Gradient_descent.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOuR3gYqBzPw",
        "outputId": "be00cd3a-53e4-4c2e-ec29-ca80e041178b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oq4MfF_2BZBd"
      },
      "source": [
        "<h2 style=\"color:green\" align=\"center\">Implement Gradient Descent For Neural Network (or Logistic Regression)</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5gDC-PTBZBh"
      },
      "source": [
        "<h4 style=\"color:blue\">Predicting if a person would buy life insurnace based on his age using logistic regression</h4>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pKyduyKiBZBi"
      },
      "source": [
        "Above is a binary logistic regression problem as there are only two possible outcomes (i.e. if person buys insurance or he/she doesn't)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sg55G2X2BZBi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "   age  affordibility  bought_insurance\n",
              "0   22              1                 0\n",
              "1   25              0                 0\n",
              "2   47              1                 1\n",
              "3   52              0                 0\n",
              "4   46              1                 1"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 28,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 18,\n        \"max\": 62,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          62,\n          19,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"affordibility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bought_insurance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Deep Learning/Gradient Descent/insurance_data.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5QRVq8D7FqvK",
        "outputId": "12ddfb17-4848-4cf0-868f-94b0b755afaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73fUjH3RBZBk"
      },
      "source": [
        "**Split train and test set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q41qwH2wBZBk"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(df[['age','affordibility']],df.bought_insurance,test_size=0.2, random_state=25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fX4VA4lWBZBl"
      },
      "source": [
        "**Preprocessing: Scale the data so that both age and affordibility are in same scaling range**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "au0UrEJ0BZBl"
      },
      "outputs": [],
      "source": [
        "X_train_scaled = X_train.copy()\n",
        "X_train_scaled['age'] = X_train_scaled['age'] / 100\n",
        "\n",
        "X_test_scaled = X_test.copy()\n",
        "X_test_scaled['age'] = X_test_scaled['age'] / 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_soHasZaBZBl"
      },
      "source": [
        "**Model Building: First build a model in keras/tensorflow and see what weights and bias values it comes up with. We will than try to reproduce same weights and bias in our plain python implementation of gradient descent. Below is the architecture of our simple neural network**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iVTKt1CxBZBl"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UW0njpFdBZBl",
        "outputId": "a3fef59a-7f67-4e29-ec7e-1a7eb3bbf351"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7113 - accuracy: 0.5000\n",
            "Epoch 2/5000\n",
            "1/1 [==============================] - 0s 999us/step - loss: 0.7110 - accuracy: 0.5000\n",
            "Epoch 3/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7106 - accuracy: 0.5000\n",
            "Epoch 4/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7102 - accuracy: 0.5000\n",
            "Epoch 5/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7098 - accuracy: 0.5000\n",
            "Epoch 6/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7094 - accuracy: 0.5000\n",
            "Epoch 7/5000\n",
            "1/1 [==============================] - 0s 999us/step - loss: 0.7091 - accuracy: 0.5000\n",
            "Epoch 8/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7087 - accuracy: 0.5000\n",
            "Epoch 9/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7083 - accuracy: 0.5000\n",
            "Epoch 10/5000\n",
            "1/1 [==============================] - 0s 999us/step - loss: 0.7079 - accuracy: 0.5000\n",
            "Epoch 11/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7076 - accuracy: 0.5000\n",
            "Epoch 12/5000\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.7072 - accuracy: 0.5000\n",
            "Epoch 13/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7068 - accuracy: 0.5000\n",
            "Epoch 14/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7065 - accuracy: 0.5000\n",
            "Epoch 15/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7061 - accuracy: 0.5000\n",
            "Epoch 16/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7057 - accuracy: 0.5000\n",
            "Epoch 17/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7054 - accuracy: 0.5000\n",
            "Epoch 18/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7050 - accuracy: 0.5000\n",
            "Epoch 19/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7046 - accuracy: 0.5000\n",
            "Epoch 20/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7043 - accuracy: 0.5000\n",
            "Epoch 21/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7039 - accuracy: 0.5000\n",
            "Epoch 22/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7035 - accuracy: 0.5000\n",
            "Epoch 23/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7032 - accuracy: 0.5000\n",
            "Epoch 24/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7028 - accuracy: 0.5000\n",
            "Epoch 25/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7025 - accuracy: 0.5000\n",
            "Epoch 26/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7021 - accuracy: 0.5000\n",
            "Epoch 27/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7017 - accuracy: 0.5000\n",
            "Epoch 28/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7014 - accuracy: 0.5000\n",
            "Epoch 29/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7010 - accuracy: 0.5000\n",
            "Epoch 30/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.7007 - accuracy: 0.5000\n",
            "Epoch 31/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.7003 - accuracy: 0.5000\n",
