model_selection.ipynb

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      "source": [
        "# Classifications methods"
      ],
      "metadata": {
        "id": "Ufi1oid4JC2P"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFq-0-o8G0-f"
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      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/trial_data.csv')\n",
        "df"
      ],
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        "outputId": "a8f3071d-ac86-4b70-9f98-e35fca673deb"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id first_name last_name                            email  gender  \\\n",
              "0      1        Ava   Johnson        ava.johnson.1@example.com  female   \n",
              "1      2       Liam     Smith         liam.smith.2@example.com    male   \n",
              "2      3       Emma     Brown         emma.brown.3@example.com  female   \n",
              "3      4       Noah    Garcia        noah.garcia.4@example.com    male   \n",
              "4      5     Olivia    Wilson      olivia.wilson.5@example.com  female   \n",
              "..   ...        ...       ...                              ...     ...   \n",
              "495  496        Mia  Thompson     mia.thompson.496@example.com  female   \n",
              "496  497      Ethan    Wright     ethan.wright.497@example.com    male   \n",
              "497  498     Sophia       Lee       sophia.lee.498@example.com  female   \n",
              "498  499    Lillian   Johnson  lillian.johnson.499@example.com  female   \n",
              "499  500     Nathan  Gonzalez  nathan.gonzalez.500@example.com    male   \n",
              "\n",
              "     part_time_job  absence_days  extracurricular_activities  \\\n",
              "0            False             3                        True   \n",
              "1             True             1                       False   \n",
              "2            False             2                        True   \n",
              "3            False             0                        True   \n",
              "4             True             4                       False   \n",
              "..             ...           ...                         ...   \n",
              "495          False             3                        True   \n",
              "496           True             4                       False   \n",
              "497          False             1                        True   \n",
              "498           True             2                        True   \n",
              "499          False             3                        True   \n",
              "\n",
              "     weekly_self_study_hours   career_aspiration  math_score  history_score  \\\n",
              "0                         25              Lawyer          82             75   \n",
              "1                         30              Doctor          90             85   \n",
              "2                         20  Government Officer          78             80   \n",
              "3                         35            Engineer          95             88   \n",
              "4                         28             Teacher          76             72   \n",
              "..                       ...                 ...         ...            ...   \n",
              "495                       25          Researcher          88             81   \n",
              "496                       30              Doctor          90             86   \n",
              "497                       25          Pharmacist          85             80   \n",
              "498                       20              Artist          90             82   \n",
              "499                       25          Researcher          88             82   \n",
              "\n",
              "     physics_score  chemistry_score  biology_score  english_score  \\\n",
              "0               88               90             80             85   \n",
              "1               92               87             83             88   \n",
              "2               84               85             79             76   \n",
              "3               90               92             85             87   \n",
              "4               88               80             77             81   \n",
              "..             ...              ...            ...            ...   \n",
              "495             88               89             82             79   \n",
              "496             89               88             81             80   \n",
              "497             88               90             82             81   \n",
              "498             90               88             81             78   \n",
              "499             88               89             83             79   \n",
              "\n",
              "     geography_score  \n",
              "0                 78  \n",
              "1                 81  \n",
              "2                 82  \n",
              "3                 90  \n",
              "4                 83  \n",
              "..               ...  \n",
              "495               83  \n",
              "496               84  \n",
              "497               82  \n",
              "498               84  \n",
              "499               83  \n",
              "\n",
              "[500 rows x 17 columns]"
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              "      <td>87</td>\n",
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              "      <td>female</td>\n",
