Linear_Regression.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "X3rgjiUTC8Ic"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/rubaomari/data/main/student/student-mat.csv'\n",
        "\n",
        "df = pd.read_csv(url,sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1- Take a quick look at the data structure (10 Marks):\n",
        "\n",
        "a. Use head, info, and describe\n",
        "\n",
        "b. Plot a histogram of the following features: Health Status (health), Study Time\n",
        "(studytime), Free Time After School (freetime), Goes Out with Friends (goout),\n",
        "Number of Absences (absences), First Period Grade (G1), Second Period Grade\n",
        "(G2), and Third Period Grade (G3).\n",
        "\n",
        "c. Print G3 correlation matrix with the other attributes.\n",
        "\n",
        "d. Check for correlation between attributes using scatter_matrix. You can\n",
        "alternatively use sns.pairplot for this task for better graphics.\n",
        "\n",
        "e. Using scatter_matrix, check for correlations between G1, G2, G3, Absences, and\n",
        "Study Time."
      ],
      "metadata": {
        "id": "GvgSiswwwu_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1a:\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWJubfjQExK1",
        "outputId": "d1682c92-9676-474e-b5b9-a0c43362edb2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1a:\n",
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDI4uuEBEzSU",
        "outputId": "b03e2104-ead3-4fd0-ca1b-fc9449e63a92"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 395 entries, 0 to 394\n",
            "Data columns (total 33 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   school      395 non-null    object\n",
            " 1   sex         395 non-null    object\n",
            " 2   age         395 non-null    int64 \n",
            " 3   address     395 non-null    object\n",
            " 4   famsize     395 non-null    object\n",
            " 5   Pstatus     395 non-null    object\n",
            " 6   Medu        395 non-null    int64 \n",
            " 7   Fedu        395 non-null    int64 \n",
            " 8   Mjob        395 non-null    object\n",
            " 9   Fjob        395 non-null    object\n",
            " 10  reason      395 non-null    object\n",
            " 11  guardian    395 non-null    object\n",
            " 12  traveltime  395 non-null    int64 \n",
            " 13  studytime   395 non-null    int64 \n",
            " 14  failures    395 non-null    int64 \n",
            " 15  schoolsup   395 non-null    object\n",
            " 16  famsup      395 non-null    object\n",
            " 17  paid        395 non-null    object\n",
            " 18  activities  395 non-null    object\n",
            " 19  nursery     395 non-null    object\n",
            " 20  higher      395 non-null    object\n",
            " 21  internet    395 non-null    object\n",
            " 22  romantic    395 non-null    object\n",
            " 23  famrel      395 non-null    int64 \n",
            " 24  freetime    395 non-null    int64 \n",
            " 25  goout       395 non-null    int64 \n",
            " 26  Dalc        395 non-null    int64 \n",
            " 27  Walc        395 non-null    int64 \n",
            " 28  health      395 non-null    int64 \n",
            " 29  absences    395 non-null    int64 \n",
            " 30  G1          395 non-null    int64 \n",
            " 31  G2          395 non-null    int64 \n",
            " 32  G3          395 non-null    int64 \n",
            "dtypes: int64(16), object(17)\n",
            "memory usage: 102.0+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1a:\n",
        "print(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmnMOVumE1Eo",
        "outputId": "4ff79c7b-6700-4657-c9f0-1d5a69d08fa7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
            "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
            "mean    16.696203    2.749367    2.521519    1.448101    2.035443    0.334177   \n",
            "std      1.276043    1.094735    1.088201    0.697505    0.839240    0.743651   \n",
            "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
            "25%     16.000000    2.000000    2.000000    1.000000    1.000000    0.000000   \n",
            "50%     17.000000    3.000000    2.000000    1.000000    2.000000    0.000000   \n",
            "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
            "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
            "\n",
            "           famrel    freetime       goout        Dalc        Walc      health  \\\n",