            "Epoch 32/5000\n",
            "1/1 [==============================] - 0s 1000us/step - loss: 0.7000 - accuracy: 0.5000\n",
            "Epoch 33/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.6996 - accuracy: 0.5000\n",
            "Epoch 34/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.6993 - accuracy: 0.5000\n",
            "Epoch 35/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.6989 - accuracy: 0.5000\n",
            "Epoch 36/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.6986 - accuracy: 0.5000\n",
            "Epoch 37/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.6982 - accuracy: 0.5000\n",
            "Epoch 38/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.6979 - accuracy: 0.5000\n",
            "Epoch 39/5000\n",
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        },
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          ]
        },
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          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 0s/step - loss: 0.6013 - accuracy: 0.6818\n",
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          ]
        },
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          ]
        },
        {
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          "text": [
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          ]
        },
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          ]
        },
        {
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          "text": [
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          ]
        },
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          ]
        },
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          ]
        },
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          ]
        },
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            "Epoch 1958/5000\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
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            "Epoch 1959/5000\n",
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            "Epoch 2039/5000\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 1ms/step - loss: 0.5511 - accuracy: 0.6818\n",
            "Epoch 2040/5000\n",
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          "text": [
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          ]
        },
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          "text": [
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        },
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          ]
        },
        {
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          "text": [
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        },
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          ]
        },
        {
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          "output_type": "stream",
          "text": [
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          ]
        },
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          ]
        },
        {
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          "output_type": "stream",
          "text": [
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          ]
        },
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          ]
        },
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          ]
        },
        {
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          "text": [
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            "Epoch 3984/5000\n",
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        },
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        },
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            "Epoch 4848/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4660 - accuracy: 0.9091\n",
            "Epoch 4849/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4660 - accuracy: 0.9091\n",
            "Epoch 4850/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4660 - accuracy: 0.9091\n",
            "Epoch 4851/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4659 - accuracy: 0.9091\n",
            "Epoch 4852/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4659 - accuracy: 0.9091\n",
            "Epoch 4853/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4659 - accuracy: 0.9091\n",
            "Epoch 4854/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4659 - accuracy: 0.9091\n",
            "Epoch 4855/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4659 - accuracy: 0.9091\n",
            "Epoch 4856/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4659 - accuracy: 0.9091\n",
            "Epoch 4857/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4658 - accuracy: 0.9091\n",
            "Epoch 4858/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4658 - accuracy: 0.9091\n",
            "Epoch 4859/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4658 - accuracy: 0.9091\n",
            "Epoch 4860/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4658 - accuracy: 0.9091\n",
            "Epoch 4861/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4658 - accuracy: 0.9091\n",
            "Epoch 4862/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4657 - accuracy: 0.9091\n",
            "Epoch 4863/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4657 - accuracy: 0.9091\n",
            "Epoch 4864/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4657 - accuracy: 0.9091\n",
            "Epoch 4865/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4657 - accuracy: 0.9091\n",
            "Epoch 4866/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4657 - accuracy: 0.9091\n",
            "Epoch 4867/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4656 - accuracy: 0.9091\n",
            "Epoch 4868/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4656 - accuracy: 0.9091\n",
            "Epoch 4869/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4656 - accuracy: 0.9091\n",
            "Epoch 4870/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4656 - accuracy: 0.9091\n",
            "Epoch 4871/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4656 - accuracy: 0.9091\n",
            "Epoch 4872/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.9091\n",
