763th patient"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHYTKMnWIzYV",
        "outputId": "ecf1f7a9-cc39-4b8e-da87-d923fe7f5e18"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After using all these patient records, we are able to build a machine learning model (random forest – best one) to accurately predict whether or not the patients in the dataset have diabetes or not along with that we were able to draw some insights from the data via data analysis and visualization."
      ],
      "metadata": {
        "id": "sCaOsN6DI6PQ"
      }
    }
  ]
}