            "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
            "mean     3.944304    3.235443    3.108861    1.481013    2.291139    3.554430   \n",
            "std      0.896659    0.998862    1.113278    0.890741    1.287897    1.390303   \n",
            "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
            "25%      4.000000    3.000000    2.000000    1.000000    1.000000    3.000000   \n",
            "50%      4.000000    3.000000    3.000000    1.000000    2.000000    4.000000   \n",
            "75%      5.000000    4.000000    4.000000    2.000000    3.000000    5.000000   \n",
            "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
            "\n",
            "         absences          G1          G2          G3  \n",
            "count  395.000000  395.000000  395.000000  395.000000  \n",
            "mean     5.708861   10.908861   10.713924   10.415190  \n",
            "std      8.003096    3.319195    3.761505    4.581443  \n",
            "min      0.000000    3.000000    0.000000    0.000000  \n",
            "25%      0.000000    8.000000    9.000000    8.000000  \n",
            "50%      4.000000   11.000000   11.000000   11.000000  \n",
            "75%      8.000000   13.000000   13.000000   14.000000  \n",
            "max     75.000000   19.000000   19.000000   20.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1b:\n",
        "df[['health','studytime','freetime','goout','absences','G1','G2','G3']].hist(figsize=(20,10))"
      ],
      "metadata": {
        "id": "nG1OBho6RBbw",
        "outputId": "ca4baf31-13ad-4f93-c384-a575a4bff627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2334bf7150>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2335b32210>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334b87c90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2334b492d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334aff8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334ab6ed0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2334a79590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334a2fad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334a2fb10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1c:\n",
        "cor = df.corr()\n",
        "print(cor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YVq9tY0uEfc",
        "outputId": "c1e905ba-4300-4a68-e955-3a5be9f52db7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 age      Medu      Fedu  traveltime  studytime  failures  \\\n",
            "age         1.000000 -0.163658 -0.163438    0.070641  -0.004140  0.243665   \n",
            "Medu       -0.163658  1.000000  0.623455   -0.171639   0.064944 -0.236680   \n",
            "Fedu       -0.163438  0.623455  1.000000   -0.158194  -0.009175 -0.250408   \n",
            "traveltime  0.070641 -0.171639 -0.158194    1.000000  -0.100909  0.092239   \n",
            "studytime  -0.004140  0.064944 -0.009175   -0.100909   1.000000 -0.173563   \n",
            "failures    0.243665 -0.236680 -0.250408    0.092239  -0.173563  1.000000   \n",
            "famrel      0.053940 -0.003914 -0.001370   -0.016808   0.039731 -0.044337   \n",
            "freetime    0.016434  0.030891 -0.012846   -0.017025  -0.143198  0.091987   \n",
            "goout       0.126964  0.064094  0.043105    0.028540  -0.063904  0.124561   \n",
            "Dalc        0.131125  0.019834  0.002386    0.138325  -0.196019  0.136047   \n",
            "Walc        0.117276 -0.047123 -0.012631    0.134116  -0.253785  0.141962   \n",
            "health     -0.062187 -0.046878  0.014742    0.007501  -0.075616  0.065827   \n",
            "absences    0.175230  0.100285  0.024473   -0.012944  -0.062700  0.063726   \n",
            "G1         -0.064081  0.205341  0.190270   -0.093040   0.160612 -0.354718   \n",
            "G2         -0.143474  0.215527  0.164893   -0.153198   0.135880 -0.355896   \n",
            "G3         -0.161579  0.217147  0.152457   -0.117142   0.097820 -0.360415   \n",
            "\n",
            "              famrel  freetime     goout      Dalc      Walc    health  \\\n",
            "age         0.053940  0.016434  0.126964  0.131125  0.117276 -0.062187   \n",
            "Medu       -0.003914  0.030891  0.064094  0.019834 -0.047123 -0.046878   \n",
            "Fedu       -0.001370 -0.012846  0.043105  0.002386 -0.012631  0.014742   \n",
            "traveltime -0.016808 -0.017025  0.028540  0.138325  0.134116  0.007501   \n",
            "studytime   0.039731 -0.143198 -0.063904 -0.196019 -0.253785 -0.075616   \n",
            "failures   -0.044337  0.091987  0.124561  0.136047  0.141962  0.065827   \n",
