coding-assignments/4.NN-Classification.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",
       "        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>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": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anush/anaconda3/envs/dart/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:916: 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": [
       "MLPClassifier(activation='relu', alpha=1, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score \n",
    "\n",
    "#model = LogisticRegression()\n",
    "model = MLPClassifier(alpha=1, max_iter=1000)\n",
    "\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test (Predict) the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = model.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.64\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y.flatten(),predicted_classes)\n",
    "print(accuracy)"
   ]
  },
  {
   "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
}