Exam.ipynb

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      "metadata": {
        "id": "15YZ_EYkjHyD"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('sample_data/Expanded_data_with_more_features.csv')"
      ],
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        "id": "_GT6Y0JtjK4A"
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      "execution_count": 5,
      "outputs": []
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        "df.head()"
      ],
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      "execution_count": 40,
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            "text/plain": [
              "   Unnamed: 0  Gender EthnicGroup          ParentEduc     LunchType TestPrep  \\\n",
              "0           0  female         NaN   bachelor's degree      standard     none   \n",
              "1           1  female     group C        some college      standard      NaN   \n",
              "2           2  female     group B     master's degree      standard     none   \n",
              "3           3    male     group A  associate's degree  free/reduced     none   \n",
              "4           4    male     group C        some college      standard     none   \n",
              "\n",
              "  ParentMaritalStatus PracticeSport IsFirstChild  NrSiblings TransportMeans  \\\n",
              "0             married     regularly          yes         3.0     school_bus   \n",
              "1             married     sometimes          yes         0.0            NaN   \n",
              "2              single     sometimes          yes         4.0     school_bus   \n",
              "3             married         never           no         1.0            NaN   \n",
              "4             married     sometimes          yes         0.0     school_bus   \n",
              "\n",
              "  WklyStudyHours  MathScore  ReadingScore  WritingScore  \n",
              "0            < 5         71            71            74  \n",
              "1         5 - 10         69            90            88  \n",
              "2            < 5         87            93            91  \n",
              "3         5 - 10         45            56            42  \n",
              "4         5 - 10         76            78            75  "
            ],
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              "      <td>88</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>female</td>\n",
              "      <td>group B</td>\n",
              "      <td>master's degree</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>single</td>\n",
              "      <td>sometimes</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>school_bus</td>\n",
              "      <td>&lt; 5</td>\n",
              "      <td>87</td>\n",
              "      <td>93</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>group A</td>\n",
              "      <td>associate's degree</td>\n",
              "      <td>free/reduced</td>\n",
              "      <td>none</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>42</td>\n",
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              "      <th>4</th>\n",
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              "      <td>male</td>\n",
              "      <td>group C</td>\n",
              "      <td>some college</td>\n",
              "      <td>standard</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>school_bus</td>\n",
              "      <td>5 - 10</td>\n",
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      "cell_type": "code",
      "source": [
        "# 1\n",
        "female = len(df[df['Gender'] == 'female'])\n",
        "male = len(df[df['Gender'] == 'male'])\n",
        "female, male"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PyxPKfgnysU",
        "outputId": "d431a7e4-a8b3-4997-b3a6-25ebecdbe6d5"
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      "execution_count": 13,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15424, 15217)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2, 3\n",
        "fem_percentage = (female / len(df)) * 100\n",
        "male_percentage = (male / len(df)) * 100\n",
        "fem_percentage, male_percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9vFI01xoRgE",
        "outputId": "37a5911e-f427-4b27-e1d2-4c2e8cae8527"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50.337782709441605, 49.6622172905584)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4, 5, 6\n",
        "math_df = df.groupby('EthnicGroup')['MathScore'].mean()\n",
        "math_res = math_df.keys()[math_df.argmax()]\n",
        "\n",
        "read_df = df.groupby('EthnicGroup')['ReadingScore'].mean()\n",
        "read_res = math_df.keys()[math_df.argmax()]\n",
        "\n",
        "write_df = df.groupby('EthnicGroup')['WritingScore'].mean()\n",
        "write_res = math_df.keys()[math_df.argmax()]\n",
        "\n",
        "math_res, read_res, write_res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Awdm4_FVo-ho",
        "outputId": "a1296dbf-eee3-49fd-8547-6230fbf6c63f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('group E', 'group E', 'group E')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7, 8, 9\n",
        "mean_math = df[df[\"TestPrep\"] == 'completed'][\"MathScore\"].mean()\n",
        "mean_read = df[df[\"TestPrep\"] == 'completed'][\"ReadingScore\"].mean()\n",
        "mean_write = df[df[\"TestPrep\"] == 'completed'][\"WritingScore\"].mean()\n",
        "mean = (mean_math + mean_read + mean_write) / 3\n",
        "mean_math, mean_read, mean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QKZxJ52pSno",
        "outputId": "914e6cb1-2aad-408c-ddb9-18262d65a452"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(69.5466599698644, 73.73299849321948, 72.66097438473129)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10, 11\n",
        "single = len(df[df['ParentMaritalStatus'] == 'single'])\n",
        "married = len(df[df['ParentMaritalStatus'] == 'married'])\n",
        "single, married"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNG8gYulpVhL",
        "outputId": "f2fe6980-52fa-47b6-d41a-0cb037daacc5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7097, 16844)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 12, 13\n",
        "regullary_math = df[df[\"PracticeSport\"] == 'regularly'][\"MathScore\"].mean()\n",
        "never_math = df[df[\"PracticeSport\"] == 'never'][\"MathScore\"].mean()\n",
        "\n",
        "regullary_read = df[df[\"PracticeSport\"] == 'regularly'][\"ReadingScore\"].mean()\n",
        "never_read = df[df[\"PracticeSport\"] == 'never'][\"ReadingScore\"].mean()\n",