            "famrel      1.000000  0.150701  0.064568 -0.077594 -0.113397  0.094056   \n",
            "freetime    0.150701  1.000000  0.285019  0.209001  0.147822  0.075733   \n",
            "goout       0.064568  0.285019  1.000000  0.266994  0.420386 -0.009577   \n",
            "Dalc       -0.077594  0.209001  0.266994  1.000000  0.647544  0.077180   \n",
            "Walc       -0.113397  0.147822  0.420386  0.647544  1.000000  0.092476   \n",
            "health      0.094056  0.075733 -0.009577  0.077180  0.092476  1.000000   \n",
            "absences   -0.044354 -0.058078  0.044302  0.111908  0.136291 -0.029937   \n",
            "G1          0.022168  0.012613 -0.149104 -0.094159 -0.126179 -0.073172   \n",
            "G2         -0.018281 -0.013777 -0.162250 -0.064120 -0.084927 -0.097720   \n",
            "G3          0.051363  0.011307 -0.132791 -0.054660 -0.051939 -0.061335   \n",
            "\n",
            "            absences        G1        G2        G3  \n",
            "age         0.175230 -0.064081 -0.143474 -0.161579  \n",
            "Medu        0.100285  0.205341  0.215527  0.217147  \n",
            "Fedu        0.024473  0.190270  0.164893  0.152457  \n",
            "traveltime -0.012944 -0.093040 -0.153198 -0.117142  \n",
            "studytime  -0.062700  0.160612  0.135880  0.097820  \n",
            "failures    0.063726 -0.354718 -0.355896 -0.360415  \n",
            "famrel     -0.044354  0.022168 -0.018281  0.051363  \n",
            "freetime   -0.058078  0.012613 -0.013777  0.011307  \n",
            "goout       0.044302 -0.149104 -0.162250 -0.132791  \n",
            "Dalc        0.111908 -0.094159 -0.064120 -0.054660  \n",
            "Walc        0.136291 -0.126179 -0.084927 -0.051939  \n",
            "health     -0.029937 -0.073172 -0.097720 -0.061335  \n",
            "absences    1.000000 -0.031003 -0.031777  0.034247  \n",
            "G1         -0.031003  1.000000  0.852118  0.801468  \n",
            "G2         -0.031777  0.852118  1.000000  0.904868  \n",
            "G3          0.034247  0.801468  0.904868  1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1d:\n",
        "sns.pairplot(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eeadHlPkwz8B",
        "outputId": "a5c3a943-da0e-4a11-eb00-6544c8305a4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fe788c215d0>"
            ]
          },
          "metadata": {},
          "execution_count": 242
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x2880 with 272 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 1e:\n",
        "from pandas.plotting import scatter_matrix\n",
        "columns = ['G1','G2','G3','absences','studytime']\n",
        "scatter_matrix(df[columns], figsize=(20,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3wRHrTNj0NzG",
        "outputId": "300530e2-111c-43f1-af4b-d24499deef2d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f23346a89d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f233422d450>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23341d9890>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f233418dd90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334151310>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2334107850>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f233413cdd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334101250>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334101290>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23340b7890>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f23340311d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333fe66d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333f9cbd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333f5f110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333f17610>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2333ecdb10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333f04fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334acc990>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f23347cad90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2334748250>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2334843a50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333dff610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333db4c10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333d7a250>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2333d30850>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2- Drop the following columns: Mother’s job (Mjob), Father’s job (Fjob), Reason at School\n",
        "(reason), and Student’s Guardian (guardian) (5 Marks)."
