Notebooks/03_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ddef724",
   "metadata": {},
   "source": [
    "## <span style=\"color : green\"> Implementing Regression Models </span>\n",
    "\n",
    "# <center> Table of Contents </center>\n",
    "\n",
    "1. Computing $b0$ and $b1$ in the simple linear equation and predict the $Ypred$\n",
    "1. Compute the intercepts and coefficients of independent variable in MLP and predict the $Ypred$\n",
    "2. Plot the regression line \n",
    "1. Evaluate the model using Mean Squared Error, R-Squared score\n",
    "1. Train a logistic model and evaluate using recall, precision, accuracy and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f873ddc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score, \\\n",
    "        recall_score, precision_score, accuracy_score, \\\n",
    "        confusion_matrix, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee8b5613",
   "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>diagnosis_result</th>\n",
       "      <th>radius</th>\n",
       "      <th>texture</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>area</th>\n",
       "      <th>smoothness</th>\n",
       "      <th>compactness</th>\n",
       "      <th>symmetry</th>\n",
       "      <th>fractal_dimension</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>151</td>\n",
       "      <td>954</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>133</td>\n",
       "      <td>1326</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>130</td>\n",
       "      <td>1203</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>78</td>\n",
       "      <td>386</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>135</td>\n",
       "      <td>1297</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis_result  radius  texture  perimeter  area  smoothness  \\\n",
       "id                                                                  \n",
       "1                 M      23       12        151   954       0.143   \n",
       "2                 B       9       13        133  1326       0.143   \n",
       "3                 M      21       27        130  1203       0.125   \n",
       "4                 M      14       16         78   386       0.070   \n",
       "5                 M       9       19        135  1297       0.141   \n",
       "\n",
       "    compactness  symmetry  fractal_dimension  \n",
       "id                                            \n",
       "1         0.278     0.242              0.079  \n",
       "2         0.079     0.181              0.057  \n",
       "3         0.160     0.207              0.060  \n",
       "4         0.284     0.260              0.097  \n",
       "5         0.133     0.181              0.059  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../Datasets/Prostate_Cancer.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d30b8a7",
   "metadata": {},
   "source": [
    "### Single Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "926eb4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['radius'].values\n",
    "y = df['diagnosis_result']\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "labels = encoder.fit_transform(y)\n",
    "\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "df203b76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(xtrain.reshape(-1,1), ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0c407587",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0 (intercept)- 0.9186344164664089 \n"
     ]
    }
   ],
   "source": [
    "# Intercept\n",
    "\n",