            "Epoch 4873/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.9091\n",
            "Epoch 4874/5000\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.9091\n",
            "Epoch 4875/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.9091\n",
            "Epoch 4876/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4655 - accuracy: 0.9091\n",
            "Epoch 4877/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4654 - accuracy: 0.9091\n",
            "Epoch 4878/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4654 - accuracy: 0.9091\n",
            "Epoch 4879/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4654 - accuracy: 0.9091\n",
            "Epoch 4880/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4654 - accuracy: 0.9091\n",
            "Epoch 4881/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4654 - accuracy: 0.9091\n",
            "Epoch 4882/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4653 - accuracy: 0.9091\n",
            "Epoch 4883/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4653 - accuracy: 0.9091\n",
            "Epoch 4884/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4653 - accuracy: 0.9091\n",
            "Epoch 4885/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4653 - accuracy: 0.9091\n",
            "Epoch 4886/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4653 - accuracy: 0.9091\n",
            "Epoch 4887/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4652 - accuracy: 0.9091\n",
            "Epoch 4888/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4652 - accuracy: 0.9091\n",
            "Epoch 4889/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4652 - accuracy: 0.9091\n",
            "Epoch 4890/5000\n",
            "1/1 [==============================] - 0s 1000us/step - loss: 0.4652 - accuracy: 0.9091\n",
            "Epoch 4891/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4652 - accuracy: 0.9091\n",
            "Epoch 4892/5000\n",
            "1/1 [==============================] - 0s 1000us/step - loss: 0.4651 - accuracy: 0.9091\n",
            "Epoch 4893/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4651 - accuracy: 0.9091\n",
            "Epoch 4894/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4651 - accuracy: 0.9091\n",
            "Epoch 4895/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4651 - accuracy: 0.9091\n",
            "Epoch 4896/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4651 - accuracy: 0.9091\n",
            "Epoch 4897/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4650 - accuracy: 0.9091\n",
            "Epoch 4898/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4650 - accuracy: 0.9091\n",
            "Epoch 4899/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4650 - accuracy: 0.9091\n",
            "Epoch 4900/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4650 - accuracy: 0.9091\n",
            "Epoch 4901/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4650 - accuracy: 0.9091\n",
            "Epoch 4902/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4650 - accuracy: 0.9091\n",
            "Epoch 4903/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4649 - accuracy: 0.9091\n",
            "Epoch 4904/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4649 - accuracy: 0.9091\n",
            "Epoch 4905/5000\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.4649 - accuracy: 0.9091\n",
            "Epoch 4906/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4649 - accuracy: 0.9091\n",
            "Epoch 4907/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4649 - accuracy: 0.9091\n",
            "Epoch 4908/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4648 - accuracy: 0.9091\n",
            "Epoch 4909/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4648 - accuracy: 0.9091\n",
            "Epoch 4910/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4648 - accuracy: 0.9091\n",
            "Epoch 4911/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4648 - accuracy: 0.9091\n",
            "Epoch 4912/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4648 - accuracy: 0.9091\n",
            "Epoch 4913/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4647 - accuracy: 0.9091\n",
            "Epoch 4914/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4647 - accuracy: 0.9091\n",
            "Epoch 4915/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4647 - accuracy: 0.9091\n",
            "Epoch 4916/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4647 - accuracy: 0.9091\n",
            "Epoch 4917/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4647 - accuracy: 0.9091\n",
            "Epoch 4918/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4646 - accuracy: 0.9091\n",
            "Epoch 4919/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4646 - accuracy: 0.9091\n",
            "Epoch 4920/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4646 - accuracy: 0.9091\n",
            "Epoch 4921/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4646 - accuracy: 0.9091\n",
            "Epoch 4922/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4646 - accuracy: 0.9091\n",
            "Epoch 4923/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4645 - accuracy: 0.9091\n",
            "Epoch 4924/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4645 - accuracy: 0.9091\n",
            "Epoch 4925/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4645 - accuracy: 0.9091\n",
            "Epoch 4926/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4645 - accuracy: 0.9091\n",
            "Epoch 4927/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4645 - accuracy: 0.9091\n",
            "Epoch 4928/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4644 - accuracy: 0.9091\n",
            "Epoch 4929/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4644 - accuracy: 0.9091\n",
            "Epoch 4930/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4644 - accuracy: 0.9091\n",
            "Epoch 4931/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4644 - accuracy: 0.9091\n",
            "Epoch 4932/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4644 - accuracy: 0.9091\n",
            "Epoch 4933/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4644 - accuracy: 0.9091\n",
            "Epoch 4934/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4643 - accuracy: 0.9091\n",