      ],
      "metadata": {
        "id": "-b3r3pJfw9xa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 2:\n",
        "df = df.drop(columns=['Mjob','Fjob','reason','guardian'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pBg0O1Rd00Ay",
        "outputId": "9aa223f5-1ede-4825-a3e5-b34385507939"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu  traveltime  \\\n",
              "0       GP   F   18       U     GT3       A     4     4           2   \n",
              "1       GP   F   17       U     GT3       T     1     1           1   \n",
              "2       GP   F   15       U     LE3       T     1     1           1   \n",
              "3       GP   F   15       U     GT3       T     4     2           1   \n",
              "4       GP   F   16       U     GT3       T     3     3           1   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...         ...   \n",
              "390     MS   M   20       U     LE3       A     2     2           1   \n",
              "391     MS   M   17       U     LE3       T     3     1           2   \n",
              "392     MS   M   21       R     GT3       T     1     1           1   \n",
              "393     MS   M   18       R     LE3       T     3     2           3   \n",
              "394     MS   M   19       U     LE3       T     1     1           1   \n",
              "\n",
              "     studytime  ...  famrel freetime goout Dalc Walc health absences  G1  G2  \\\n",
              "0            2  ...       4        3     4    1    1      3        6   5   6   \n",
              "1            2  ...       5        3     3    1    1      3        4   5   5   \n",
              "2            2  ...       4        3     2    2    3      3       10   7   8   \n",
              "3            3  ...       3        2     2    1    1      5        2  15  14   \n",
              "4            2  ...       4        3     2    1    2      5        4   6  10   \n",
              "..         ...  ...     ...      ...   ...  ...  ...    ...      ...  ..  ..   \n",
              "390          2  ...       5        5     4    4    5      4       11   9   9   \n",
              "391          1  ...       2        4     5    3    4      2        3  14  16   \n",
              "392          1  ...       5        5     3    3    3      3        3  10   8   \n",
              "393          1  ...       4        4     1    3    4      5        0  11  12   \n",
              "394          1  ...       3        2     3    3    3      5        5   8   9   \n",
              "\n",
              "     G3  \n",
              "0     6  \n",
              "1     6  \n",
              "2    10  \n",
              "3    15  \n",
              "4    10  \n",
              "..   ..  \n",
              "390   9  \n",
              "391  16  \n",
              "392   7  \n",
              "393  10  \n",
              "394   9  \n",
              "\n",
              "[395 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f97d8131-a747-4b3d-b9ff-cc1cbc09a835\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>traveltime</th>\n",
              "      <th>studytime</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>2</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>2</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>1</td>\n",
              "      <td>3</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>1</td>\n",
              "      <td>2</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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>R</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>R</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 29 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3- Check if there are null values and fill them with the average value of that attribute if it is\n",
        "numerical, and with the most common occurrence of that value if it is categorical (5\n",
        "Marks)."
