4_gradient_descent_model.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "id": "d8c54991-a892-41a7-a86f-39b925de0b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2004243e-0126-4286-8e36-211ac4b85d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>age</th>\n",
       "      <th>affordibility</th>\n",
       "      <th>bought_insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>46</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"insurance_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "064782d9-a8e1-41f0-8a68-48ccd87cb445",
   "metadata": {},
   "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=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "656b3592-2de5-4877-9f53-ceb131d0c35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1201ada-b293-457a-821e-50cf8eb00716",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since ages are 1 to 100, ages will be divided to 100 in order to scale them\n",
    "\n",
    "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": "code",
   "execution_count": 10,
   "id": "ce7a8aaf-e3e8-4c1e-ae55-9cde87d32c84",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>affordibility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.56</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.45</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
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       "      <th>27</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  affordibility\n",
       "16  0.25              0\n",
       "6   0.55              0\n",
       "3   0.52              0\n",
       "21  0.26              0\n",
       "26  0.23              1\n",
       "25  0.54              1\n",
       "12  0.27              0\n",
       "4   0.46              1\n",
       "10  0.18              1\n",
       "5   0.56              1\n",
       "20  0.21              1\n",
       "2   0.47              1\n",
       "7   0.60              0\n",
       "24  0.50              1\n",
       "23  0.45              1\n",
       "18  0.19              0\n",
       "11  0.28              1\n",
       "22  0.40              1\n",
       "27  0.46              1\n",
       "13  0.29              0\n",
       "15  0.55              1\n",
       "8   0.62              1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eda37997-2000-4df5-866f-3a41f481d113",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n",
      "Epoch 1/5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-11 12:03:35.067686: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2023-01-11 12:03:35.067936: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2023-01-11 12:03:35.279319: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2023-01-11 12:03:35.425384: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step - loss: 0.7146 - accuracy: 0.5000\n",
      "Epoch 2/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7142 - accuracy: 0.5000\n",
      "Epoch 3/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7138 - accuracy: 0.5000\n",
      "Epoch 4/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7135 - accuracy: 0.5000\n",
      "Epoch 5/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7131 - accuracy: 0.5000\n",
      "Epoch 6/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7127 - accuracy: 0.5000\n",
      "Epoch 7/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7123 - accuracy: 0.5000\n",
      "Epoch 8/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.7119 - accuracy: 0.5000\n",
      "Epoch 9/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7116 - accuracy: 0.5000\n",
      "Epoch 10/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7112 - accuracy: 0.5000\n",
      "Epoch 11/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7108 - accuracy: 0.5000\n",
      "Epoch 12/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7104 - accuracy: 0.5000\n",
      "Epoch 13/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7101 - accuracy: 0.5000\n",
      "Epoch 14/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.7097 - accuracy: 0.5000\n",
      "Epoch 15/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7093 - accuracy: 0.5000\n",
      "Epoch 16/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7090 - accuracy: 0.5000\n",
      "Epoch 17/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.7086 - accuracy: 0.5000\n",
      "Epoch 18/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7082 - accuracy: 0.5000\n",
      "Epoch 19/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.7079 - accuracy: 0.5000\n",
      "Epoch 20/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7075 - accuracy: 0.5000\n",
      "Epoch 21/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7071 - accuracy: 0.5000\n",
      "Epoch 22/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7068 - accuracy: 0.5000\n",
      "Epoch 23/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7064 - accuracy: 0.5000\n",
      "Epoch 24/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7060 - accuracy: 0.5000\n",
      "Epoch 25/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7057 - accuracy: 0.5000\n",
      "Epoch 26/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7053 - accuracy: 0.5000\n",
      "Epoch 27/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7049 - accuracy: 0.5000\n",
      "Epoch 28/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7046 - accuracy: 0.5000\n",
      "Epoch 29/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7042 - accuracy: 0.5000\n",
      "Epoch 30/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.7039 - accuracy: 0.5000\n",
      "Epoch 31/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7035 - accuracy: 0.5000\n",
      "Epoch 32/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7032 - accuracy: 0.5000\n",
