project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder,StandardScaler\n",
    "from sklearn.metrics import accuracy_score,classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import chi2,SelectKBest\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data =pd.read_csv(r\"C:\\Users\\Manali\\Desktop\\datasets\\student-mat.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0       0    0   18        1        0        0     4     4     0     4  ...   \n",
       "1       0    0   17        1        0        1     1     1     0     2  ...   \n",
       "2       0    0   15        1        1        1     1     1     0     2  ...   \n",
       "3       0    0   15        1        0        1     4     2     1     3  ...   \n",
       "4       0    0   16        1        0        1     3     3     2     2  ...   \n",
       "\n",
       "   famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  G3  \n",
       "0       4         3      4     1     1       3         6   5   6   6  \n",
       "1       5         3      3     1     1       3         4   5   5   6  \n",
       "2       4         3      2     2     3       3        10   7   8  10  \n",
       "3       3         2      2     1     1       5         2  15  14  15  \n",
       "4       4         3      2     1     2       5         4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_with_obj_type = [c for c,d in zip(data.columns,data.dtypes) if d == np.object]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "Scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in cols_with_obj_type:\n",
    "    data[col] = label_encoder.fit_transform(data[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = Scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = pd.DataFrame(db, columns=data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
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       "      <th>absences</th>\n",
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       "      <td>-1.330954</td>\n",
       "      <td>0.535392</td>\n",
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       "      <td>-1.600009</td>\n",
       "      <td>-1.399970</td>\n",
       "      <td>-1.769793</td>\n",
       "      <td>-0.325831</td>\n",
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       "      <td>0.062194</td>\n",
       "      <td>-0.236010</td>\n",
       "      <td>-0.997295</td>\n",
       "      <td>0.583385</td>\n",
       "      <td>0.551100</td>\n",
       "      <td>-0.399289</td>\n",
       "      <td>0.536865</td>\n",
       "      <td>-1.179147</td>\n",
       "      <td>-0.722415</td>\n",
       "      <td>-0.090739</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-0.948176</td>\n",
       "      <td>-1.330954</td>\n",
       "      <td>0.535392</td>\n",
       "      <td>-0.636941</td>\n",
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       "      <td>-1.003789</td>\n",
       "      <td>1.041070</td>\n",
       "      <td>-0.464016</td>\n",
       "      <td>1.234133</td>\n",
       "      <td>0.874715</td>\n",
       "      <td>1.002004</td>\n",
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       "      <td>-0.36305</td>\n",
       "      <td>-0.948176</td>\n",
       "      <td>-0.546287</td>\n",
       "      <td>0.535392</td>\n",
       "      <td>-0.636941</td>\n",
       "      <td>0.340322</td>\n",
       "      <td>0.229234</td>\n",
       "      <td>0.440257</td>\n",
       "      <td>-0.138362</td>\n",
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      ],
      "text/plain": [
       "    school       sex       age   address   famsize   Pstatus      Medu  \\\n",
       "0 -0.36305 -0.948176  1.023046  0.535392 -0.636941 -2.938392  1.143856   \n",
       "1 -0.36305 -0.948176  0.238380  0.535392 -0.636941  0.340322 -1.600009   \n",
       "2 -0.36305 -0.948176 -1.330954  0.535392  1.570004  0.340322 -1.600009   \n",
       "3 -0.36305 -0.948176 -1.330954  0.535392 -0.636941  0.340322  1.143856   \n",
       "4 -0.36305 -0.948176 -0.546287  0.535392 -0.636941  0.340322  0.229234   \n",
       "\n",
       "       Fedu      Mjob      Fjob  ...    famrel  freetime     goout      Dalc  \\\n",
       "0  1.360371 -1.769793  1.993149  ...  0.062194 -0.236010  0.801479 -0.540699   \n",
       "1 -1.399970 -1.769793 -0.325831  ...  1.178860 -0.236010 -0.097908 -0.540699   \n",
       "2 -1.399970 -1.769793 -0.325831  ...  0.062194 -0.236010 -0.997295  0.583385   \n",
       "3 -0.479857 -0.954077  0.833659  ... -1.054472 -1.238419 -0.997295 -0.540699   \n",
       "4  0.440257 -0.138362 -0.325831  ...  0.062194 -0.236010 -0.997295 -0.540699   \n",
       "\n",
       "       Walc    health  absences        G1        G2        G3  \n",
       "0 -1.003789 -0.399289  0.036424 -1.782467 -1.254791 -0.964934  \n",
       "1 -1.003789 -0.399289 -0.213796 -1.782467 -1.520979 -0.964934  \n",
       "2  0.551100 -0.399289  0.536865 -1.179147 -0.722415 -0.090739  \n",
       "3 -1.003789  1.041070 -0.464016  1.234133  0.874715  1.002004  \n",
       "4 -0.226345  1.041070 -0.213796 -1.480807 -0.190038 -0.090739  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a840f1b048>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 4000x2000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10),dpi=200)\n",
    "sns.heatmap(data.corr())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature = ['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu','Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime','failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
    "'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc','Walc', 'health', 'absences']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G1_X = data[feature].values\n",
    "G1_Y = data[\"G1\"].values\n",
    "G2_X = data[feature + [\"G1\"]].values\n",
    "G2_Y = data[\"G2\"].values\n",
    "G3_X = data[feature + [\"G2\"]].values\n",