      ],
      "metadata": {
        "id": "BrXz55QlxDad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 3:\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfG_dw0W4M2s",
        "outputId": "dbdad14c-e025-47fa-a0ff-b40c8a4132f0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school        0\n",
              "sex           0\n",
              "age           0\n",
              "address       0\n",
              "famsize       0\n",
              "Pstatus       0\n",
              "Medu          0\n",
              "Fedu          0\n",
              "traveltime    0\n",
              "studytime     0\n",
              "failures      0\n",
              "schoolsup     0\n",
              "famsup        0\n",
              "paid          0\n",
              "activities    0\n",
              "nursery       0\n",
              "higher        0\n",
              "internet      0\n",
              "romantic      0\n",
              "famrel        0\n",
              "freetime      0\n",
              "goout         0\n",
              "Dalc          0\n",
              "Walc          0\n",
              "health        0\n",
              "absences      0\n",
              "G1            0\n",
              "G2            0\n",
              "G3            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 3:\n",
        "percentage = 0.01\n",
        "mask = np.random.choice([True, False], size=df.shape, p=[percentage,1-percentage])\n",
        "dfmod1= df.mask(mask)\n",
        "\n",
        "dfmod1.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxUvTm-NGrdd",
        "outputId": "a8533a7f-1cfa-42ed-cb50-d64a5d9d3ec6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school        6\n",
              "sex           4\n",
              "age           5\n",
              "address       3\n",
              "famsize       3\n",
              "Pstatus       4\n",
              "Medu          4\n",
              "Fedu          5\n",
              "traveltime    3\n",
              "studytime     4\n",
              "failures      2\n",
              "schoolsup     8\n",
              "famsup        7\n",
              "paid          5\n",
              "activities    6\n",
              "nursery       2\n",
              "higher        2\n",
              "internet      1\n",
              "romantic      2\n",
              "famrel        5\n",
              "freetime      4\n",
              "goout         4\n",
              "Dalc          3\n",
              "Walc          5\n",
              "health        7\n",
              "absences      9\n",
              "G1            4\n",
              "G2            0\n",
              "G3            4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 3:\n",
        "dfmod1[['age','Medu','Fedu','traveltime','studytime','failures','famrel','freetime','goout','Dalc','Walc','health','absences','G1','G2','G3']] = dfmod1[['age','Medu','Fedu','traveltime','studytime','failures','famrel','freetime','goout','Dalc','Walc','health','absences','G1','G2','G3']].apply(lambda x: x.fillna(x.mean()))\n",
        "dfmod1[['school','sex','address','famsize','Pstatus','schoolsup','famsup','paid','activities','nursery','higher','internet','romantic']] = dfmod1[['school','sex','address','famsize','Pstatus','schoolsup','famsup','paid','activities','nursery','higher','internet','romantic']].apply(lambda x: x.fillna(x.mode()[0]))"
      ],
      "metadata": {
        "id": "x5ucxEgAJCk6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 3:\n",
        "dfmod1.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSLMP4bbSaLu",
        "outputId": "e8671623-884f-45fb-8601-b74f136d47af"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school        0\n",
              "sex           0\n",
              "age           0\n",
              "address       0\n",
              "famsize       0\n",
              "Pstatus       0\n",
              "Medu          0\n",
              "Fedu          0\n",
              "traveltime    0\n",
              "studytime     0\n",
              "failures      0\n",
              "schoolsup     0\n",
              "famsup        0\n",
              "paid          0\n",
              "activities    0\n",
              "nursery       0\n",
              "higher        0\n",
              "internet      0\n",
              "romantic      0\n",
              "famrel        0\n",
              "freetime      0\n",
              "goout         0\n",
              "Dalc          0\n",
              "Walc          0\n",
              "health        0\n",
              "absences      0\n",
              "G1            0\n",
              "G2            0\n",
              "G3            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4- Encode the categorical columns using get_dummies encoder (10 Marks)."
