Student_Performance_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dccefef0",
   "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>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",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <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>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",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <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     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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import Library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "import sklearn\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "\n",
    "style.use(\"ggplot\")\n",
    "\n",
    "data = pd.read_csv(\"student-mat.csv\", sep=\";\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6e85a413",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label: G3 (is what we're trying to get / what we're looking for)\n",
    "# G3 is the final scores of student in 3 Grade.\n",
    "predict = \"G3\"\n",
    "\n",
    "# We cut down our df to following attributes:\n",
    "data = data[[\"G1\", \"G2\", \"absences\",\"failures\", \"studytime\",\"G3\"]]\n",
    "data = shuffle(data) # Optional - shuffle the data\n",
    "\n",
    "# Attributes\n",
    "x = np.array(data.drop([predict], 1))\n",
    "# Labels\n",
    "y =np.array(data[predict]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1f4f8da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attributes --> Labels (Training)\n",
    "# Attributes --> ?      (Testing)\n",
    "# split up 10% of data into test samples\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.1)\n",
    "\n",
    "\n",
    "### NUMERICAL ANALYSIS ###\n",
    "x_df = data.drop([predict], 1)\n",
    "y_df = data[predict]\n",
    "df_num = data[['G1','G2',\"G3\"]]\n",
    "df_cat = data[[\"absences\",\"failures\", \"studytime\"]]\n",
    "sns.heatmap(df_num.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4a6edf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in df_num.columns:\n",
    "    plt.hist(df_num[i])\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b174ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alkat\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alkat\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alkat\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Categorical Analysis ###\n",
    "# Show Distributions for all categorical variables / columns\n",
    "for i in df_cat.columns:\n",
    "    sns.barplot(df_cat[i].value_counts().index, df_cat[i].value_counts()).set_title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c30227d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5866324062897985\n",
      "Accuracy: 0.8364514253412505\n",
      "Accuracy: 0.8422289959805951\n",
      "Accuracy: 0.8483722264696976\n",
      "Accuracy: 0.8308047614798494\n",
      "Accuracy: 0.7509135746760264\n",
      "Accuracy: 0.711242395914672\n",
      "Accuracy: 0.867834035343823\n",
      "Accuracy: 0.8298997965268892\n",
      "Accuracy: 0.874614654942594\n",
      "Accuracy: 0.6843234853228777\n",
      "Accuracy: 0.8804877980395349\n",
      "Accuracy: 0.7859432335212804\n",
      "Accuracy: 0.774730019906687\n",
      "Accuracy: 0.8910069702876313\n",
      "Accuracy: 0.9101003403592323\n",
      "Accuracy: 0.8378482402322229\n",
      "Accuracy: 0.748285513383966\n",
      "Accuracy: 0.9040887421974603\n",
      "Accuracy: 0.8286904430323361\n",
      "______________________________________\n",
      "\n",
      "Coefficient in 5D-Space:  [ 0.14929275  0.98669119  0.03786191 -0.27234744 -0.21284321]\n",
      "Intercept:  -1.5253738110370882\n",
      "______________________________________\n",
      "\n",
      "Predicted Final Grade - Input Data - Actual Final Grade\n",
      "6.327256328794325 [7 7 3 0 1] 8\n",
      "11.886999055538293 [14 11 18  0  1] 13\n",
      "8.274950167241775 [8 9 4 0 2] 10\n",
      "11.296419302083828 [ 8 12  0  0  1] 12\n",
      "3.392961591315303 [6 5 0 1 3] 0\n",
      "8.284968637602256 [10  9  2  0  3] 10\n",
      "15.164422762800541 [15 15  2  0  2] 16\n",
      "14.93940618456779 [14 15  0  0  2] 15\n",
      "10.994796315517359 [13 11  4  0  2] 11\n",
      "11.110536950809344 [12 11 11  0  2] 11\n",
      "10.933400755550934 [13 11  8  0  3] 11\n",
      "12.808867475402263 [10 13  6  0  1] 13\n",
      "17.575664934705575 [16 17  4  0  1] 18\n",
      "9.49667640746022 [11 10  4  0  3] 10\n",
      "13.041747626545089 [14 13  2  0  2] 13\n",
      "15.713254165771136 [14 16  0  0  3] 16\n",
      "6.864727452223244 [7 8 8 0 4] 8\n",
      "12.593869367529992 [11 13  2  0  2] 14\n",
      "11.197711348172374 [11 12  4  1  3] 11\n",
      "8.474278200671558 [10  9  7  0  3] 9\n",
      "7.996401601952673 [10  9  0  0  4] 0\n",
      "-0.5438749974186922 [8 0 0 0 1] 0\n",
      "14.404903046858493 [15 14  8  0  2] 14\n",
      "9.40877921442161 [10 10  0  0  2] 10\n",
      "10.757606369473717 [11 11  0  0  1] 12\n",
      "10.109058267847644 [ 9 11  2  0  3] 11\n",
      "12.800121319386188 [13 12 20  0  1] 12\n",