            "Epoch 4935/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4643 - accuracy: 0.9091\n",
            "Epoch 4936/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4643 - accuracy: 0.9091\n",
            "Epoch 4937/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4643 - accuracy: 0.9091\n",
            "Epoch 4938/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4643 - accuracy: 0.9091\n",
            "Epoch 4939/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4642 - accuracy: 0.9091\n",
            "Epoch 4940/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4642 - accuracy: 0.9091\n",
            "Epoch 4941/5000\n",
            "1/1 [==============================] - 0s 1000us/step - loss: 0.4642 - accuracy: 0.9091\n",
            "Epoch 4942/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4642 - accuracy: 0.9091\n",
            "Epoch 4943/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4642 - accuracy: 0.9091\n",
            "Epoch 4944/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4641 - accuracy: 0.9091\n",
            "Epoch 4945/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4641 - accuracy: 0.9091\n",
            "Epoch 4946/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4641 - accuracy: 0.9091\n",
            "Epoch 4947/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4641 - accuracy: 0.9091\n",
            "Epoch 4948/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4641 - accuracy: 0.9091\n",
            "Epoch 4949/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4640 - accuracy: 0.9091\n",
            "Epoch 4950/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4640 - accuracy: 0.9091\n",
            "Epoch 4951/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4640 - accuracy: 0.9091\n",
            "Epoch 4952/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4640 - accuracy: 0.9091\n",
            "Epoch 4953/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4640 - accuracy: 0.9091\n",
            "Epoch 4954/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4639 - accuracy: 0.9091\n",
            "Epoch 4955/5000\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4639 - accuracy: 0.9091\n",
            "Epoch 4956/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4639 - accuracy: 0.9091\n",
            "Epoch 4957/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4639 - accuracy: 0.9091\n",
            "Epoch 4958/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4639 - accuracy: 0.9091\n",
            "Epoch 4959/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4639 - accuracy: 0.9091\n",
            "Epoch 4960/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4638 - accuracy: 0.9091\n",
            "Epoch 4961/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4638 - accuracy: 0.9091\n",
            "Epoch 4962/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4638 - accuracy: 0.9091\n",
            "Epoch 4963/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4638 - accuracy: 0.9091\n",
            "Epoch 4964/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4638 - accuracy: 0.9091\n",
            "Epoch 4965/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4637 - accuracy: 0.9091\n",
            "Epoch 4966/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4637 - accuracy: 0.9091\n",
            "Epoch 4967/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4637 - accuracy: 0.9091\n",
            "Epoch 4968/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4637 - accuracy: 0.9091\n",
            "Epoch 4969/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4637 - accuracy: 0.9091\n",
            "Epoch 4970/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4636 - accuracy: 0.9091\n",
            "Epoch 4971/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4636 - accuracy: 0.9091\n",
            "Epoch 4972/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4636 - accuracy: 0.9091\n",
            "Epoch 4973/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4636 - accuracy: 0.9091\n",
            "Epoch 4974/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4636 - accuracy: 0.9091\n",
            "Epoch 4975/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4635 - accuracy: 0.9091\n",
            "Epoch 4976/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4635 - accuracy: 0.9091\n",
            "Epoch 4977/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4635 - accuracy: 0.9091\n",
            "Epoch 4978/5000\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 0.4635 - accuracy: 0.9091\n",
            "Epoch 4979/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4635 - accuracy: 0.9091\n",
            "Epoch 4980/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4635 - accuracy: 0.9091\n",
            "Epoch 4981/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4634 - accuracy: 0.9091\n",
            "Epoch 4982/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4634 - accuracy: 0.9091\n",
            "Epoch 4983/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4634 - accuracy: 0.9091\n",
            "Epoch 4984/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4634 - accuracy: 0.9091\n",
            "Epoch 4985/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4634 - accuracy: 0.9091\n",
            "Epoch 4986/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4633 - accuracy: 0.9091\n",
            "Epoch 4987/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4633 - accuracy: 0.9091\n",
            "Epoch 4988/5000\n",
            "1/1 [==============================] - 0s 999us/step - loss: 0.4633 - accuracy: 0.9091\n",
            "Epoch 4989/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4633 - accuracy: 0.9091\n",
            "Epoch 4990/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4633 - accuracy: 0.9091\n",
            "Epoch 4991/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4632 - accuracy: 0.9091\n",
            "Epoch 4992/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4632 - accuracy: 0.9091\n",
            "Epoch 4993/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4632 - accuracy: 0.9091\n",
            "Epoch 4994/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4632 - accuracy: 0.9091\n",
            "Epoch 4995/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4632 - accuracy: 0.9091\n",