      ],
      "metadata": {
        "id": "9wSrVC6xxHN5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 4:\n",
        "dfmod2 = pd.get_dummies(dfmod1)\n",
        "dfmod2"
      ],
      "metadata": {
        "id": "MeOdxuo8VKPt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "fee168dc-7628-4181-c5b9-11648da03702"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  Medu  Fedu  traveltime  studytime  failures  famrel  freetime  \\\n",
              "0    18.0   4.0   4.0         2.0        2.0       0.0     4.0       3.0   \n",
              "1    17.0   1.0   1.0         1.0        2.0       0.0     5.0       3.0   \n",
              "2    15.0   1.0   1.0         1.0        2.0       3.0     4.0       3.0   \n",
              "3    15.0   4.0   2.0         1.0        3.0       0.0     3.0       2.0   \n",
              "4    16.0   3.0   3.0         1.0        2.0       0.0     4.0       3.0   \n",
              "..    ...   ...   ...         ...        ...       ...     ...       ...   \n",
              "390  20.0   2.0   2.0         1.0        2.0       2.0     5.0       5.0   \n",
              "391  17.0   3.0   1.0         2.0        1.0       0.0     2.0       4.0   \n",
              "392  21.0   1.0   1.0         1.0        1.0       3.0     5.0       5.0   \n",
              "393  18.0   3.0   2.0         3.0        1.0       0.0     4.0       4.0   \n",
              "394  19.0   1.0   1.0         1.0        1.0       0.0     3.0       2.0   \n",
              "\n",
              "     goout  Dalc  ...  activities_no  activities_yes  nursery_no  nursery_yes  \\\n",
              "0      4.0   1.0  ...              1               0           0            1   \n",
              "1      3.0   1.0  ...              1               0           1            0   \n",
              "2      2.0   2.0  ...              1               0           0            1   \n",
              "3      2.0   1.0  ...              0               1           0            1   \n",
              "4      2.0   1.0  ...              1               0           0            1   \n",
              "..     ...   ...  ...            ...             ...         ...          ...   \n",
              "390    4.0   4.0  ...              1               0           0            1   \n",
              "391    5.0   3.0  ...              1               0           1            0   \n",
              "392    3.0   3.0  ...              1               0           1            0   \n",
              "393    1.0   3.0  ...              1               0           1            0   \n",
              "394    3.0   3.0  ...              1               0           0            1   \n",
              "\n",
              "     higher_no  higher_yes  internet_no  internet_yes  romantic_no  \\\n",
              "0            0           1            1             0            1   \n",
              "1            0           1            0             1            1   \n",
              "2            0           1            0             1            1   \n",
              "3            0           1            0             1            0   \n",
              "4            0           1            1             0            1   \n",
              "..         ...         ...          ...           ...          ...   \n",
              "390          0           1            1             0            1   \n",
              "391          0           1            0             1            1   \n",
              "392          0           1            1             0            1   \n",
              "393          0           1            0             1            1   \n",
              "394          0           1            0             1            1   \n",
              "\n",
              "     romantic_yes  \n",
              "0               0  \n",
              "1               0  \n",
              "2               0  \n",
              "3               1  \n",
              "4               0  \n",
              "..            ...  \n",
              "390             0  \n",
              "391             0  \n",
              "392             0  \n",
              "393             0  \n",
              "394             0  \n",
              "\n",
              "[395 rows x 42 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>...</th>\n",
              "      <th>activities_no</th>\n",
              "      <th>activities_yes</th>\n",
              "      <th>nursery_no</th>\n",
              "      <th>nursery_yes</th>\n",
              "      <th>higher_no</th>\n",
              "      <th>higher_yes</th>\n",
              "      <th>internet_no</th>\n",
              "      <th>internet_yes</th>\n",
              "      <th>romantic_no</th>\n",
              "      <th>romantic_yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>20.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>17.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>18.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 42 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7425457-3358-43b2-9923-2219bdfab1f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b7425457-3358-43b2-9923-2219bdfab1f3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b7425457-3358-43b2-9923-2219bdfab1f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5- Split the data into 80% training set and 20% testing set (10 Marks)."
      ],
      "metadata": {
        "id": "X6y3p_9vxKoW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 5:\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = dfmod2.iloc[:,dfmod2.columns != 'G3']\n",
        "y = dfmod2['G3']\n",
        "SEED = 4\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.20,random_state=SEED)"
      ],
      "metadata": {
        "id": "kdAxwFwRdmHj"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "6- Train a linear regression model to predict G3 (10 Marks)."
      ],
      "metadata": {
        "id": "niIWVHhcxOe0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 6:\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "reg = LinearRegression()\n",
        "reg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = reg.predict(X_test)"
      ],
      "metadata": {
        "id": "K-S4oby3iO-D"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "7- Test on the test dataset, and report on the Mean Absolute Error (MAE) and Mean\n",
        "Squared Error (MSE). Compare predicted sample results (e.g., 5 samples) with the test\n",
        "labels. (10 Marks)."