      "Epoch 33/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.7028 - accuracy: 0.5000\n",
      "Epoch 34/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.7025 - accuracy: 0.5000\n",
      "Epoch 35/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7021 - accuracy: 0.5000\n",
      "Epoch 36/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.7018 - accuracy: 0.5000\n",
      "Epoch 37/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7014 - accuracy: 0.5000\n",
      "Epoch 38/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.7011 - accuracy: 0.5000\n",
      "Epoch 39/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7007 - accuracy: 0.5000\n",
      "Epoch 40/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.7004 - accuracy: 0.5000\n",
      "Epoch 41/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.7000 - accuracy: 0.5000\n",
      "Epoch 42/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.6997 - accuracy: 0.5000\n",
      "Epoch 43/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6993 - accuracy: 0.5000\n",
      "Epoch 44/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6990 - accuracy: 0.5000\n",
      "Epoch 45/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.6986 - accuracy: 0.5000\n",
      "Epoch 46/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.6983 - accuracy: 0.5000\n",
      "Epoch 47/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6980 - accuracy: 0.5000\n",
      "Epoch 48/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6976 - accuracy: 0.5000\n",
      "Epoch 49/5000\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.6973 - accuracy: 0.5000\n",
      "Epoch 50/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6970 - accuracy: 0.5000\n",
      "Epoch 51/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6966 - accuracy: 0.5000\n",
      "Epoch 52/5000\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.6963 - accuracy: 0.5000\n",
      "Epoch 53/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.6960 - accuracy: 0.5000\n",
      "Epoch 54/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.6956 - accuracy: 0.5000\n",
      "Epoch 55/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.6953 - accuracy: 0.5000\n",
      "Epoch 56/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6950 - accuracy: 0.5000\n",
      "Epoch 57/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6946 - accuracy: 0.5000\n",
      "Epoch 58/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6943 - accuracy: 0.5000\n",
      "Epoch 59/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6940 - accuracy: 0.5000\n",
      "Epoch 60/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6937 - accuracy: 0.5000\n",
      "Epoch 61/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6933 - accuracy: 0.5000\n",
      "Epoch 62/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6930 - accuracy: 0.5000\n",
      "Epoch 63/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.6927 - accuracy: 0.5000\n",
      "Epoch 64/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6924 - accuracy: 0.5000\n",
      "Epoch 65/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6921 - accuracy: 0.5000\n",
      "Epoch 66/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.6917 - accuracy: 0.5000\n",
      "Epoch 67/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6914 - accuracy: 0.5000\n",
      "Epoch 68/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6911 - accuracy: 0.5000\n",
      "Epoch 69/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6908 - accuracy: 0.5000\n",
      "Epoch 70/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6905 - accuracy: 0.5000\n",
      "Epoch 71/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6902 - accuracy: 0.5000\n",
      "Epoch 72/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6899 - accuracy: 0.5000\n",
      "Epoch 73/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6896 - accuracy: 0.5000\n",
      "Epoch 74/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6892 - accuracy: 0.5000\n",
      "Epoch 75/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6889 - accuracy: 0.5000\n",
      "Epoch 76/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6886 - accuracy: 0.5000\n",
      "Epoch 77/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6883 - accuracy: 0.5000\n",
      "Epoch 78/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6880 - accuracy: 0.5000\n",
      "Epoch 79/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6877 - accuracy: 0.5000\n",
      "Epoch 80/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.6874 - accuracy: 0.5000\n",
      "Epoch 81/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.6871 - accuracy: 0.5000\n",
      "Epoch 82/5000\n",
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      "Epoch 1080/5000\n",
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      "Epoch 2021/5000\n",
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      "Epoch 2022/5000\n",
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      "Epoch 2028/5000\n",
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      "Epoch 2030/5000\n",
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      "Epoch 2031/5000\n",
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      "Epoch 2040/5000\n",
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      "Epoch 2068/5000\n",
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      "Epoch 2069/5000\n",
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      "Epoch 2070/5000\n",
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      "Epoch 2075/5000\n",
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      "Epoch 2084/5000\n",