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              "      <td>True</td>\n",
              "      <td>20</td>\n",
              "      <td>Government Officer</td>\n",
              "      <td>78</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
              "      <td>85</td>\n",
              "      <td>79</td>\n",
              "      <td>76</td>\n",
              "      <td>82</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Noah</td>\n",
              "      <td>Garcia</td>\n",
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              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>35</td>\n",
              "      <td>Engineer</td>\n",
              "      <td>95</td>\n",
              "      <td>88</td>\n",
              "      <td>90</td>\n",
              "      <td>92</td>\n",
              "      <td>85</td>\n",
              "      <td>87</td>\n",
              "      <td>90</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Olivia</td>\n",
              "      <td>Wilson</td>\n",
              "      <td>olivia.wilson.5@example.com</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>28</td>\n",
              "      <td>Teacher</td>\n",
              "      <td>76</td>\n",
              "      <td>72</td>\n",
              "      <td>88</td>\n",
              "      <td>80</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>83</td>\n",
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              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>496</td>\n",
              "      <td>Mia</td>\n",
              "      <td>Thompson</td>\n",
              "      <td>mia.thompson.496@example.com</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>25</td>\n",
              "      <td>Researcher</td>\n",
              "      <td>88</td>\n",
              "      <td>81</td>\n",
              "      <td>88</td>\n",
              "      <td>89</td>\n",
              "      <td>82</td>\n",
              "      <td>79</td>\n",
              "      <td>83</td>\n",
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              "      <td>497</td>\n",
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              "      <td>Doctor</td>\n",
              "      <td>90</td>\n",
              "      <td>86</td>\n",
              "      <td>89</td>\n",
              "      <td>88</td>\n",
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              "      <td>Sophia</td>\n",
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              "      <td>499</td>\n",
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              "<p>500 rows × 17 columns</p>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 500,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 144,\n        \"min\": 1,\n        \"max\": 500,\n        \"num_unique_values\": 500,\n        \"samples\": [\n          362,\n          74,\n          375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"first_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 57,\n        \"samples\": [\n          \"Ava\",\n          \"Lucas\",\n          \"Audrey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"Clark\",\n          \"Wright\",\n          \"Martin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 500,\n        \"samples\": [\n          \"nathan.lee.362@example.com\",\n          \"grace.smith.74@example.com\",\n          \"oliver.gonzalez.375@example.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"part_time_job\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"absence_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"extracurricular_activities\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekly_self_study_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 15,\n        \"max\": 35,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Researcher\",\n          \"Software Developer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 74,\n        \"max\": 95,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          82,\n          89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 72,\n        \"max\": 90,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          75,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 79,\n        \"max\": 92,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          91,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 78,\n        \"max\": 93,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          90,\n          87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 75,\n        \"max\": 89,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          78,\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 76,\n        \"max\": 90,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          83,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 77,\n        \"max\": 90,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          89,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['first_name','last_name','email'])"
      ],
      "metadata": {
        "id": "0-_z5qMcH_oJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns={'absence_days': 'attendance_percent'})"
      ],
      "metadata": {
        "id": "jBNxA0mJIWhp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['attendance_percent'] = df['attendance_percent'] * 10"
      ],
      "metadata": {
        "id": "wRdFMWfrHtvy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_df = df.select_dtypes(include='number')\n",
        "\n",
        "medians = numeric_df.median()\n",
        "df[numeric_df.columns] = df[numeric_df.columns].fillna(medians)"
      ],
      "metadata": {
        "id": "Z4qoH2ZhHjCi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode categorical variables into numeric values\n",
