7_2_6_Implementing_Logistic_Regression_from_scratch_in_Python.ipynb

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        "<a href=\"https://colab.research.google.com/github/Agamdeep555/GITDemo./blob/main/7_2_6_Implementing_Logistic_Regression_from_scratch_in_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5BLpBB_cv8y"
      },
      "source": [
        "**Logistic Regression:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQA2GUq7cPXd"
      },
      "source": [
        "![Picture4.png](data:image/png;base64,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)"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0dxwE-Ubsdf"
      },
      "source": [
        "Y_hat --> predicted value\n",
        "\n",
        "X --> Input Variable\n",
        "\n",
        "w --> weight\n",
        "\n",
        "b --> bias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8OJXGuPvDt2"
      },
      "source": [
        "**Gradient Descent:**\n",
        "\n",
        "Gradient Descent is an optimization algorithm used for minimizing the loss function in various machine learning algorithms. It is used for updating the parameters of the learning model.\n",
        "\n",
        "w  =  w - α*dw\n",
        "\n",
        "b  =  b - α*db"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSAfYP7WmECB"
      },
      "source": [
        "**Learning Rate:**\n",
        "\n",
        "Learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8AfHQz9cXRF"
      },
      "source": [
        "**Derivatives:**"
      ]
    },
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      "cell_type": "markdown",
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      "source": [
        "![Picture3.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxOpuBj_SqV-"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdjRDi8wlgX6"
      },
      "source": [
        "# importing numpy library\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTA-BwebLjdc"
      },
      "source": [
        "**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcqEjNwtKHPq"
      },
      "source": [
        "class Logistic_Regression():\n",
        "\n",
        "\n",
        "  # declaring learning rate & number of iterations (Hyperparametes)\n",
        "  def __init__(self, learning_rate, no_of_iterations):\n",
        "\n",
        "    self.learning_rate = learning_rate\n",
        "    self.no_of_iterations = no_of_iterations\n",
        "\n",
        "\n",
        "\n",
        "  # fit function to train the model with dataset\n",
        "  def fit(self, X, Y):\n",
        "\n",
        "    # number of data points in the dataset (number of rows)  -->  m\n",
        "    # number of input features in the dataset (number of columns)  --> n\n",
        "    self.m, self.n = X.shape\n",
        "\n",
        "\n",
        "    #initiating weight & bias value\n",
        "\n",
        "    self.w = np.zeros(self.n)\n",
        "\n",
        "    self.b = 0\n",
        "\n",
        "    self.X = X\n",
        "\n",
        "    self.Y = Y\n",
        "\n",
        "\n",
        "    # implementing Gradient Descent for Optimization\n",
        "\n",
        "    for i in range(self.no_of_iterations):\n",
        "      self.update_weights()\n",
        "\n",
        "\n",
        "\n",
        "  def update_weights(self):\n",
        "\n",
        "    # Y_hat formula (sigmoid function)\n",
        "\n",
        "    Y_hat = 1 / (1 + np.exp( - (self.X.dot(self.w) + self.b ) ))\n",
        "\n",
        "\n",
        "    # derivaties\n",
        "\n",
        "    dw = (1/self.m)*np.dot(self.X.T, (Y_hat - self.Y))\n",
        "\n",
        "    db = (1/self.m)*np.sum(Y_hat - self.Y)\n",
        "\n",
        "\n",
        "    # updating the weights & bias using gradient descent\n",
        "\n",
        "    self.w = self.w - self.learning_rate * dw\n",
        "\n",
        "    self.b = self.b - self.learning_rate * db\n",
        "\n",
        "\n",
        "  # Sigmoid Equation & Decision Boundary\n",
        "\n",
        "  def predict(self, X):\n",
        "\n",
        "    Y_pred = 1 / (1 + np.exp( - (X.dot(self.w) + self.b ) ))\n",
        "    Y_pred = np.where( Y_pred > 0.5, 1, 0)\n",
        "    return Y_pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnPbntVRnfvV"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-71UtHzNVWjB"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmfOfG8joBBy"
      },
      "source": [
        "Data Collection and Analysis\n",
        "\n",
        "PIMA Diabetes Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xpw6Mj_pn_TL"
      },
      "source": [
        "# loading the diabetes dataset to a pandas DataFrame\n",
        "diabetes_dataset = pd.read_csv('/content/diabetes.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tjO09ncovoh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "54bd44af-816d-4bae-a13c-8907b9be4cc0"
      },
      "source": [
        "# printing the first 5 rows of the dataset\n",
        "diabetes_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72  ...                     0.627   50        1\n",
              "1            1       85             66  ...                     0.351   31        0\n",
              "2            8      183             64  ...                     0.672   32        1\n",
              "3            1       89             66  ...                     0.167   21        0\n",
              "4            0      137             40  ...                     2.288   33        1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lynParo6pEMB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "263c83a5-89a5-4f87-9bf7-c8167e36c357"