    "print(f'b0 (intercept)- {model.intercept_} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "16803226",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b1 (Co-efficient)- -0.02076585518754571 \n"
     ]
    }
   ],
   "source": [
    "# Co-efficient\n",
    "\n",
    "print(f'b1 (Co-efficient)- {model.coef_[0]} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "36d16df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qZ68MAbdm5qMRcVl1fNPj5pKk7mjpXi6ZeTdwd82wukWemRfvfyxJ0mR5pagklYSFLkklYaFLUklY6JJUEha6JJWEhS5JJWGhS1JJWOiSVBIWuiSVhIUuSSVhoUtSSVjoklQSFroklYSFLkklYaFLUklY6JJUEha6JJWEhS5JJWGhS1JJWOiSVBIWuiSVhIUuSSVhoUtSSVjoklQSFroklYSFLkklYaFLUklY6JJUEha6JJWEhS5JJdFSoUfE6RGxKSI2R8RVdcZfEBHfrb7uj4jj2x9VktTMhIUeEUPALcAZwCLg/IhYVDPZfwC/lJnvAK4HVrQ7qCSpuVb20E8ENmfm45m5C1gFnD12gsy8PzOfq759AJjX3piSpIm0UuhzgS1j3m+tDmvkN4Bv1hsREZdGxEhEjIyOjraeUpI0oVYKPeoMy7oTRpxCpdA/WW98Zq7IzOHMHJ4zZ07rKSVJE5rewjRbgflj3s8DttdOFBHvAL4EnJGZz7QnniSpVa3soT8ELIyIIyPiQOA84K6xE0TEEcBq4MLM/H77Y0qSJjLhHnpmvhwRS4C1wBBwa2Y+GhGXVccvB64F3gx8MSIAXs7M4c7FliTVisy6h8M7bnh4OEdGRnry2ZI0qCJiXaMdZq8UlaSSsNAlqSQsdEkqCQtdkkrCQpekkrDQJakkLHRJKgkLXZJKwkKXpJKw0CWpJCx0SSoJC12SSsJCl6SSsNAlqSQsdEkqCQtdkkrCQpekkrDQJakkLHRJKgkLXZJKwkKXpJKw0CWpJCx0SSoJC12SSsJCl6SSsNAlqSQsdEkqCQtdkkrCQpekkrDQJakkLHRJKonprUwUEacDfw4MAV/KzD+uGR/V8WcCPwEuzsyH25y1Y9as38aytZvY/vxODp81g6WnHc3id83tdaxScN12jutWtSYs9IgYAm4BfhnYCjwUEXdl5mNjJjsDWFh9nQT8RfVn31uzfhtXr97Azt17ANj2/E6uXr0BwI1jP7luO8d1q3paOeRyIrA5Mx/PzF3AKuDsmmnOBm7LigeAWRFxWJuzdsSytZte3Sj22rl7D8vWbupRovJw3XaO61b1tFLoc4EtY95vrQ6b7DRExKURMRIRI6Ojo5PN2hHbn985qeFqneu2c1y3qqeVQo86w3IfpiEzV2TmcGYOz5kzp5V8HXf4rBmTGq7WuW47x3Wrelop9K3A/DHv5wHb92GavrT0tKOZccDQuGEzDhhi6WlH9yhRebhuO8d1q3paOcvlIWBhRBwJbAPOAz5SM81dwJKIWEXly9D/ysyn2pq0Q/Z+geTZAu3nuu0c163qiczCkZHiRBFnAn9G5bTFWzPzMxFxGUBmLq+etngzcDqV0xYvycyRZsscHh7OkZGmk0iSakTEuswcrjeupfPQM/Nu4O6aYcvH/J7A5fsTUpK0f7xSVJJKwkKXpJKw0CWpJCx0SSqJls5y6cgHR4wCT/TkwxubDezodYhJGKS8g5QVBivvIGWFwcrbj1nflpl1r8zsWaH3o4gYaXQ6UD8apLyDlBUGK+8gZYXByjtIWcFDLpJUGha6JJWEhT7eil4HmKRByjtIWWGw8g5SVhisvIOU1WPoklQW7qFLUklY6JJUElO20CPi1oh4OiK+N2bYmyLinoj4QfXnIb3MuFeDrMsi4v9GxHcj4usRMauHEcepl3fMuP8VERkRs3uRrVajrBFxRURsiohHI+KzvcpXq8G/hXdGxAMR8Uj1iWAn9jLjXhExPyLui4iN1fX4O9XhfbedNcnat9tZPVO20IGVVG73O9ZVwD9l5kLgn6rv+8FKilnvAY7LzHcA3weu7naoJlZSzEtEzKfysPEnux2oiZXUZI2IU6g8J/cdmXks8Lke5GpkJcV1+1ng05n5TuDa6vt+8DJwZWYeA7wXuDwiFtGf21mjrP28nRVM2ULPzG8Bz9YMPhv4cvX3LwOLu5mpkXpZM/MfM/Pl6tsHqDwlqi80WLcANwKfoM7jCXulQdbfBv44M1+qTvN014M10CBvAj9V/f2N9MnTwjLzqcx8uPr7j4CNVJ413HfbWaOs/byd1TNlC72Bt+x90lL156E9ztOq/wl8s9chmomIs4BtmfmdXmdpwVHAL0bEgxHxLxHxnl4HmsDHgWURsYXKXxN9txcZEQuAdwEP0ufbWU3Wsfp+O7PQB1xEXEPlz8W/6XWWRiLiIOAaKocDBsF04BAqf3ovBe6oPpWrX/028LuZOR/4XeCvepxnnIg4GPga8PHMfKHXeZpplHUQtjOw0Gv9MCIOA6j+7Js/teuJiI8CHwIuyP6+oOB/AEcC34mI/6TyZ+vDEfHWnqZqbCuwOiu+DbxC5SZN/eqjwOrq7/8H6IsvRQEi4gAqBfk3mbk3Y19uZw2yDtJ2ZqHXuIvKxkH15509zNJURJwOfBI4KzN/0us8zWTmhsw8NDMXZOYCKoV5Qmb+vx5Ha2QNcCpARBwFHEj/3XFvrO3AL1V/PxX4QQ+zvKr6V81fARsz8/NjRvXddtYo6yBtZwBk5pR8AbcDTwG7qRTMbwBvpvKt+w+qP9/U65xNsm4GtgCPVF/Le52zWd6a8f8JzO51zibr9kDgK8D3gIeBU3udc4K8vwCsA75D5bjvu3uds5r1F6h8YfvdMf9Oz+zH7axJ1r7dzuq9vPRfkkrCQy6SVBIWuiSVhIUuSSVhoUtSSVjoklQSFroklYSFLkkl8f8BxbdQfScJ7sgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression Line \n",
    "ypred = model.predict(xtest.reshape(-1,1))\n",
    "\n",
    "plt.scatter(xtest, ytest)\n",
    "plt.plot(xtest, ypred, c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f3b14dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2122606845136341"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(ytest, ypred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fd7eeb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6647896824598756"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsquared = r2_score(ytest, ypred)\n",