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      "Epoch 2099/5000\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 0.5015 - accuracy: 0.8182\n",
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      "Epoch 3906/5000\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4944/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4945/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4946/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4947/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.9091\n",
      "Epoch 4948/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4949/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4950/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4951/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4952/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.9091\n",
      "Epoch 4953/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4954/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4955/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4956/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4957/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4958/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.9091\n",
      "Epoch 4959/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4960/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4961/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4962/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4963/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.9091\n",
      "Epoch 4964/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4965/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4966/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4967/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4968/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4969/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4787 - accuracy: 0.9091\n",
      "Epoch 4970/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4971/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4972/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4973/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4974/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.9091\n",
      "Epoch 4975/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4976/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4977/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4978/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4979/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4980/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4785 - accuracy: 0.9091\n",
      "Epoch 4981/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4982/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4983/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4984/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4985/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.9091\n",
      "Epoch 4986/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4987/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4988/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4989/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4990/5000\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4783 - accuracy: 0.9091\n",
      "Epoch 4991/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4992/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4993/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4994/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4995/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4996/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.9091\n",
      "Epoch 4997/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n",
      "Epoch 4998/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n",
      "Epoch 4999/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n",
      "Epoch 5000/5000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.9091\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x295462f10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kernel_initializer refers initial weights\n",
    "\n",
    "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\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(X_train_scaled, y_train, epochs=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41b34157-c51e-409f-bb87-ecd040205ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 100ms/step - loss: 0.3153 - accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-11 12:07:06.849087: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3152589201927185, 1.0]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27ad9feb-80e4-4f50-beba-b231b97290a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>affordibility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  affordibility\n",
       "1   0.25              0\n",
       "0   0.22              1\n",
       "14  0.49              1\n",
       "9   0.61              1\n",
       "19  0.18              1\n",
       "17  0.58              1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ccf577f-0b3b-4dbe-abe8-92ee63c3db0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 43ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-11 12:08:09.337055: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.16282065],\n",
       "       [0.41183808],\n",
       "       [0.72550565],\n",
       "       [0.82668287],\n",
       "       [0.365132  ],\n",
       "       [0.80450743]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c1a2489-f973-417c-930f-b6e60735ea55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[4.9196653],\n",
       "        [1.4286071]], dtype=float32),\n",
       " array([-2.8673053], dtype=float32))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weights and bias\n",
    "coef, intercept = model.get_weights()\n",
