linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "from sklearn import linear_model\n",
    "from sklearn.utils import shuffle\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <td>GP</td>\n",
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       "      <td>17</td>\n",
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       "      <td>GT3</td>\n",
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       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
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       "      <td>5</td>\n",
       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>GP</td>\n",
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       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
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       "      <td>at_home</td>\n",
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       "      <td>3</td>\n",
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       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "      <th>4</th>\n",
       "      <td>GP</td>\n",
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       "      <td>16</td>\n",
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       "      <td>GT3</td>\n",
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       "      <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     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \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": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('student-mat.csv',sep=';')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
       "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
       "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
       "       'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
       "       'Walc', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>G1</th>\n",
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       "      <th>G3</th>\n",
       "      <th>studytime</th>\n",
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      ],
      "text/plain": [
       "   G1  G2  G3  studytime  failures  absences\n",
       "0   5   6   6          2         0         6\n",
       "1   5   5   6          2         0         4\n",
       "2   7   8  10          2         3        10\n",
       "3  15  14  15          3         0         2\n",
       "4   6  10  10          2         0         4"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[[\"G1\",\"G2\",\"G3\",\"studytime\",\"failures\",\"absences\"]]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = 'G3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(data.drop(predict, axis=1))\n",
    "y = np.array(data[predict])\n",
    "X_train,X_test,y_train,y_test = sklearn.model_selection.train_test_split(X,y,test_size=0.1)"
   ]
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   "cell_type": "code",
   "execution_count": 245,
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      "0.7367552992938022\n",
      "0.8656456860699797\n",
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      "0.8235228769666063\n",
      "0.8414274283520689\n",
      "0.8381832329028465\n",
      "0.8972474062930862\n",
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      "0.6826088451280136\n",
      "0.9011524943894982\n",
      "0.8902269636530404\n",
      "0.7873201513351693\n",
      "0.8570967016180786\n",
      "0.9000995858698033\n",
      "0.8651533872818784\n",
      "0.8210438036876657\n",
      "0.8791916240449268\n",
      "0.8764402248045341\n",
      "0.7464250055127285\n",
      "0.8156632190214782\n",
      "0.8637091726401629\n",
      "0.8908905161097439\n",
      "0.8448668554384955\n",
      "0.8444236627955133\n"
     ]
    }
   ],
   "source": [
    "best = 0\n",
    "for i in range (1000):\n",
    "    X_train,X_test,y_train,y_test = sklearn.model_selection.train_test_split(X,y,test_size=0.1)\n",
    "\n",
    "    linear = linear_model.LinearRegression()\n",
    "    linear.fit(X_train,y_train)\n",
    "    acc = linear.score(X_test,y_test)\n",
    "    print(acc)\n",
    "\n",
    "if acc > best:\n",
    "    best = acc\n",
    "    with open('studentmodel.pickle','wb') as f:\n",
    "        pickle.dump(linear,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in = open('studentmodel.pickle','rb')\n",
    "linear = pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Co:  [ 0.17224209  0.96781958 -0.18219811 -0.20657314  0.03564522]\n",
      "Intercept:  -1.6462184949797063\n"
     ]
    }
   ],
   "source": [
    "print('Co: ' , linear.coef_)\n",
    "print('Intercept: ' , linear.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.619004581543653 [16 15  2  0 10] 15\n",
      "9.995970702430643 [10 10  2  0 17] 10\n",
      "11.870601969982722 [14 11  1  0 18] 13\n",
      "8.646009720647161 [9 9 1 0 6] 10\n",
      "12.737469901775412 [13 12  1  0 20] 12\n",
      "4.117436262411781 [ 5  5  2  0 12] 5\n",
      "8.768678552663669 [11  9  3  0 10] 10\n",
      "11.751371750675942 [11 12  1  0  2] 11\n",
      "13.05371948083451 [14 13  2  0  2] 13\n",
      "4.901045721795958 [6 6 2 0 2] 6\n",
      "7.123777144271163 [10  8  1  3  3] 7\n",
      "8.220279076526337 [8 9 2 0 4] 10\n",
      "7.706181147063056 [7 9 2 2 6] 8\n",
      "8.148988634959137 [8 9 2 0 2] 10\n",
      "7.3950403809379015 [8 8 2 0 8] 6\n",
      "8.71730016221436 [9 9 1 0 8] 10\n",
      "7.292076728725391 [8 8 1 0 0] 11\n",
      "5.748001602470759 [7 7 4 0 4] 6\n",
      "8.249940281889279 [9 9 2 0 0] 10\n",
      "4.8323836162575 [8 6 2 2 2] 5\n",
      "3.4237980610161323 [3 5 2 1 8] 5\n",
      "12.627988836713685 [13 13  3  0  0] 14\n",
      "12.142935781866976 [10 13  4  0  6] 13\n",
      "11.078136566352288 [ 9 12  3  0  3] 11\n",
      "12.315177870364316 [11 13  4  0  6] 14\n",
      "14.56362799415935 [13 15  3  0  0] 15\n",
      "10.977511445536816 [13 11  3  0  8] 11\n",
      "5.522339612146565 [8 7 2 3 0] 0\n",
      "15.333842815274856 [16 15  2  0  2] 15\n",
      "2.8354100428035824 [7 4 1 2 0] 0\n",
      "13.910631387068255 [14 14  3  0  4] 14\n",
      "18.506523276732548 [18 18  3  0  5] 19\n",
      "9.093457549854353 [11  9  2  0 14] 9\n",
      "5.8075013784153615 [8 7 2 3 8] 8\n",
      "10.019647892372927 [12 10  2  0  8] 11\n",
      "7.993013113556067 [10  8  1  0 10] 9\n",
      "7.640176883648516 [8 9 1 3 0] 10\n",
      "13.475478114817047 [15 13  2  0  9] 15\n",
      "7.851419872321372 [6 9 1 1 4] 8\n",
      "5.358449576562095 [ 7  6  2  0 10] 6\n"
     ]
    }
   ],
   "source": [
    "predictions = linear.predict(X_test)\n",
    "\n",
    "for x in range (len(predictions)):\n",
    "    print(predictions[x], X_test[x],y_test[x])\n",
    "\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Select predictor and target variables\n",
    "predictor = 'absences'\n",
    "target = 'G3'\n",
    "\n",
    "# Plot data points\n",
    "plt.scatter(data[predictor], data[target], alpha=0.5, color='b', label='Data Points')\n",
    "\n",
    "# Fit a linear regression model\n",
    "model = LinearRegression()\n",
    "X = data[[predictor]]\n",
    "y = data[target]\n",
    "model.fit(X, y)\n",
    "\n",
    "# Plot regression line\n",
    "plt.plot(X, model.predict(X), color='r', label='Regression Line')\n",
    "\n",
    "# Customize plot\n",
    "plt.xlabel(predictor)\n",
    "plt.ylabel(target)\n",
    "plt.title('Relationship between Absences and Final Grade')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}