        "label_encoder = LabelEncoder()\n",
        "df['gender'] = label_encoder.fit_transform(df['gender'])\n",
        "df['part_time_job'] = label_encoder.fit_transform(df['part_time_job'])\n",
        "df['extracurricular_activities'] = label_encoder.fit_transform(df['extracurricular_activities'])"
      ],
      "metadata": {
        "id": "i0IduOWNHklq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the average score across all subjects\n",
        "df['average_score'] = df[['math_score', 'history_score', 'physics_score',\n",
        "                          'chemistry_score', 'biology_score', 'english_score',\n",
        "                          'geography_score']].mean(axis=1)"
      ],
      "metadata": {
        "id": "4HYcl7RSHmN2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
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        "id": "IU8-oTJhJJB0",
        "outputId": "086903f7-d8d0-4a60-de3f-09f7cd850315"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  gender  part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0   1       0              0                  30                           1   \n",
              "1   2       1              1                  10                           0   \n",
              "2   3       0              0                  20                           1   \n",
              "3   4       1              0                   0                           1   \n",
              "4   5       0              1                  40                           0   \n",
              "\n",
              "   weekly_self_study_hours   career_aspiration  math_score  history_score  \\\n",
              "0                       25              Lawyer          82             75   \n",
              "1                       30              Doctor          90             85   \n",
              "2                       20  Government Officer          78             80   \n",
              "3                       35            Engineer          95             88   \n",
              "4                       28             Teacher          76             72   \n",
              "\n",
              "   physics_score  chemistry_score  biology_score  english_score  \\\n",
              "0             88               90             80             85   \n",
              "1             92               87             83             88   \n",
              "2             84               85             79             76   \n",
              "3             90               92             85             87   \n",
              "4             88               80             77             81   \n",
              "\n",
              "   geography_score  average_score  \n",
              "0               78      82.571429  \n",
              "1               81      86.571429  \n",
              "2               82      80.571429  \n",
              "3               90      89.571429  \n",
              "4               83      79.571429  "
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-787c411f-4d22-456e-93bd-ebf9206def11');\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",
              "\n",
              "\n",
              "<div id=\"df-d9f25315-4c2a-48a3-9285-b141f23e560d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9f25315-4c2a-48a3-9285-b141f23e560d')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d9f25315-4c2a-48a3-9285-b141f23e560d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 500,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 144,\n        \"min\": 1,\n        \"max\": 500,\n        \"num_unique_values\": 500,\n        \"samples\": [\n          362,\n          74,\n          375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"part_time_job\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attendance_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 50,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          30,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"extracurricular_activities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekly_self_study_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 15,\n        \"max\": 35,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Researcher\",\n          \"Software Developer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 74,\n        \"max\": 95,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          82,\n          89\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 72,\n        \"max\": 90,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          75,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 79,\n        \"max\": 92,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          91,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 78,\n        \"max\": 93,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          90,\n          87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 75,\n        \"max\": 89,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          78,\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 76,\n        \"max\": 90,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          83,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 77,\n        \"max\": 90,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          89,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.058764782039219,\n        \"min\": 78.14285714285714,\n        \"max\": 89.57142857142857,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          85.85714285714286,\n          82.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "performance_threshold = 70 # score\n",