    "rsquared"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b55e97f",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9edccc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(xtrain.reshape(-1,1), ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4eb365dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0 (intercept)- [1.75061574] \n"
     ]
    }
   ],
   "source": [
    "# Intercept\n",
    "\n",
    "print(f'b0 (intercept)- {model.intercept_} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c8966c55",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b1 (Co-efficient)- [[-0.08674008]] \n"
     ]
    }
   ],
   "source": [
    "# Co-efficient\n",
    "\n",
    "print(f'b1 (Co-efficient)- {model.coef_} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1ee9ae6e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Classification Line \n",
    "\n",
    "x = np.sort(xtest.reshape(-1,1), axis=0)\n",
    "ypred = model.predict(x)\n",
    "\n",
    "\n",
    "plt.scatter(x, ytest)\n",
    "plt.plot(x, ypred, color='red')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "48600178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "75fb6c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7647058823529411"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f66b0fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8125"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8a6b43b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.787878787878788\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(ytest, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "fdfd0ba9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  3]\n",
      " [ 4 13]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(ytest, ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2c39f0",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4b173e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis_result', 'radius', 'texture', 'perimeter', 'area',\n",
       "       'smoothness', 'compactness', 'symmetry', 'fractal_dimension'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "13576b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['radius', 'texture']].values\n",
    "y = df['diagnosis_result']\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "labels = encoder.fit_transform(y)\n",
    "\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, labels, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a580a106",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(xtrain, ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7365fa6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0 (intercept) : 1.1169873972460809 \n"
     ]
    }
   ],
   "source": [
    "# Intercept\n",
    "\n",
    "print(f'b0 (intercept) : {model.intercept_} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0260acea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b1 (Co-efficient) : [-0.03014932 -0.00242113] \n"
     ]
    }
   ],
   "source": [
    "# Co-efficient\n",
    "\n",
    "print(f'b1 (Co-efficient) : {model.coef_} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e1e5d243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18256686187853127"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(ytest, ypred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "15aa376e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4318969559100494"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsquared = r2_score(ytest, ypred)\n",
    "rsquared"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}