      "5.153110628329032 [ 6  6 22  2  2] 4\n",
      "8.485511913998785 [ 8  8 30  0  1] 8\n",
      "10.843348665061919 [13 11  0  0  2] 10\n",
      "10.010260021342555 [12 10  8  0  2] 11\n",
      "9.31860055771815 [ 9  9 22  0  1] 9\n",
      "18.936665457568576 [18 18  6  0  1] 18\n",
      "13.197505071901343 [12 13 14  0  2] 12\n",
      "10.544763159051854 [11 11  0  0  2] 10\n",
      "11.780817664531591 [14 12  6  0  4] 13\n",
      "11.261731468835894 [10  9 75  0  2] 9\n",
      "11.907918579365253 [12 12  6  0  2] 12\n",
      "10.33191994862999 [11 11  0  0  3] 10\n",
      "15.002956641984621 [13 15  0  0  1] 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TRAIN MODEL MULTIPLE TIMES FOR BEST SCORE\n",
    "best = 0\n",
    "for _ in range(20):\n",
    "    # split df up\n",
    "    x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.1)\n",
    "    # choose model\n",
    "    linear = linear_model.LinearRegression()\n",
    "    # train model\n",
    "    linear.fit(x_train, y_train)\n",
    "    # test & return accuracy\n",
    "    acc = linear.score(x_test, y_test)\n",
    "    print(\"Accuracy: \" + str(acc))\n",
    "\n",
    "    # save our new model if the current score better than the previous one that we've seen.\n",
    "    if acc > best:\n",
    "        best = acc\n",
    "        with open(\"studentgrades.pickle\", \"wb\") as f:\n",
    "            pickle.dump(linear, f)\n",
    "\n",
    "# LOAD pickle into our linear model\n",
    "pickle_in = open(\"studentgrades.pickle\", \"rb\")\n",
    "linear = pickle.load(pickle_in)\n",
    "\n",
    "\n",
    "print(\"______________________________________\\n\")\n",
    "print('Coefficients / Slopes in 5D-Space: ', linear.coef_) # how fast our line increases.\n",
    "print('Intercept: ', linear.intercept_)\n",
    "print(\"______________________________________\\n\") \n",
    "# intercept; where our line on Y starts.\n",
    "# slope = (y2 – y1) / (x2 – x1)\n",
    "\n",
    "# We're gonna use our model to predict y_test \n",
    "# by feeding machine with x_test\n",
    "predicted = linear.predict(x_test)\n",
    "# Show Input Data & predictions\n",
    "\n",
    "print(\"Predicted Final Grade - Input Data - Actual Final Grade\")\n",
    "for x in range(len(predicted)):\n",
    "    print(predicted[x], x_test[x], y_test[x])\n",
    "\n",
    "\n",
    "# Drawing and plotting model\n",
    "plot = \"failures\"\n",
    "plt.scatter(data[plot], data[\"G3\"])\n",
    "plt.legend(loc=4)\n",
    "plt.xlabel(plot)\n",
    "plt.ylabel(\"Final Grade\")\n",
    "plt.show()\n",
    "\n",
    "#\"G1\", \"G2\", \"absences\",\"failures\", \"studytime\","
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "254ecfbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a model using a polynomial function\n",
    "# First Step is to seperate Attributes from Lables\n",
    "# Attributes\n",
    "x = np.array(data.drop([predict], axis=1))\n",
    "# Labels\n",
    "y =np.array(data[predict])\n",
    "\n",
    "# Introduction of Polynomial Featuers \n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "polynomial_converter = PolynomialFeatures(degree=3)\n",
    "\n",
    "# Converter \"fits\" to data, in this case, reads in every X column\n",
    "# Then it \"transforms\" and ouputs the new polynomial data\n",
    "poly_features = polynomial_converter.fit_transform(x)\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(poly_features, y, test_size=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dc6d357a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# We evaluate its performance on the test set\n",
    "# Testing our model\n",
    "test_predictions = model.predict (x_test) #polynomial_converter.fit_transform(x_test)\n",
    "\n",
    "# model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "333c933c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Polynomial Regression is better than linear Regression because:\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "MAE = mean_absolute_error(y_test,test_predictions)\n",
    "MSE = mean_squared_error(y_test,test_predictions)\n",
    "RMSE = np.sqrt(MSE) # root mean squared error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cd35e2ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.000111436843872"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "16fcd48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.64013903549594"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "050bf614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.954761564936114"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bedff69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.415189873417722"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['G3'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c1cba07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0a9ad3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Regularization with SciKit-Learn ###\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"student-mat.csv\", sep=\";\")\n",
    "\n",
    "# We cut down our df to following attributes:\n",
    "df = df[[\"G1\", \"G2\", \"absences\",\"failures\", \"studytime\",\"G3\"]]\n",