        "attendance_threshold =  50# attendance\n",
        "\n",
        "# Define the risk classification based on performance and attendance\n",
        "df['at_risk'] = ((df['average_score'] < performance_threshold) |\n",
        "                 (df['attendance_percent'] < attendance_threshold)).astype(int)\n",
        "\n",
        "\n",
        "# Splitting data into features (X) and target (y)\n",
        "X = df[['average_score', 'attendance_percent', 'part_time_job', 'extracurricular_activities', 'weekly_self_study_hours']]\n",
        "y = df['at_risk']"
      ],
      "metadata": {
        "id": "uuGYpcCbH0y8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "  # Assuming `df` is your preprocessed dataframe\n",
        "X = df[['average_score', 'attendance_percent', 'part_time_job', 'extracurricular_activities', 'weekly_self_study_hours']]\n",
        "y = df['at_risk']  # Target variable\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "eEuyOgpyG8uj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, X_train, X_test, y_train, y_test):\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    print(f\"Model: {model.__class__.__name__}\")\n",
        "    print(f\"Accuracy: {accuracy_score(y_test, y_pred):.4f}\")\n",
        "    print(classification_report(y_test, y_pred))\n",
        "\n",
        "    # Confusion matrix\n",
        "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "    plt.title(f\"Confusion Matrix - {model.__class__.__name__}\")\n",
        "    plt.show()\n",
        "\n",
        "    print(\"Testing Accuracy\")\n",
        "    print(model.score(X_test, y_test))\n",
        "    print(\"Training Accuracy\")\n",
        "    print(model.score(X_train, y_train))"
      ],
      "metadata": {
        "id": "1oLsDgvWG_bd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Decision Tree Classifier\n",
        "dt_model = DecisionTreeClassifier(random_state=42)\n",
        "evaluate_model(dt_model, X_train, X_test, y_train, y_test)\n",
        "\n",
        "# 2. Random Forest Classifier\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "evaluate_model(rf_model, X_train, X_test, y_train, y_test)\n",
        "\n",
        "# 3. Gradient Boosting Classifier\n",
        "gb_model = GradientBoostingClassifier(random_state=42)\n",
        "evaluate_model(gb_model, X_train, X_test, y_train, y_test)\n",
        "\n",
        "# 4. K-Nearest Neighbors\n",
        "knn_model = KNeighborsClassifier()\n",
        "evaluate_model(knn_model, X_train, X_test, y_train, y_test)\n",
        "\n",
        "# 5. Support Vector Classifier\n",
        "svc_model = SVC(random_state=42)\n",
        "evaluate_model(svc_model, X_train, X_test, y_train, y_test)\n",
        "\n",
        "# 6. Logistic Regression\n",
        "lr_model = LogisticRegression(random_state=42)\n",
        "evaluate_model(lr_model, X_train, X_test, y_train, y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QlH2vTZPHBBR",
        "outputId": "633a097b-508b-4bad-df6c-5d16bc32791d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: DecisionTreeClassifier\n",
            "Accuracy: 1.0000\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00        99\n",
            "\n",
            "    accuracy                           1.00       100\n",
            "   macro avg       1.00      1.00      1.00       100\n",
            "weighted avg       1.00      1.00      1.00       100\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "1.0\n",
            "Training Accuracy\n",
            "1.0\n",
            "Model: RandomForestClassifier\n",
            "Accuracy: 1.0000\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00        99\n",
            "\n",
            "    accuracy                           1.00       100\n",
            "   macro avg       1.00      1.00      1.00       100\n",
            "weighted avg       1.00      1.00      1.00       100\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "1.0\n",
            "Training Accuracy\n",
            "1.0\n",
            "Model: GradientBoostingClassifier\n",
            "Accuracy: 1.0000\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00        99\n",
            "\n",
            "    accuracy                           1.00       100\n",
            "   macro avg       1.00      1.00      1.00       100\n",
            "weighted avg       1.00      1.00      1.00       100\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "1.0\n",
            "Training Accuracy\n",
            "1.0\n",
            "Model: KNeighborsClassifier\n",
            "Accuracy: 0.9900\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         1\n",
            "           1       0.99      1.00      0.99        99\n",
            "\n",
            "    accuracy                           0.99       100\n",
            "   macro avg       0.49      0.50      0.50       100\n",
            "weighted avg       0.98      0.99      0.99       100\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "0.99\n",
            "Training Accuracy\n",
            "0.995\n",
            "Model: SVC\n",
            "Accuracy: 0.9900\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00         1\n",
            "           1       0.99      1.00      0.99        99\n",
            "\n",
            "    accuracy                           0.99       100\n",
            "   macro avg       0.49      0.50      0.50       100\n",
            "weighted avg       0.98      0.99      0.99       100\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "0.99\n",
            "Training Accuracy\n",
            "0.995\n",
            "Model: LogisticRegression\n",
