coding-assignments/2.Logistic-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Diabetes Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.623660</td>\n",
       "      <td>78.024693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.286711</td>\n",
       "      <td>43.894998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.847409</td>\n",
       "      <td>72.902198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.182599</td>\n",
       "      <td>86.308552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.032736</td>\n",
       "      <td>75.344376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45.083277</td>\n",
       "      <td>56.316372</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>61.106665</td>\n",
       "      <td>96.511426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75.024746</td>\n",
       "      <td>46.554014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>76.098787</td>\n",
       "      <td>87.420570</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84.432820</td>\n",
       "      <td>43.533393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>95.861555</td>\n",
       "      <td>38.225278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>75.013658</td>\n",
       "      <td>30.603263</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>82.307053</td>\n",
       "      <td>76.481963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>69.364589</td>\n",
       "      <td>97.718692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>39.538339</td>\n",
       "      <td>76.036811</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>53.971052</td>\n",
       "      <td>89.207350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>69.070144</td>\n",
       "      <td>52.740470</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>67.946855</td>\n",
       "      <td>46.678574</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70.661510</td>\n",
       "      <td>92.927138</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>76.978784</td>\n",
       "      <td>47.575964</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1  2\n",
       "0   34.623660  78.024693  0\n",
       "1   30.286711  43.894998  0\n",
       "2   35.847409  72.902198  0\n",
       "3   60.182599  86.308552  1\n",
       "4   79.032736  75.344376  1\n",
       "5   45.083277  56.316372  0\n",
       "6   61.106665  96.511426  1\n",
       "7   75.024746  46.554014  1\n",
       "8   76.098787  87.420570  1\n",
       "9   84.432820  43.533393  1\n",
       "10  95.861555  38.225278  0\n",
       "11  75.013658  30.603263  0\n",
       "12  82.307053  76.481963  1\n",
       "13  69.364589  97.718692  1\n",
       "14  39.538339  76.036811  0\n",
       "15  53.971052  89.207350  1\n",
       "16  69.070144  52.740470  1\n",
       "17  67.946855  46.678574  0\n",
       "18  70.661510  92.927138  1\n",
       "19  76.978784  47.575964  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks_df = pd.read_csv(\"marks.txt\", header=None)\n",
    "marks_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": [
    "# X = feature values, all the columns except the last column\n",
    "X = marks_df.iloc[:, :-1]\n",
    "\n",
    "# y = target values, last column of the data frame\n",
    "y = marks_df.iloc[:, -1]\n",
    "\n",
    "# filter out the applicants that got admitted\n",
    "admitted = marks_df.loc[y == 1]\n",
    "\n",
    "# filter out the applicants that din't get admission\n",
    "not_admitted = marks_df.loc[y == 0]\n",
    "\n",
    "# plots\n",
    "plt.scatter(admitted.iloc[:, 0], admitted.iloc[:, 1], s=10, label='Admitted')\n",
    "plt.scatter(not_admitted.iloc[:, 0], not_admitted.iloc[:, 1], s=10, label='Not Admitted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.c_[np.ones((X.shape[0], 1)), X]\n",
    "y = y[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Logistc Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anush/anaconda3/envs/dart/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/anush/anaconda3/envs/dart/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.metrics import accuracy_score \n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test (Predict) the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = model.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.91\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y.flatten(),predicted_classes)\n",
    "parameters = model.coef_\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": [
    "x_values = [np.min(X[:, 1] - 5), np.max(X[:, 2] + 5)]\n",
    "parameters = [[-25.16131856,0.20623159,0.20147149]]\n",
    "y_values = - (parameters[0][0] + np.dot(parameters[0][1], x_values)) / parameters[0][2]\n",
    "\n",
    "plt.scatter(admitted.iloc[:, 0], admitted.iloc[:, 1], s=10, label='Admitted')\n",
    "plt.scatter(not_admitted.iloc[:, 0], not_admitted.iloc[:, 1], s=10, label='Not Admitted')\n",
    "\n",
    "plt.plot(x_values, y_values, label='Decision Boundary')\n",
    "plt.xlabel('Marks in 1st Exam')\n",
    "plt.ylabel('Marks in 2nd Exam')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}