    "coef, intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "073b0669-1b63-4dc0-bb46-9e9d5d9d27ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# implement the last neuron without tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa0cf6d1-42d6-4a5e-901d-22f1cfe504dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999847700205"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "id": "36142211-1a27-45b7-82b1-be52f95e8a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction_function(age, affordibility):\n",
    "    weighted_sum = coef[0]*age + coef[1]*affordibility + intercept\n",
    "    return sigmoid(weighted_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "162747d5-6ac7-447d-969f-14a8d936d53b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7054827391901236"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_function(.47,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "214ba494-3654-465a-9a6f-2d5e0646dfa0",
   "metadata": {},
   "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": "code",
   "execution_count": 22,
   "id": "68bdcbc3-17f3-4494-bc0a-3e849fd1188f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99999386, 0.5       , 0.73105858])"
      ]
     },
     "execution_count": 22,
     "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": 25,
   "id": "076d529d-2bed-4602-b761-ada062b27114",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent(age, affordability, y_true, epochs, loss_threshold):\n",
    "    # w1, w2, bias\n",
    "    w1 = w2 = 1\n",
    "    bias = 0\n",
    "    rate = 0.5\n",
    "    n = len(age)\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        weighted_sum = w1*age + w2*affordability + bias\n",
    "        y_predicted = sigmoid_numpy(weighted_sum)\n",
    "        \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",
    "        bias_d = np.mean(y_predicted-y_true)\n",
    "        \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_threshold:\n",
    "            break\n",
    "        \n",
    "    return w1, w2, bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35c1ae47-0f75-4be7-b222-0953b2f69a55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:0, W1:0.973391914875179, W2:0.9484790550524685, Bias:-0.1133436520447742, loss:0.714610156590683\n",
      "Epoch:1, W1:0.9526050112606814, W2:0.9061888855737805, Bias:-0.21202325373024944, loss:0.6844545754122794\n",
      "Epoch:2, W1:0.9371389216394038, W2:0.8724863718378795, Bias:-0.297354777348674, loss:0.6623114062843333\n",
      "Epoch:3, W1:0.9263972300850559, W2:0.846496403485574, Bias:-0.3708685783767047, loss:0.6463299135248587\n",
      "Epoch:4, W1:0.9197530021886515, W2:0.8272363836850968, Bias:-0.4341501414207025, loss:0.6349055537023248\n",
      "Epoch:5, W1:0.9165978234998884, W2:0.8137152861544186, Bias:-0.48872346395231225, loss:0.6267483980102484\n",
      "Epoch:6, W1:0.9163723575035057, W2:0.804999539668341, Bias:-0.535980249721865, loss:0.6208785684684357\n",
      "Epoch:7, W1:0.9185813941347364, W2:0.8002492021116546, Bias:-0.577146673201612, loss:0.6165824439197473\n",
      "Epoch:8, W1:0.9227979840062284, W2:0.7987322635165252, Bias:-0.6132761019356343, loss:0.6133562082414478\n",
      "Epoch:9, W1:0.9286609028533963, W2:0.7998249643661494, Bias:-0.645257370024138, loss:0.6108521448174768\n",
      "Epoch:10, W1:0.9358686190488851, W2:0.8030042884051622, Bias:-0.6738309657576457, loss:0.6088341776838265\n",
      "Epoch:11, W1:0.9441718469380087, W2:0.8078368096906972, Bias:-0.699608196742467, loss:0.6071438947291015\n",
      "Epoch:12, W1:0.9533659204718994, W2:0.8139664580193542, Bias:-0.7230904542297271, loss:0.6056758621053777\n",
      "Epoch:13, W1:0.9632836449771243, W2:0.8211026351142845, Bias:-0.7446870773611227, loss:0.6043602825252016\n",
      "Epoch:14, W1:0.9737889259533272, W2:0.8290093912698744, Bias:-0.7647311653649123, loss:0.6031510906563414\n",
      "Epoch:15, W1:0.9847712657290106, W2:0.8374959415161981, Bias:-0.7834931699835671, loss:0.6020179120220727\n",
      "Epoch:16, W1:0.9961411069340184, W2:0.8464085600623791, Bias:-0.8011923559334373, loss:0.600940693662132\n",
      "Epoch:17, W1:1.0078259477418539, W2:0.8556237683517071, Bias:-0.818006336657052, loss:0.5999061483592272\n",
      "Epoch:18, W1:1.0197671329684783, W2:0.8650426771317322, Bias:-0.8340789351359146, loss:0.598905413935753\n",
      "Epoch:19, W1:1.031917222235739, W2:0.8745863263481679, Bias:-0.8495266209383467, loss:0.5979325190876422\n",
      "Epoch:20, W1:1.0442378425785437, W2:0.8841918703080089, Bias:-0.8644437559243406, loss:0.5969833809878354\n",
      "Epoch:21, W1:1.0566979429974126, W2:0.8938094688922846, Bias:-0.8789068539279128, loss:0.5960551517820798\n",
      "Epoch:22, W1:1.069272379734157, W2:0.9033997626998168, Bias:-0.8929780306777588, loss:0.5951457931712361\n",
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      "Epoch:376, W1:4.886976648891136, W2:1.4755334364855204, Bias:-2.902229339162611, loss:0.4783942628808729\n",
      "Epoch:377, W1:4.894912560686863, W2:1.4757780951712576, Bias:-2.9056783978436656, loss:0.4782440153523897\n",
      "Epoch:378, W1:4.902834960491424, W2:1.476022916232298, Bias:-2.909122064576124, loss:0.478094272032023\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.902834960491424, 1.476022916232298, -2.909122064576124)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient_descent(X_train_scaled[\"age\"],X_train_scaled[\"affordibility\"],y_train,1000,0.4781)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3a2b458c-0ef5-4679-aa0c-2041b1445c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[4.9196653],\n",
       "        [1.4286071]], dtype=float32),\n",
       " array([-2.8673053], dtype=float32))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef, intercept"
   ]
  }
 ],
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