      },
      "source": [
        "# number of rows and Columns in this dataset\n",
        "diabetes_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NDJOlrEpmoL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "edab8d68-41a1-43ad-b7d1-c8cada11d506"
      },
      "source": [
        "# getting the statistical measures of the data\n",
        "diabetes_dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pregnancies     Glucose  ...         Age     Outcome\n",
              "count   768.000000  768.000000  ...  768.000000  768.000000\n",
              "mean      3.845052  120.894531  ...   33.240885    0.348958\n",
              "std       3.369578   31.972618  ...   11.760232    0.476951\n",
              "min       0.000000    0.000000  ...   21.000000    0.000000\n",
              "25%       1.000000   99.000000  ...   24.000000    0.000000\n",
              "50%       3.000000  117.000000  ...   29.000000    0.000000\n",
              "75%       6.000000  140.250000  ...   41.000000    1.000000\n",
              "max      17.000000  199.000000  ...   81.000000    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrpHzaGpp5dQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52b808a5-92b0-4375-813d-3d4a52f366d5"
      },
      "source": [
        "diabetes_dataset['Outcome'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cB1qRaNcqeh5"
      },
      "source": [
        "0 --> Non-Diabetic\n",
        "\n",
        "1 --> Diabetic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6MWR0k_qSCK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "d817bb27-3d56-43e9-a72d-1225679c6c06"
      },
      "source": [
        "diabetes_dataset.groupby('Outcome').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.298000</td>\n",
              "      <td>109.980000</td>\n",
              "      <td>68.184000</td>\n",
              "      <td>19.664000</td>\n",
              "      <td>68.792000</td>\n",
              "      <td>30.304200</td>\n",
              "      <td>0.429734</td>\n",
              "      <td>31.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.865672</td>\n",
              "      <td>141.257463</td>\n",
              "      <td>70.824627</td>\n",
              "      <td>22.164179</td>\n",
              "      <td>100.335821</td>\n",
              "      <td>35.142537</td>\n",
              "      <td>0.550500</td>\n",
              "      <td>37.067164</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Pregnancies     Glucose  ...  DiabetesPedigreeFunction        Age\n",
              "Outcome                           ...                                     \n",
              "0           3.298000  109.980000  ...                  0.429734  31.190000\n",
              "1           4.865672  141.257463  ...                  0.550500  37.067164\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoDW7l9mqqHZ"
      },
      "source": [
        "# separating the data and labels\n",
        "features = diabetes_dataset.drop(columns = 'Outcome', axis=1)\n",
        "target = diabetes_dataset['Outcome']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eiRW9M9raMm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4503035-f3c1-44ea-e130-d7a8834b3817"
      },
      "source": [
        "print(features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age\n",
            "0              6      148             72  ...  33.6                     0.627   50\n",
            "1              1       85             66  ...  26.6                     0.351   31\n",
            "2              8      183             64  ...  23.3                     0.672   32\n",
            "3              1       89             66  ...  28.1                     0.167   21\n",
            "4              0      137             40  ...  43.1                     2.288   33\n",
            "..           ...      ...            ...  ...   ...                       ...  ...\n",
            "763           10      101             76  ...  32.9                     0.171   63\n",
            "764            2      122             70  ...  36.8                     0.340   27\n",
            "765            5      121             72  ...  26.2                     0.245   30\n",
            "766            1      126             60  ...  30.1                     0.349   47\n",
            "767            1       93             70  ...  30.4                     0.315   23\n",
            "\n",
            "[768 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AoxgTJAMrcCl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c925995f-76eb-4473-d734-91ccc5d54edb"
      },
      "source": [
        "print(target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      1\n",
            "1      0\n",
            "2      1\n",
            "3      0\n",
            "4      1\n",
            "      ..\n",
            "763    0\n",
            "764    0\n",
            "765    0\n",
            "766    1\n",
            "767    0\n",
            "Name: Outcome, Length: 768, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umAbo_kqrlzI"
      },
      "source": [
        "Data Standardization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njfM5X60rgnc"
      },
      "source": [
        "scaler = StandardScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0ai5ARbr53p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a350912-b2c7-4169-8596-fdef55e76d38"
      },
      "source": [
        "scaler.fit(features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler(copy=True, with_mean=True, with_std=True)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHxNwPuZr-kD"
      },