      ],
      "metadata": {
        "id": "p_rw1NjJxQmV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 7:\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "\n",
        "print(f'MAE: {mean_absolute_error(y_test,y_pred)}')\n",
        "print(f'MSE: {mean_squared_error(y_test,y_pred)}')\n",
        "\n",
        "result = pd.DataFrame({'Prediction':y_pred,'Actual':y_test})\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cttak-cnkCVG",
        "outputId": "908ff39e-d112-4af2-8397-a1f07dc89185"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 1.0936594334630174\n",
            "MSE: 2.604655820481578\n",
            "     Prediction  Actual\n",
            "241   10.569683    12.0\n",
            "70    15.080102    15.0\n",
            "6     11.881046    11.0\n",
            "160    4.197778     0.0\n",
            "186   10.675292    11.0\n",
            "..          ...     ...\n",
            "170    3.887923     0.0\n",
            "298   12.510855    14.0\n",
            "42    18.728213    18.0\n",
            "133   11.607198    11.0\n",
            "394    7.650622     9.0\n",
            "\n",
            "[79 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8- Train a Ridge regression model and a Lasso (alpha=0.5) regression model (10 Marks)."
      ],
      "metadata": {
        "id": "Az2zyM_RxTV-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 8:\n",
        "from sklearn.linear_model import Ridge, Lasso\n",
        "\n",
        "ridge = Ridge(alpha=0.5)\n",
        "ridge.fit(X_train,y_train)\n",
        "\n",
        "y_pred_ridge = ridge.predict(X_test)"
      ],
      "metadata": {
        "id": "J1voruY0lU0K"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 8:\n",
        "lasso = Lasso(alpha=0.5)\n",
        "lasso.fit(X_train,y_train)\n",
        "\n",
        "y_pred_lasso = lasso.predict(X_test)"
      ],
      "metadata": {
        "id": "T8sP4Vbcpa5c"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "9- Test the models trained in step 8 on the test dataset and compare the MAE and MSE\n",
        "results with the ones you got in step 7 (10 Marks)."
      ],
      "metadata": {
        "id": "j2Igm7rqxWau"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 9:\n",
        "print(f'Linear Regression MAE: {mean_absolute_error(y_test,y_pred)}')\n",
        "print(f'Linear Regression MSE: {mean_squared_error(y_test,y_pred)}')\n",
        "\n",
        "print(f'Ridge Regression MAE: {mean_absolute_error(y_test,y_pred_ridge)}')\n",
        "print(f'Ridge Regression MSE: {mean_squared_error(y_test,y_pred_ridge)}')\n",
        "\n",
        "print(f'Lasso Regression MAE: {mean_absolute_error(y_test,y_pred_lasso)}')\n",
        "print(f'Lasso Regression MSE: {mean_squared_error(y_test,y_pred_lasso)}')\n",
        "\n",
        "compare = pd.DataFrame({'Linear Regression Prediction':y_pred,'Ridge Regression Prediction':y_pred_ridge,'Lasso Regression Prediction':y_pred_lasso,'Actual':y_test})\n",
        "print(compare)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8KozH_Tp7en",
        "outputId": "e6cd6afd-1c64-4fb6-faa3-b190fe4048b0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression MAE: 1.0936594334630174\n",
            "Linear Regression MSE: 2.604655820481578\n",
            "Ridge Regression MAE: 1.0925413210466655\n",
            "Ridge Regression MSE: 2.602504428650689\n",
            "Lasso Regression MAE: 1.036977878575666\n",
            "Lasso Regression MSE: 2.7080219626433206\n",
            "     Linear Regression Prediction  Ridge Regression Prediction  \\\n",
            "241                     10.569683                    10.571671   \n",
            "70                      15.080102                    15.077711   \n",
            "6                       11.881046                    11.881474   \n",
            "160                      4.197778                     4.201649   \n",
            "186                     10.675292                    10.678381   \n",
            "..                            ...                          ...   \n",