            "Epoch 4996/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4631 - accuracy: 0.9091\n",
            "Epoch 4997/5000\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 0.4631 - accuracy: 0.9091\n",
            "Epoch 4998/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4631 - accuracy: 0.9091\n",
            "Epoch 4999/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4631 - accuracy: 0.9091\n",
            "Epoch 5000/5000\n",
            "1/1 [==============================] - 0s 0s/step - loss: 0.4631 - accuracy: 0.9091\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x22933758d30>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(1, input_shape=(2,), activation='sigmoid', kernel_initializer='ones', bias_initializer='zeros')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy', # log loss\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train_scaled, y_train, epochs=5000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bm-XqiH3BZBm"
      },
      "source": [
        "**Evaluate the model on test set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "UzvhG8mhBZBm",
        "outputId": "110efc09-cc7c-4125-ea6d-a938094c68ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 1ms/step - loss: 0.3550 - accuracy: 1.0000\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.35497748851776123, 1.0]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(X_test_scaled,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "iUXQn6cyIDsG",
        "outputId": "02790d7d-3e09-4aff-8b24-930f74af3a45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  affordibility\n",
              "2   0.47              1\n",
              "10  0.18              1\n",
              "21  0.26              0\n",
              "11  0.28              1\n",
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              "summary": "{\n  \"name\": \"X_test_scaled\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16582118883504202,\n        \"min\": 0.18,\n        \"max\": 0.61,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.47,\n          0.18,\n          0.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"affordibility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1TUwCRjbBZBm",
        "outputId": "d6606089-90a8-42b7-a3da-44c5ddafc848"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.7054848 ],\n",
              "       [0.35569546],\n",
              "       [0.16827849],\n",
              "       [0.47801173],\n",
              "       [0.7260697 ],\n",
              "       [0.8294984 ]], dtype=float32)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.predict(X_test_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "ibpOfPOCBZBm",
        "outputId": "0f4fd265-0076-44f7-8c22-fad120f6d8af"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2     1\n",
              "10    0\n",
              "21    0\n",
              "11    0\n",
              "14    1\n",
              "9     1\n",
              "Name: bought_insurance, dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53IDravZBZBm"
      },
      "source": [
        "**Now get the value of weights and bias from the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_DaoTjKRBZBm"
      },
      "outputs": [],
      "source": [
        "coef, intercept = model.get_weights()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "5WiuMFRMBZBm",
        "outputId": "f9fe30e8-4048-4d4c-a39b-c5b9b8a3d47b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([[5.060867 ],\n",
              "        [1.4086502]], dtype=float32),\n",
              " array([-2.9137027], dtype=float32))"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "coef, intercept"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LP7aqxS1BZBn"
      },
      "source": [
        "**This means w1=5.060867, w2=1.4086502, bias =-2.9137027**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nESnEfZZBZBn",
        "outputId": "c4cb6b1e-59a8-4fe0-f256-580e105400ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9999999847700205"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def sigmoid(x):\n",
        "        import math\n",
        "        return 1 / (1 + math.exp(-x))\n",
        "sigmoid(18)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kirkHTErBZBn",
        "outputId": "05b8a451-fd87-4db5-fbbc-d21896bed266"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "    age  affordibility\n",
              "2    47              1\n",
              "10   18              1\n",
              "21   26              0\n",
              "11   28              1\n",
              "14   49              1\n",
              "9    61              1"
            ]
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          "execution_count": 34,
          "metadata": {},
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      "source": [
        "X_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "645Kl8LdBZBn"
      },
      "source": [
        "**Instead of model.predict, write our own prediction function that uses w1,w2 and bias**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WWlScDbhBZBn",
        "outputId": "9f77a1c6-d72d-4b5a-c0a7-2deaf56ca7f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7054848693136117"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def prediction_function(age, affordibility):\n",
        "    weighted_sum = coef[0]*age + coef[1]*affordibility + intercept\n",
        "    return sigmoid(weighted_sum)\n",
        "\n",
        "prediction_function(.47, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "zg486gz5BZBn",
        "outputId": "1bb593a4-cf14-48dc-a2ff-773d856028e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.35569549781937626"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prediction_function(.18, 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7eYCgCRBZBn"
      },
      "source": [
        "**Now we start implementing gradient descent in plain python. Again the goal is to come up with same w1, w2 and bias that keras model calculated. We want to show how keras/tensorflow would have computed these values internally using gradient descent**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfxA22S_BZBn"
      },
      "source": [
        "**First write couple of helper routines such as sigmoid and log_loss**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s6YfoA_DBZBn",
        "outputId": "85bc6dbe-fc02-4bf1-d32d-7d5a0eb2a542"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.99999386, 0.5       , 0.73105858])"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def sigmoid_numpy(X):\n",