      "source": [
        "standardized_data = scaler.transform(features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjMwZ5x6sPUJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98b189e5-bb79-4019-9a29-9f47441cfc44"
      },
      "source": [
        "print(standardized_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
            "   1.4259954 ]\n",
            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
            "  -0.19067191]\n",
            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
            "  -0.10558415]\n",
            " ...\n",
            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
            "  -0.27575966]\n",
            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
            "   1.17073215]\n",
            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
            "  -0.87137393]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxWSl4SGsRjE"
      },
      "source": [
        "features = standardized_data\n",
        "target = diabetes_dataset['Outcome']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhJF_7QjsjmP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4aceaff2-f4bb-4aa8-d0b3-2fbbe9b104c9"
      },
      "source": [
        "print(features)\n",
        "print(target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
            "   1.4259954 ]\n",
            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
            "  -0.19067191]\n",
            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
            "  -0.10558415]\n",
            " ...\n",
            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
            "  -0.27575966]\n",
            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
            "   1.17073215]\n",
            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
            "  -0.87137393]]\n",
            "0      1\n",
            "1      0\n",
            "2      1\n",
            "3      0\n",
            "4      1\n",
            "      ..\n",
            "763    0\n",
            "764    0\n",
            "765    0\n",
            "766    1\n",
            "767    0\n",
            "Name: Outcome, Length: 768, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHciEFkxsoQP"
      },
      "source": [
        "Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEfKGj_yslvD"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(features,target, test_size = 0.2, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DR05T-o0t3FQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f118c5e-8073-49ac-a8e5-f7175de24fb2"
      },
      "source": [
        "print(features.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(768, 8) (614, 8) (154, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElJ3tkOtuC_n"
      },
      "source": [
        "Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5szLWHlNt9xc"
      },
      "source": [
        "classifier = Logistic_Regression(learning_rate=0.01, no_of_iterations=1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncJWY_7suPAb"
      },
      "source": [
        "#training the support vector Machine Classifier\n",
        "classifier.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UV4-CAfquiyP"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhAjGPJWunXa"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJLEPQK7ueXp"
      },
      "source": [
        "# accuracy score on the training data\n",
        "X_train_prediction = classifier.predict(X_train)\n",
        "training_data_accuracy = accuracy_score( Y_train, X_train_prediction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmJ22qhVvNwj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c48b6ada-2b72-4c7b-9484-ffcec321fed6"
      },
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.7768729641693811\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2CICFMEvcCl"
      },
      "source": [
        "# accuracy score on the test data\n",
        "X_test_prediction = classifier.predict(X_test)\n",
        "test_data_accuracy = accuracy_score( Y_test, X_test_prediction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2GcW_t_vz7C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb916142-f1a0-49b3-a75c-f3f555957fef"
      },
      "source": [
        "print('Accuracy score of the test data : ', test_data_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the test data :  0.7662337662337663\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gq8ZX1xpwPF5"
      },
      "source": [
        "Making a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-ULRe4yv5tH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70433fc9-8809-4107-c502-90c34285d4df"
      },
      "source": [
        "input_data = (5,166,72,19,175,25.8,0.587,51)\n",
        "\n",
        "# changing the input_data to numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the array as we are predicting for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "# standardize the input data\n",
        "std_data = scaler.transform(input_data_reshaped)\n",
        "print(std_data)\n",
        "\n",
        "prediction = classifier.predict(std_data)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0] == 0):\n",
        "  print('The person is not diabetic')\n",
        "else:\n",
        "  print('The person is diabetic')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.3429808   1.41167241  0.14964075 -0.09637905  0.82661621 -0.78595734\n",
            "   0.34768723  1.51108316]]\n",
            "[1]\n",
            "The person is diabetic\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hqu9FUHMU8IJ"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}