            "170                      3.887923                     3.890409   \n",
            "298                     12.510855                    12.511824   \n",
            "42                      18.728213                    18.728207   \n",
            "133                     11.607198                    11.608467   \n",
            "394                      7.650622                     7.650001   \n",
            "\n",
            "     Lasso Regression Prediction  Actual  \n",
            "241                    10.482515    12.0  \n",
            "70                     14.651360    15.0  \n",
            "6                      11.605638    11.0  \n",
            "160                     5.222241     0.0  \n",
            "186                    11.562634    11.0  \n",
            "..                           ...     ...  \n",
            "170                     4.142122     0.0  \n",
            "298                    12.783076    14.0  \n",
            "42                     18.237986    18.0  \n",
            "133                    11.057350    11.0  \n",
            "394                     8.403748     9.0  \n",
            "\n",
            "[79 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "10- Use cross validation ( KFold cross_validation_score, default scoring method, n_splits=5)\n",
        "to find the best performing regression algorithm (10 Marks)."
      ],
      "metadata": {
        "id": "9CkJaMKHxaql"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 10:\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val = cross_val_score(reg,X_train,y_train, cv=5)\n",
        "cross_val_ridge = cross_val_score(ridge,X_train,y_train, cv=5)\n",
        "cross_val_lasso = cross_val_score(lasso,X_train,y_train, cv=5)\n",
        "\n",
        "print(f'Linear Regression Cross Validation Score: {cross_val}')\n",
        "print(f'Ridge Regression Cross Validation Score: {cross_val_ridge}')\n",
        "print(f'Lasso Regression Cross Validation Score: {cross_val_lasso}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jw-6lNFlrzfq",
        "outputId": "4068b66c-895c-4c2c-89cd-d08d180f5c23"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Cross Validation Score: [0.76539175 0.76240365 0.77395414 0.77539143 0.78658044]\n",
            "Ridge Regression Cross Validation Score: [0.76568812 0.76303787 0.77429303 0.77539881 0.78708313]\n",
            "Lasso Regression Cross Validation Score: [0.77645158 0.83390143 0.7973046  0.76424178 0.82122692]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "11- Report on cv_result.mean and cv_result.std for the three models (10 Marks).\n"
      ],
      "metadata": {
        "id": "Nw5ayYVnxc9G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Question 11:\n",
        "\n",
        "print(f'Linear Regression Cross Validation Score Mean: {cross_val.mean()}')\n",
        "print(f'Linear Regression Cross Validation Score Standard Deviation: {cross_val.std()}')\n",
        "\n",
        "print(f'Ridge Regression Cross Validation Score Mean: {cross_val_ridge.mean()}')\n",
        "print(f'Ridge Regression Cross Validation Score Standard Deviation: {cross_val_ridge.std()}')\n",
        "\n",
        "print(f'Lasso Regression Cross Validation Score Mean: {cross_val_lasso.mean()}')\n",
        "print(f'Lasso Regression Cross Validation Score Standard Deviation: {cross_val_lasso.std()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyTXt1BgvCAW",
        "outputId": "63b9b5e5-928f-4949-eba4-a561482b2343"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Cross Validation Score Mean: 0.7727442830791755\n",
            "Linear Regression Cross Validation Score Standard Deviation: 0.008495868074566013\n",
            "Ridge Regression Cross Validation Score Mean: 0.7731001901000127\n",
            "Ridge Regression Cross Validation Score Standard Deviation: 0.008466621759108275\n",
            "Lasso Regression Cross Validation Score Mean: 0.7986252636764622\n",
            "Lasso Regression Cross Validation Score Standard Deviation: 0.02619497293838924\n"
          ]
        }
      ]
    }
  ]
}