        "   return 1/(1+np.exp(-X))\n",
        "\n",
        "sigmoid_numpy(np.array([12,0,1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "giovdpH4BZBo"
      },
      "outputs": [],
      "source": [
        "def log_loss(y_true, y_predicted):\n",
        "    epsilon = 1e-15\n",
        "    y_predicted_new = [max(i,epsilon) for i in y_predicted]\n",
        "    y_predicted_new = [min(i,1-epsilon) for i in y_predicted_new]\n",
        "    y_predicted_new = np.array(y_predicted_new)\n",
        "    return -np.mean(y_true*np.log(y_predicted_new)+(1-y_true)*np.log(1-y_predicted_new))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bA9MjbgrBZBo"
      },
      "source": [
        "**All right now comes the time to implement our final gradient descent function !! yay !!!**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gfS1AKiSBZBo"
      },
      "outputs": [],
      "source": [
        "def gradient_descent(age, affordability, y_true, epochs, loss_thresold):\n",
        "    w1 = w2 = 1\n",
        "    bias = 0\n",
        "    rate = 0.5\n",
        "    n = len(age)\n",
        "    for i in range(epochs):\n",
        "        weighted_sum = w1 * age + w2 * affordability + bias\n",
        "        y_predicted = sigmoid_numpy(weighted_sum)\n",
        "        loss = log_loss(y_true, y_predicted)\n",
        "\n",
        "        w1d = (1/n)*np.dot(np.transpose(age),(y_predicted-y_true))\n",
        "        w2d = (1/n)*np.dot(np.transpose(affordability),(y_predicted-y_true))\n",
        "\n",
        "        bias_d = np.mean(y_predicted-y_true)\n",
        "        w1 = w1 - rate * w1d\n",
        "        w2 = w2 - rate * w2d\n",
        "        bias = bias - rate * bias_d\n",
        "\n",
        "        print (f'Epoch:{i}, w1:{w1}, w2:{w2}, bias:{bias}, loss:{loss}')\n",
        "\n",
        "        if loss<=loss_thresold:\n",
        "            break\n",
        "\n",
        "    return w1, w2, bias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "PxdVaqukBZBo",
        "outputId": "3027eca2-a1f6-4ccf-e414-4167f7c647c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:0, w1:0.974907633470177, w2:0.948348125394529, bias:-0.11341867736368583, loss:0.7113403233723417\n",
            "Epoch:1, w1:0.9556229728273669, w2:0.9058873696677865, bias:-0.2122349122718517, loss:0.681264778737757\n",
            "Epoch:2, w1:0.9416488476693794, w2:0.8719790823960313, bias:-0.2977578997796538, loss:0.6591474252715025\n",
            "Epoch:3, w1:0.9323916996249162, w2:0.8457541517722915, bias:-0.3715094724003511, loss:0.6431523291301917\n",
            "Epoch:4, w1:0.9272267472726993, w2:0.8262362885332687, bias:-0.43506643026891584, loss:0.6316873063379158\n",
            "Epoch:5, w1:0.9255469396815343, w2:0.8124402814952774, bias:-0.48994490058938817, loss:0.623471707997592\n",
            "Epoch:6, w1:0.9267936114129968, w2:0.8034375029757677, bias:-0.5375299543522853, loss:0.6175321183044205\n",
            "Epoch:7, w1:0.93047170420295, w2:0.7983920007454487, bias:-0.5790424270894963, loss:0.6131591858705934\n",
            "Epoch:8, w1:0.9361540784567942, w2:0.7965748796787705, bias:-0.6155315088627655, loss:0.6098518179750948\n",
            "Epoch:9, w1:0.9434791243557357, w2:0.7973647616854131, bias:-0.6478828179413606, loss:0.6072639970231438\n",
            "Epoch:10, w1:0.9521448361628082, w2:0.8002404280558159, bias:-0.6768343869109611, loss:0.6051606942838051\n",
            "Epoch:11, w1:0.9619014360798376, w2:0.8047697991276092, bias:-0.7029956527236098, loss:0.6033841405177724\n",
            "Epoch:12, w1:0.9725437902239876, w2:0.8105978078160995, bias:-0.7268665798879941, loss:0.6018292976282692\n",
            "Epoch:13, w1:0.9839042828641819, w2:0.8174345999952901, bias:-0.7488554155033402, loss:0.6004266142491015\n",
            "Epoch:14, w1:0.9958464546516588, w2:0.8250447751055391, bias:-0.769294418817745, loss:0.5991301804031037\n",
            "Epoch:15, w1:1.0082595007242081, w2:0.8332379503132499, bias:-0.7884533918133878, loss:0.5979097221992564\n",
            "Epoch:16, w1:1.0210536111133566, w2:0.8418606908070436, bias:-0.8065510935633605, loss:0.5967452540340851\n",
            "Epoch:17, w1:1.034156080666481, w2:0.8507897246509063, bias:-0.8237647415878959, loss:0.5956235357679642\n",
            "Epoch:18, w1:1.0475080939682688, w2:0.8599263049589658, bias:-0.8402378469114854, loss:0.5945357398126384\n",
            "Epoch:19, w1:1.0610620872868053, w2:0.8691915649552581, bias:-0.8560866317233403, loss:0.5934759216594547\n",
            "Epoch:20, w1:1.0747795953648005, w2:0.8785227146485403, bias:-0.8714052604012273, loss:0.5924400203169505\n",
            "Epoch:21, w1:1.0886295007650375, w2:0.8878699408379153, bias:-0.8862700880092956, loss:0.5914252065214394\n",
            "Epoch:22, w1:1.1025866146156011, w2:0.8971938890119441, bias:-0.9007431016492755, loss:0.5904294583596238\n",
            "Epoch:23, w1:1.1166305284975315, w2:0.9064636231844676, bias:-0.9148747025148294, loss:0.5894512852039241\n",
            "Epoch:24, w1:1.130744687166233, w2:0.9156549761872753, bias:-0.9287059516737735, loss:0.5884895481881011\n",
            "Epoch:25, w1:1.1449156405234902, w2:0.9247492176815199, bias:-0.9422703810055986, loss:0.5875433434377384\n",
            "Epoch:26, w1:1.1591324407175467, w2:0.9337319799254573, bias:-0.9555954523596437, loss:0.5866119260562087\n",
            "Epoch:27, w1:1.1733861565198167, w2:0.9425923921787205, bias:-0.9687037326284236, loss:0.5856946605640079\n",
            "Epoch:28, w1:1.1876694823356402, w2:0.9513223836938425, bias:-0.9816138397030203, loss:0.5847909885040025\n",
            "Epoch:29, w1:1.2019764234961199, w2:0.9599161227563919, bias:-0.994341203820797, loss:0.5839004071862915\n",
            "Epoch:30, w1:1.2163020429886013, w2:0.9683695654077998, bias:-1.006898680274023, loss:0.5830224556644095\n",
            "Epoch:31, w1:1.2306422576428844, w2:0.9766800925300213, bias:-1.0192970425003876, loss:0.5821567054089575\n",
            "Epoch:32, w1:1.244993674111329, w2:0.9848462180774323, bias:-1.0315453789434794, loss:0.5813027540359855\n",
            "Epoch:33, w1:1.2593534568600255, w2:0.9928673545729692, bias:-1.0436514125153917, loss:0.5804602210251408\n",
            "Epoch:34, w1:1.2737192219062514, w2:1.0007436246821175, bias:-1.0556217578155669, loss:0.5796287447370064\n",