    "\n",
    "# Attributes\n",
    "X = df.drop([\"G3\"], axis=1)\n",
    "# Labels\n",
    "y =df[\"G3\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c1843792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Polynomial Feature Set\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "polynomial_converter = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly_features = polynomial_converter.fit_transform(X)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "04e72440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 20)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# poly_features include not just the original terms \n",
    "# but also interaction (higher degree) Terms\n",
    "poly_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "06b5cb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train | Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8daf0cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.1801074 ,  1.46329902, -0.71465103, -0.46337631,  1.15053101,\n",
       "        2.61464798,  2.17686421, -0.71940815, -0.4675205 ,  2.44237801,\n",
       "        1.72767487, -0.7371801 , -0.44996336,  1.97695309, -0.25114116,\n",
       "       -0.333327  , -0.67764573, -0.35857453, -0.44925713,  1.0577742 ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling the Data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c541ec1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.,  6.,  6.,  0.,  2., 25., 30., 30.,  0., 10., 36., 36.,  0.,\n",
       "       12., 36.,  0., 12.,  0.,  0.,  4.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "017d8fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Ridge Regression ###\n",
    "# helps by reducing the potential for overfitting to the training data\n",
    "# it does this by adding in a \"Penalty Term\" to the error that is \n",
    "# based on the squared value of the coefficients.\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# using the cross validation method we're gonna show later \n",
    "# why 10 is the best alpha value that we can choose\n",
    "ridge_model = Ridge(alpha=1)\n",
    "ridge_model.fit(X_train,y_train)\n",
    "# help(Ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "536d917d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = ridge_model.predict(X_test)\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "MAE = mean_absolute_error(y_test,test_predictions)\n",
    "MSE = mean_squared_error(y_test,test_predictions)\n",
    "RMSE = np.sqrt(MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c1f8007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1982532492605362"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2242efe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9332720401387817"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b1c92b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV # Ridge with cross validation\n",
    "# RidgeVC is gonna perform a cross validation for a variety of alpha values\n",
    "# and then it is gonna report back the best alpha value performance\n",
    "\n",
    "# See all options: sklearn.metrics.SCORERS.keys()\n",
    "ridge_cv_model = RidgeCV(alphas=(0.1, 1.0, 10.0),scoring='neg_mean_absolute_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "51940e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([ 0.1,  1. , 10. ]), scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The more alpha options you pass, the longer this will take.\n",
    "# Fortunately our data set is still pretty small\n",
    "ridge_cv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7b0ae34e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the alpha that did the best is:\n",
    "ridge_cv_model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "813bc1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = ridge_cv_model.predict(X_test)\n",
    "\n",
    "MAE = mean_absolute_error(y_test,test_predictions)\n",
    "MSE = mean_squared_error(y_test,test_predictions)\n",
    "RMSE = np.sqrt(MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "572bc87b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.241200538156996"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "455c3051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9574379260919614"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6459e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the cross validation to determine the best alpha, \n",
    "# has improved the Results slightly now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "06185686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.4750282 ,  3.44455492,  1.94283909, -1.99389071, -0.34232484,\n",
       "        2.67357497, -5.38585156,  0.06043811,  0.33203071, -0.66301224,\n",
       "        2.82418203, -1.49850151,  0.20723506,  1.34538529, -0.87861077,\n",
       "        0.08842517,  0.59872747,  1.36020439, -0.07297938, -0.36010142])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_cv_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bd2fe90",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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