            "Accuracy: 1.0000\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00         1\n",
            "           1       1.00      1.00      1.00        99\n",
            "\n",
            "    accuracy                           1.00       100\n",
            "   macro avg       1.00      1.00      1.00       100\n",
            "weighted avg       1.00      1.00      1.00       100\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "1.0\n",
            "Training Accuracy\n",
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K fold Cross Verification"
      ],
      "metadata": {
        "id": "QoNPhNJ7Q9u0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import numpy as np\n",
        "\n",
        "# Define the models\n",
        "models = {\n",
        "    \"DecisionTreeClassifier\": DecisionTreeClassifier(),\n",
        "    \"RandomForestClassifier\": RandomForestClassifier(),\n",
        "    \"GradientBoostingClassifier\": GradientBoostingClassifier(),\n",
        "    \"KNeighborsClassifier\": KNeighborsClassifier(),\n",
        "    \"SVC\": SVC(),\n",
        "    \"LogisticRegression\": LogisticRegression()\n",
        "}\n",
        "\n",
        "# Perform K-fold cross-validation for each model\n",
        "k = 5  # Number of folds\n",
        "for name, model in models.items():\n",
        "    scores = cross_val_score(model, X, y, cv=k)\n",
        "    print(f\"Model: {name}\")\n",
        "    print(f\"Cross-Validation Accuracy: {np.mean(scores):.4f}\")\n",
        "    print(f\"Cross-Validation Scores: {scores}\")\n",
        "    print(\"-\" * 50)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJmoeRmDQ_v9",
        "outputId": "d34c2c93-ae99-423e-9459-2d31a9df5584"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: DecisionTreeClassifier\n",
            "Cross-Validation Accuracy: 0.9960\n",
            "Cross-Validation Scores: [0.99 1.   1.   1.   0.99]\n",
            "--------------------------------------------------\n",
            "Model: RandomForestClassifier\n",
            "Cross-Validation Accuracy: 0.9980\n",
            "Cross-Validation Scores: [1.   1.   1.   1.   0.99]\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: GradientBoostingClassifier\n",
            "Cross-Validation Accuracy: 0.9960\n",
            "Cross-Validation Scores: [0.99 1.   1.   1.   0.99]\n",
            "--------------------------------------------------\n",
            "Model: KNeighborsClassifier\n",
            "Cross-Validation Accuracy: 0.9940\n",
            "Cross-Validation Scores: [1.   1.   0.99 0.99 0.99]\n",
            "--------------------------------------------------\n",
            "Model: SVC\n",
            "Cross-Validation Accuracy: 0.9940\n",
            "Cross-Validation Scores: [1.   1.   0.99 0.99 0.99]\n",
            "--------------------------------------------------\n",
            "Model: LogisticRegression\n",
            "Cross-Validation Accuracy: 1.0000\n",
            "Cross-Validation Scores: [1. 1. 1. 1. 1.]\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import StackingClassifier\n",
        "# Base models\n",
        "base_models = [\n",
        "    ('svc', SVC(probability=True))  # Set probability=True to enable stacking\n",
        "]\n",
        "\n",
        "# Final model\n",
        "final_model = SVC()\n",
        "\n",
        "# Stacking Classifier\n",
        "stacking_model = StackingClassifier(estimators=base_models, final_estimator=final_model)\n",
        "\n",
        "# Fit the model\n",
        "stacking_model.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate\n",
        "accuracy = stacking_model.score(X_test, y_test)\n",
        "print(f'Stacking Model Accuracy: {accuracy:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxQD4tx0SLkP",
        "outputId": "620694f6-d0c3-4352-994a-0df38570e203"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stacking Model Accuracy: 0.9900\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:776: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regression"
      ],
      "metadata": {
        "id": "iQF9IWhGPdoz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_attrition_score(row):\n",
        "    score_factor = (100 - row['average_score']) / 100  # Lower score increases attrition\n",
        "    attendance_factor = (100 - row['attendance_percent']) / 100  # Lower attendance increases attrition\n",
        "    job_factor = row['part_time_job'] * 0.2  # If part-time job, increase attrition\n",
        "    activity_factor = row['extracurricular_activities'] * 0.1  # If involved in extracurriculars, increase attrition\n",
        "    weekly_study_factor = row['weekly_self_study_hours'] * 0.01\n",
        "\n",
        "    # Combine the factors into an attrition score\n",
        "    attrition_score = score_factor + attendance_factor + job_factor + activity_factor + weekly_study_factor\n",
        "    return attrition_score\n",
        "\n",
        "# Apply the function to each row in the dataset\n",
        "df['attrition_score'] = df.apply(calculate_attrition_score, axis=1)"
      ],
      "metadata": {
        "id": "QRm2TAmyPdXG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df.head()"
      ],
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          "height": 226
        },
        "id": "_8YxOjPhPijJ",
        "outputId": "508104e3-5974-46b1-d67f-db46766ba653"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  gender  part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0   1       0              0                  30                           1   \n",
              "1   2       1              1                  10                           0   \n",