            "Epoch:35, w1:1.2880889512619975, w2:1.0084757098569885, bias:-1.067462128294764, loss:0.57880798028165\n",
            "Epoch:36, w1:1.3024609240300042, w2:1.0160647288004139, bias:-1.079177503164993, loss:0.5779975979476322\n",
            "Epoch:37, w1:1.3168336608930384, w2:1.0235121399165894, bias:-1.0907722619345364, loss:0.5771972820026156\n",
            "Epoch:38, w1:1.3312058793761508, w2:1.0308196630555955, bias:-1.1022502929016078, loss:0.5764067297427645\n",
            "Epoch:39, w1:1.3455764577755067, w2:1.03798921677727, bias:-1.1136150806976406, loss:0.5756256507109632\n",
            "Epoch:40, w1:1.3599444060604624, w2:1.0450228680985851, bias:-1.124869776972519, loss:0.5748537660316542\n",
            "Epoch:41, w1:1.3743088423875545, w2:1.0519227922827652, bias:-1.1360172575115421, loss:0.5740908078281305\n",
            "Epoch:42, w1:1.3886689741318716, w2:1.0586912407061275, bias:-1.1470601684290498, loss:0.5733365186998219\n",
            "Epoch:43, w1:1.4030240825556768, w2:1.065330515222758, bias:-1.1580009635655026, loss:0.5725906512447178\n",
            "Epoch:44, w1:1.4173735104064382, w2:1.0718429477560005, bias:-1.1688419347984433, loss:0.5718529676170188\n",
            "Epoch:45, w1:1.4317166518748927, w2:1.0782308840940895, bias:-1.1795852366431911, loss:0.5711232391133308\n",
            "Epoch:46, w1:1.4460529444550507, w2:1.0844966710669584, bias:-1.1902329062502135, loss:0.5704012457828124\n",
            "Epoch:47, w1:1.4603818623375084, w2:1.0906426464418717, bias:-1.2007868796899672, loss:0.5696867760580651\n",
            "Epoch:48, w1:1.474702911039356, w2:1.0966711310047097, bias:-1.2112490052422353, loss:0.5689796264044564\n",
            "Epoch:49, w1:1.4890156230318012, w2:1.1025844223976542, bias:-1.2216210542672539, loss:0.5682796009861607\n",
            "Epoch:50, w1:1.503319554173139, w2:1.108384790367645, bias:-1.2319047301235464, loss:0.5675865113475955\n",
            "Epoch:51, w1:1.5176142807921111, w2:1.1140744731472638, bias:-1.2421016755069894, loss:0.5669001761092021\n",
            "Epoch:52, w1:1.5318993972968091, w2:1.1196556747438646, bias:-1.2522134785128962, loss:0.566220420676692\n",
            "Epoch:53, w1:1.546174514208496, w2:1.1251305629563741, bias:-1.2622416776643735, loss:0.5655470769630154\n",
            "Epoch:54, w1:1.5604392565392238, w2:1.1305012679742994, bias:-1.2721877661030871, loss:0.5648799831223866\n",
            "Epoch:55, w1:1.5746932624478314, w2:1.1357698814417572, bias:-1.2820531951006382, loss:0.5642189832957764\n",
            "Epoch:56, w1:1.5889361821215457, w2:1.1409384558921223, bias:-1.2918393770181935, loss:0.5635639273673236\n",
            "Epoch:57, w1:1.6031676768405996, w2:1.1460090044772484, bias:-1.3015476878174077, loss:0.562914670731162\n",
            "Epoch:58, w1:1.6173874181914885, w2:1.150983500930006, bias:-1.3111794692058374, loss:0.5622710740681858\n",
            "Epoch:59, w1:1.6315950874010996, w2:1.155863879710811, bias:-1.320736030484068, loss:0.5616330031323111\n",
            "Epoch:60, w1:1.6457903747692835, w2:1.1606520362984267, bias:-1.3302186501488862, loss:0.5610003285458062\n",
            "Epoch:61, w1:1.6599729791817353, w2:1.1653498275930778, bias:-1.3396285772964385, loss:0.5603729256032908\n",
            "Epoch:62, w1:1.6741426076885264, w2:1.1699590724061661, bias:-1.348967032860937, loss:0.5597506740840237\n",
            "Epoch:63, w1:1.6882989751364197, w2:1.1744815520159209, bias:-1.3582352107177078, loss:0.5591334580721091\n",
            "Epoch:64, w1:1.7024418038453646, w2:1.1789190107723873, bias:-1.3674342786739169, loss:0.5585211657842806\n",
            "Epoch:65, w1:1.7165708233213919, w2:1.1832731567384374, bias:-1.3765653793659074, loss:0.5579136894049259\n",
            "Epoch:66, w1:1.7306857699995972, w2:1.1875456623561378, bias:-1.385629631078514, loss:0.5573109249280388\n",
            "Epoch:67, w1:1.744786387012096, w2:1.1917381651299463, bias:-1.3946281284988502, loss:0.556712772005794\n",
            "Epoch:68, w1:1.7588724239767908, w2:1.1958522683199346, bias:-1.4035619434147377, loss:0.5561191338034575\n",
            "Epoch:69, w1:1.7729436368035707, w2:1.199889541639625, bias:-1.412432125366066, loss:0.5555299168603557\n",
            "Epoch:70, w1:1.786999787515191, w2:1.2038515219541523, bias:-1.4212397022558536, loss:0.55494503095664\n",
            "Epoch:71, w1:1.8010406440805895, w2:1.2077397139753707, bias:-1.4299856809265468, loss:0.5543643889855958\n",
            "Epoch:72, w1:1.815065980258806, w2:1.21155559095125, bias:-1.4386710477060998, loss:0.5537879068312537\n",
            "Epoch:73, w1:1.8290755754520085, w2:1.2153005953475013, bias:-1.4472967689275704, loss:0.5532155032510755\n",
            "Epoch:74, w1:1.8430692145663952, w2:1.2189761395198406, bias:-1.4558637914253105, loss:0.5526470997634942\n",
            "Epoch:75, w1:1.857046687879965, w2:1.2225836063756854, bias:-1.4643730430103006, loss:0.5520826205400979\n",
            "Epoch:76, w1:1.8710077909163219, w2:1.2261243500243837, bias:-1.472825432926746, loss:0.5515219923022593\n",
            "Epoch:77, w1:1.8849523243238275, w2:1.2295996964153224, bias:-1.4812218522917016, loss:0.5509651442220168\n",
            "Epoch:78, w1:1.898880093759531, w2:1.2330109439634602, bias:-1.4895631745192046, loss:0.5504120078270269\n",
            "Epoch:79, w1:1.9127909097773987, w2:1.2363593641619885, bias:-1.4978502557301687, loss:0.5498625169094129\n",
            "Epoch:80, w1:1.9266845877204517, w2:1.2396462021819508, bias:-1.5060839351490927, loss:0.5493166074383391\n",
            "Epoch:81, w1:1.9405609476164691, w2:1.2428726774587495, bias:-1.5142650354884937, loss:0.5487742174761587\n",
            "Epoch:82, w1:1.9544198140769822, w2:1.2460399842655545, bias:-1.5223943633218389, loss:0.5482352870979753\n",
            "Epoch:83, w1:1.968261016199314, w2:1.2491492922736824, bias:-1.5304727094456483, loss:0.5476997583144769\n",
            "Epoch:84, w1:1.9820843874714607, w2:1.252201747100074, bias:-1.538500849231359, loss:0.5471675749979029\n",
            "Epoch:85, w1:1.995889765679638, w2:1.2551984708420285, bias:-1.5464795429674643, loss:0.5466386828110088\n",
            "Epoch:86, w1:2.0096769928183367, w2:1.2581405625993878, bias:-1.554409536192387, loss:0.5461130291389041\n",
            "Epoch:87, w1:2.023445915002755, w2:1.2610290989843815, bias:-1.5622915600184968, loss:0.545590563023641\n",
