model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%matplotlit` not found.\n"
     ]
    }
   ],
   "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",
    "%matplotlit inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
       "      <th>bought_insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"insurance_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 3)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)\n",
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\n",
    "\n",
    "X_train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 5ms/step - loss: 0.6871 - accuracy: 0.5000\n",
      "Epoch 72/5000\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 0.4631 - accuracy: 0.9091\n",
      "Epoch 5000/5000\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4631 - accuracy: 0.9091\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x19cf78ae210>"
      ]
     },
     "execution_count": 44,
     "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',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(X_train_scaled, y_train, epochs=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 144ms/step - loss: 0.3550 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3549776077270508, 1.0]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>2</th>\n",
       "      <td>0.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.28</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "14  0.49              1\n",
       "9   0.61              1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 88ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.7054848 ],\n",
       "       [0.3556957 ],\n",
       "       [0.16827832],\n",
       "       [0.47801185],\n",
       "       [0.7260696 ],\n",
       "       [0.8294983 ]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[5.060863 ],\n",
       "        [1.4086521]], dtype=float32),\n",
       " array([-2.913703], dtype=float32))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef, intercept = model.get_weights()\n",
    "coef, intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```Doing everything manually```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999847700205"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.705484819775958"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prediction_function(age, affordibility):\n",
    "    y = coef[0]*age + coef[1]*affordibility + intercept\n",
    "    return sigmoid(y)\n",
    "prediction_function(.47, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_loss(y_true, y_pred):\n",
    "    epsilon = 1e-15\n",
    "    y_pred_new = [max(i, epsilon) for i in y_pred]\n",
    "    y_pred_new = [min(i, 1-epsilon) for i in y_pred_new]\n",
    "    y_pred_new = np.array(y_pred_new)\n",
    "    return -np.mean(y_true*np.log(y_pred_new) + (1-y_true)*np.log(1-y_pred_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99999386, 0.5       , 0.73105858])"
      ]
     },
     "execution_count": 53,
     "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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "class myNN:\n",
    "    def __init__(self):\n",
    "        self.w1 = 1\n",
    "        self.w2 = 1\n",
    "        self.bias = 0\n",
    "        \n",
    "    def fit(self, X, y, epochs, threshold):\n",
    "        self.w1, self.w2, self.bias = self.gradient_descent(X['age'], X['affordibility'], y, epochs, threshold)\n",
    "\n",
    "    def predict(self, X_test):\n",
    "        weighted_sum = self.w1 * X_test['age'] + self.w2 * X_test['affordibility'] + self.bias\n",
    "        return sigmoid_numpy(weighted_sum)\n",
    "\n",
    "    def gradient_descent(self, age, affordibility, y_true, epochs, threshold):\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 * affordibility + 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(affordibility), (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",
    "            if(i%50 == 0):\n",
    "                print(f'Epoch: {i}, w1: {w1}, w2: {w2}, bias: {bias}, loss: {loss}')\n",
    "\n",
    "            if(loss <= threshold):\n",
    "                print(f'Epoch: {i}, w1: {w1}, w2: {w2}, bias: {bias}, loss: {loss}')\n",
    "                break\n",
    "        \n",
    "        return w1, w2, bias\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, w1: 0.974907633470177, w2: 0.948348125394529, bias: -0.11341867736368583, loss: 0.7113403233723417\n",
      "Epoch: 50, w1: 1.503319554173139, w2: 1.108384790367645, bias: -1.2319047301235464, loss: 0.5675865113475955\n",
      "Epoch: 100, w1: 2.200713131760032, w2: 1.2941584023238903, bias: -1.6607009122062801, loss: 0.5390680417774752\n",
      "Epoch: 150, w1: 2.8495727769689085, w2: 1.3696895491572745, bias: -1.986105845859897, loss: 0.5176462164249294\n",
      "Epoch: 200, w1: 3.443016970881803, w2: 1.4042218624465033, bias: -2.2571369883752723, loss: 0.5005011269691375\n",
      "Epoch: 250, w1: 3.982450494649576, w2: 1.4239127329321233, bias: -2.494377365971801, loss: 0.48654089537617085\n",
      "Epoch: 300, w1: 4.472179522095915, w2: 1.438787986553552, bias: -2.707387811922373, loss: 0.4750814640632793\n",
      "Epoch: 350, w1: 4.917245868007634, w2: 1.4525660781176122, bias: -2.901176333556766, loss: 0.46561475306999006\n",
      "Epoch: 366, w1: 5.051047623653049, w2: 1.4569794548473887, bias: -2.9596534546250037, loss: 0.46293944095888917\n"
     ]
    }
   ],
   "source": [
    "custom_model = myNN()\n",
    "custom_model.fit(X_train_scaled, y_train, epochs=500, threshold=0.4631)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[5.060863 ],\n",
       "        [1.4086521]], dtype=float32),\n",
       " array([-2.913703], dtype=float32))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef, intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     0.705020\n",
       "10    0.355836\n",
       "21    0.161599\n",
       "11    0.477919\n",
       "14    0.725586\n",
       "9     0.828987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 52ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.7054848 ],\n",
       "       [0.3556957 ],\n",
       "       [0.16827832],\n",
       "       [0.47801185],\n",
       "       [0.7260696 ],\n",
       "       [0.8294983 ]], dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```Results are almost same```"
   ]
  }
 ],
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