        "regullary_math > never_math, regullary_read > never_read"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5XRo1KJpgfh",
        "outputId": "f8e021c6-af8e-40c7-820e-f7e5e5052cb8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(True, True)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 14, 15\n",
        "bus = len(df[df['TransportMeans'] == 'school_bus'])\n",
        "private = len(df[df['TransportMeans'] == 'private'])\n",
        "bus, private"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbuJKc_opvdI",
        "outputId": "5d7ee9ee-83fa-4f57-888c-95e1e5e66c26"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16145, 11362)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification"
      ],
      "metadata": {
        "id": "qhF8whFPp7vc"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn as sk\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import ShuffleSplit"
      ],
      "metadata": {
        "id": "iFEbI6MWtPA9"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols = [\n",
        "    'Gender', \n",
        "    'EthnicGroup', \n",
        "    'ParentEduc', \n",
        "    'LunchType', \n",
        "    'ParentMaritalStatus', \n",
        "    'PracticeSport', \n",
        "    'IsFirstChild', \n",
        "    'NrSiblings',       \n",
        "    'TransportMeans', \n",
        "    'WklyStudyHours', \n",
        "    'MathScore', \n",
        "    'ReadingScore', \n",
        "    'WritingScore'\n",
        "]"
      ],
      "metadata": {
        "id": "Y2FnFFvitjAr"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Outcome'] = df['TestPrep'].isin(['completed'])\n",
        "df[cols + ['Outcome']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "fMugY65RtuaM",
        "outputId": "73d1725c-bc4b-41ec-d750-3fabe8da1af6"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Gender EthnicGroup          ParentEduc     LunchType  \\\n",
              "0      female         NaN   bachelor's degree      standard   \n",
              "1      female     group C        some college      standard   \n",
              "2      female     group B     master's degree      standard   \n",
              "3        male     group A  associate's degree  free/reduced   \n",
              "4        male     group C        some college      standard   \n",
              "...       ...         ...                 ...           ...   \n",
              "30636  female     group D         high school      standard   \n",
              "30637    male     group E         high school      standard   \n",
              "30638  female         NaN         high school  free/reduced   \n",
              "30639  female     group D  associate's degree      standard   \n",
              "30640    male     group B        some college      standard   \n",
              "\n",
              "      ParentMaritalStatus PracticeSport IsFirstChild  NrSiblings  \\\n",
              "0                 married     regularly          yes         3.0   \n",
              "1                 married     sometimes          yes         0.0   \n",
              "2                  single     sometimes          yes         4.0   \n",
              "3                 married         never           no         1.0   \n",
              "4                 married     sometimes          yes         0.0   \n",
              "...                   ...           ...          ...         ...   \n",
              "30636              single     sometimes           no         2.0   \n",
              "30637              single     regularly           no         1.0   \n",
              "30638             married     sometimes           no         1.0   \n",
              "30639             married     regularly           no         3.0   \n",
              "30640             married         never           no         1.0   \n",
              "\n",
              "      TransportMeans WklyStudyHours  MathScore  ReadingScore  WritingScore  \\\n",
              "0         school_bus            < 5         71            71            74   \n",
              "1                NaN         5 - 10         69            90            88   \n",
              "2         school_bus            < 5         87            93            91   \n",
              "3                NaN         5 - 10         45            56            42   \n",
              "4         school_bus         5 - 10         76            78            75   \n",
              "...              ...            ...        ...           ...           ...   \n",
              "30636     school_bus         5 - 10         59            61            65   \n",
              "30637        private         5 - 10         58            53            51   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 58
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_features = [\n",
        "    'MathScore', \n",
        "    'ReadingScore', \n",
        "    'WritingScore'\n",
        "]\n",
        "cat_features = [\n",
        "    'Gender', \n",
        "    #'EthnicGroup', \n",
        "    'ParentEduc', \n",
        "    'LunchType', \n",
        "    'ParentMaritalStatus', \n",
        "    'PracticeSport',\n",
        "    'IsFirstChild',\n",
        "    'TransportMeans',\n",
        "    'WklyStudyHours',\n",
        "    'NrSiblings'\n",
        "]"
      ],
      "metadata": {
        "id": "Ws8l57a_twng"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_pipeline = Pipeline([\n",
        "    ('imputer', SimpleImputer(strategy=\"most_frequent\")),\n",
        "    ('scaler', StandardScaler()),\n",
        "])\n",
        "\n",
        "pipeline = ColumnTransformer([\n",
        "    (\"num\", num_pipeline, num_features),\n",
        "    (\"cat\", OneHotEncoder(), cat_features)\n",
        "])"
      ],
      "metadata": {
        "id": "uHeOi_dmt245"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pipeline.fit_transform(df)\n",
        "Y = df['IsTestCompleted'].values"
      ],
      "metadata": {
        "id": "wn1E_Pw1vHpc"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv = ShuffleSplit(n_splits=3, test_size=0.1)\n",
        "cross_val_score(LogisticRegression(), X, Y, cv=cv).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KhL3yCBt44K",
        "outputId": "07a101dc-1fb2-4dee-98ae-714c8dd348c1"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7510603588907014"
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          },
          "metadata": {},
          "execution_count": 69
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XukOo4gju7sd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}