            "Epoch:88, w1:2.037196382383489, w2:1.2638651346193672, bias:-1.5701263314476384, loss:0.5450712351014357\n",
            "Epoch:89, w1:2.0509282490633733, w2:1.2666497026227028, bias:-1.577914553678509, loss:0.5445549975424178\n",
            "Epoch:90, w1:2.0646413730163813, w2:1.2693838150830055, bias:-1.585656916406187, loss:0.5440418039927909\n",
            "Epoch:91, w1:2.0783356160084976, w2:1.2720684635220503, bias:-1.5933540961140944, loss:0.5435316095193146\n",
            "Epoch:92, w1:2.092010843520487, w2:1.2747046193465692, bias:-1.6010067563586592, loss:0.5430243705560023\n",
            "Epoch:93, w1:2.1056669246724873, w2:1.2772932342892063, bias:-1.6086155480469115, loss:0.5425200448529465\n",
            "Epoch:94, w1:2.1193037321503643, w2:1.2798352408388918, bias:-1.6161811097072503, loss:0.5420185914271816\n",
            "Epoch:95, w1:2.1329211421337653, w2:1.282331552660889, bias:-1.62370406775359, loss:0.5415199705154982\n",
            "Epoch:96, w1:2.1465190342258187, w2:1.2847830650067702, bias:-1.6311850367430925, loss:0.5410241435291299\n",
            "Epoch:97, w1:2.1600972913844236, w2:1.2871906551145709, bias:-1.638624619627673, loss:0.5405310730102341\n",
            "Epoch:98, w1:2.173655799855084, w2:1.2895551825993699, bias:-1.6460234079994696, loss:0.5400407225900913\n",
            "Epoch:99, w1:2.1871944491052364, w2:1.2918774898345378, bias:-1.6533819823304428, loss:0.5395530569489538\n",
            "Epoch:100, w1:2.200713131760032, w2:1.2941584023238903, bias:-1.6607009122062801, loss:0.5390680417774752\n",
            "Epoch:101, w1:2.2142117435395288, w2:1.2963987290649803, bias:-1.667980756554761, loss:0.5385856437396539\n",
            "Epoch:102, w1:2.227690183197251, w2:1.298599262903754, bias:-1.675222063868738, loss:0.5381058304372313\n",
            "Epoch:103, w1:2.241148352460086, w2:1.300760780880796, bias:-1.6824253724238831, loss:0.5376285703754836\n",
            "Epoch:104, w1:2.2545861559694704, w2:1.302884044569378, bias:-1.68959121049134, loss:0.5371538329303491\n",
            "Epoch:105, w1:2.268003501223843, w2:1.3049698004055226, bias:-1.696720096545423, loss:0.5366815883168389\n",
            "Epoch:106, w1:2.281400298522321, w2:1.30701878001029, bias:-1.7038125394664934, loss:0.5362118075586769\n",
            "Epoch:107, w1:2.294776460909571, w2:1.3090317005044871, bias:-1.7108690387391425, loss:0.5357444624591193\n",
            "Epoch:108, w1:2.308131904121845, w2:1.3110092648159952, bias:-1.717890084645807, loss:0.5352795255729065\n",
            "Epoch:109, w1:2.3214665465341486, w2:1.3129521619799067, bias:-1.7248761584559342, loss:0.5348169701793002\n",
            "Epoch:110, w1:2.334780309108516, w2:1.3148610674316565, bias:-1.7318277326108187, loss:0.5343567702561651\n",
            "Epoch:111, w1:2.3480731153433556, w2:1.3167366432933292, bias:-1.7387452709042173, loss:0.5338989004550466\n",
            "Epoch:112, w1:2.361344891223853, w2:1.318579538653317, bias:-1.7456292286588566, loss:0.5334433360772121\n",
            "Epoch:113, w1:2.3745955651733888, w2:1.3203903898394993, bias:-1.752480052898938, loss:0.5329900530506113\n",
            "Epoch:114, w1:2.387825068005961, w2:1.3221698206861112, bias:-1.759298182518741, loss:0.532539027907722\n",
            "Epoch:115, w1:2.4010333328795768, w2:1.3239184427944621, bias:-1.7660840484474298, loss:0.5320902377642434\n",
            "Epoch:116, w1:2.4142202952505927, w2:1.3256368557876617, bias:-1.772838073810153, loss:0.5316436602986057\n",
            "Epoch:117, w1:2.427385892828985, w2:1.3273256475595065, bias:-1.7795606740855376, loss:0.5311992737322607\n",
            "Epoch:118, w1:2.440530065534521, w2:1.328985394517677, bias:-1.7862522572596642, loss:0.5307570568107249\n",
            "Epoch:119, w1:2.4536527554538115, w2:1.3306166618213877, bias:-1.7929132239766132, loss:0.5303169887853427\n",
            "Epoch:120, w1:2.46675390679823, w2:1.332220003613633, bias:-1.7995439676856702, loss:0.5298790493957434\n",
            "Epoch:121, w1:2.479833465862666, w2:1.3337959632481655, bias:-1.8061448747852695, loss:0.5294432188529623\n",
            "Epoch:122, w1:2.4928913809851023, w2:1.3353450735113397, bias:-1.8127163247637639, loss:0.5290094778232023\n",
            "Epoch:123, w1:2.505927602506996, w2:1.336867856838949, bias:-1.8192586903370926, loss:0.5285778074122077\n",
            "Epoch:124, w1:2.518942082734439, w2:1.3383648255281848, bias:-1.82577233758343, loss:0.5281481891502279\n",
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          ]
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        {
          "name": "stdout",
          "output_type": "stream",
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            "Epoch:361, w1:5.00966212989009, w2:1.4555970892403576, bias:-2.9415522492234514, loss:0.46375862615603475\n",
            "Epoch:362, w1:5.0179700269326, w2:1.45587330578711, bias:-2.9451849214244237, loss:0.46359358472018636\n",
            "Epoch:363, w1:5.026262488012782, w2:1.456149647855258, bias:-2.94881136056773, loss:0.4634291480718573\n",
            "Epoch:364, w1:5.0345395499882475, w2:1.456426118261257, bias:-2.952431584207713, loss:0.46326531361545026\n",
            "Epoch:365, w1:5.042801249636176, w2:1.4567027197201048, bias:-2.956045609784402, loss:0.46310207876795667\n",
            "Epoch:366, w1:5.051047623653049, w2:1.4569794548473887, bias:-2.9596534546250037, loss:0.46293944095888917\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(5.051047623653049, 1.4569794548473887, -2.9596534546250037)"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gradient_descent(X_train_scaled['age'],X_train_scaled['affordibility'],y_train,1000, 0.4631)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E0AZ7QiABZBo",
        "outputId": "796a44e1-2448-4ef9-d3f7-b40588d9bce7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([[5.060867 ],\n",
              "        [1.4086502]], dtype=float32),\n",
              " array([-2.9137027], dtype=float32))"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "coef, intercept"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqP5707BBZBo"
      },
      "source": [
        "**This shows that in the end we were able to come up with same value of w1,w2 and bias using a plain python implementation of gradient descent function**"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "provenance": []
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  "nbformat": 4,
  "nbformat_minor": 0
}