    "G3_Y = data[\"G3\"].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G1_X = SelectKBest(chi2, k=20).fit_transform(G1_X, G1_Y)\n",
    "G2_X = SelectKBest(chi2, k=10).fit_transform(G2_X, G2_Y)\n",
    "G3_X = SelectKBest(chi2, k=10).fit_transform(G3_X, G3_Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grade_1 =  MLPClassifier(hidden_layer_sizes=(100,),alpha=0.0001,max_iter=1000)\n",
    "grade_2 =  MLPClassifier(hidden_layer_sizes=(100,),alpha=0.0001,max_iter=3000)\n",
    "grade_3 =  MLPClassifier(hidden_layer_sizes=(100,),alpha=0.0001,max_iter=3000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G1_train_X,G1_test_X, G1_train_Y,G1_test_Y = train_test_split(G1_X,G1_Y,test_size=0.2)\n",
    "G2_train_X,G2_test_X, G2_train_Y,G2_test_Y = train_test_split(G2_X,G2_Y,test_size=0.2)\n",
    "G3_train_X,G3_test_X, G3_train_Y,G3_test_Y = train_test_split(G3_X,G3_Y,test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, 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=3000, 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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grade_1.fit(G1_X, G1_Y)\n",
    "grade_2.fit(G2_X,G2_Y)\n",
    "grade_3.fit(G3_X,G3_Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grade_1.score(G1_test_X,G1_test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9367088607594937"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grade_2.score(G2_test_X,G2_test_Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9873417721518988"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grade_3.score(G3_test_X,G3_test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       1.00      1.00      1.00         1\n",
      "           4       1.00      1.00      1.00         1\n",
      "           5       1.00      1.00      1.00         7\n",
      "           6       1.00      0.96      0.98        24\n",
      "           7       1.00      0.97      0.99        37\n",
      "           8       0.98      0.98      0.98        41\n",
      "           9       1.00      1.00      1.00        31\n",
      "          10       0.98      1.00      0.99        51\n",
      "          11       0.97      0.97      0.97        39\n",
      "          12       0.94      0.97      0.96        35\n",
      "          13       1.00      1.00      1.00        33\n",
      "          14       1.00      1.00      1.00        30\n",
      "          15       1.00      1.00      1.00        24\n",
      "          16       1.00      1.00      1.00        22\n",
      "          17       1.00      1.00      1.00         8\n",
      "          18       1.00      1.00      1.00         8\n",
      "          19       1.00      1.00      1.00         3\n",
      "\n",
      "   micro avg       0.99      0.99      0.99       395\n",
      "   macro avg       0.99      0.99      0.99       395\n",
      "weighted avg       0.99      0.99      0.99       395\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(G1_Y, grade_1.predict(G1_X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.96        13\n",
      "           4       1.00      1.00      1.00         1\n",
      "           5       1.00      1.00      1.00        15\n",
      "           6       1.00      1.00      1.00        14\n",
      "           7       1.00      1.00      1.00        21\n",
      "           8       0.97      0.97      0.97        32\n",
      "           9       0.96      0.92      0.94        50\n",
      "          10       0.84      0.89      0.86        46\n",
      "          11       0.94      0.86      0.90        35\n",
      "          12       0.83      0.85      0.84        41\n",
      "          13       0.89      0.89      0.89        37\n",
      "          14       0.86      0.83      0.84        23\n",
      "          15       0.86      0.88      0.87        34\n",
      "          16       1.00      1.00      1.00        13\n",
      "          17       1.00      1.00      1.00         5\n",
      "          18       1.00      1.00      1.00        12\n",
      "          19       1.00      1.00      1.00         3\n",
      "\n",
      "   micro avg       0.92      0.92      0.92       395\n",
      "   macro avg       0.95      0.95      0.95       395\n",
      "weighted avg       0.92      0.92      0.92       395\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(G2_Y, grade_2.predict(G2_X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        38\n",
      "           4       1.00      1.00      1.00         1\n",
      "           5       1.00      1.00      1.00         7\n",
      "           6       1.00      1.00      1.00        15\n",
      "           7       1.00      1.00      1.00         9\n",
      "           8       1.00      1.00      1.00        32\n",
      "           9       1.00      0.96      0.98        28\n",
      "          10       0.98      0.98      0.98        56\n",
      "          11       1.00      1.00      1.00        47\n",
      "          12       0.97      1.00      0.98        31\n",
      "          13       0.97      1.00      0.98        31\n",
      "          14       1.00      0.96      0.98        27\n",
      "          15       1.00      0.97      0.98        33\n",
      "          16       0.94      1.00      0.97        16\n",
      "          17       1.00      1.00      1.00         6\n",
      "          18       1.00      1.00      1.00        12\n",
      "          19       1.00      1.00      1.00         5\n",
      "          20       1.00      1.00      1.00         1\n",
      "\n",
      "   micro avg       0.99      0.99      0.99       395\n",
      "   macro avg       0.99      0.99      0.99       395\n",
      "weighted avg       0.99      0.99      0.99       395\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(G3_Y, grade_3.predict(G3_X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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