              "2   3       0              0                  20                           1   \n",
              "3   4       1              0                   0                           1   \n",
              "4   5       0              1                  40                           0   \n",
              "\n",
              "   weekly_self_study_hours   career_aspiration  math_score  history_score  \\\n",
              "0                       25              Lawyer          82             75   \n",
              "1                       30              Doctor          90             85   \n",
              "2                       20  Government Officer          78             80   \n",
              "3                       35            Engineer          95             88   \n",
              "4                       28             Teacher          76             72   \n",
              "\n",
              "   physics_score  chemistry_score  biology_score  english_score  \\\n",
              "0             88               90             80             85   \n",
              "1             92               87             83             88   \n",
              "2             84               85             79             76   \n",
              "3             90               92             85             87   \n",
              "4             88               80             77             81   \n",
              "\n",
              "   geography_score  average_score  at_risk  attrition_score  \n",
              "0               78      82.571429        1         1.224286  \n",
              "1               81      86.571429        1         1.534286  \n",
              "2               82      80.571429        1         1.294286  \n",
              "3               90      89.571429        1         1.554286  \n",
              "4               83      79.571429        1         1.284286  "
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2,\n        \"min\": 72,\n        \"max\": 90,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          75,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 79,\n        \"max\": 92,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          91,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 78,\n        \"max\": 93,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          90,\n          87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 75,\n        \"max\": 89,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          78,\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 76,\n        \"max\": 90,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          83,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 77,\n        \"max\": 90,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          89,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.058764782039219,\n        \"min\": 78.14285714285714,\n        \"max\": 89.57142857142857,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          85.85714285714286,\n          82.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at_risk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attrition_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1645923306331728,\n        \"min\": 0.8457142857142856,\n        \"max\": 1.7457142857142858,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          1.3757142857142857,\n          1.2014285714285713\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_regression_model(model, X_train, X_test, y_train, y_test):\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Metrics\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "    rmse = np.sqrt(mse)  # Root Mean Squared Error\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "    # Print the results\n",
        "    print(f\"Model: {model.__class__.__name__}\")\n",
        "    print(f\"MSE: {mse:.4f}\")\n",
        "    print(f\"RMSE: {rmse:.4f}\")\n",
        "    print(f\"R²: {r2:.4f}\")\n",
        "\n",
        "    return {\"model\": model.__class__.__name__,\n",
        "            \"MSE\": mse,\n",
        "            \"RMSE\": rmse,\n",
        "            \"R²\": r2}\n"
      ],
      "metadata": {
        "id": "2k03ZrIzPwlZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_regression = []\n",
        "\n",
        "# Linear Regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "results_regression.append(evaluate_regression_model(lin_reg, X_train, X_test, y_train, y_test))\n",
        "\n",
        "# Ridge Regression\n",
        "from sklearn.linear_model import Ridge\n",
        "ridge_reg = Ridge(alpha=1.0)\n",
        "results_regression.append(evaluate_regression_model(ridge_reg, X_train, X_test, y_train, y_test))\n",
        "\n",
        "# Lasso Regression\n",
        "from sklearn.linear_model import Lasso\n",
        "lasso_reg = Lasso(alpha=0.1)\n",
        "results_regression.append(evaluate_regression_model(lasso_reg, X_train, X_test, y_train, y_test))\n",
        "\n",
        "# Decision Tree Regressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "dt_reg = DecisionTreeRegressor(random_state=42)\n",
        "results_regression.append(evaluate_regression_model(dt_reg, X_train, X_test, y_train, y_test))\n",
        "\n",
        "# Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf_reg = RandomForestRegressor(random_state=42)\n",
        "results_regression.append(evaluate_regression_model(rf_reg, X_train, X_test, y_train, y_test))\n",
        "\n",
        "# Gradient Boosting Regressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "gb_reg = GradientBoostingRegressor(random_state=42)\n",
        "results_regression.append(evaluate_regression_model(gb_reg, X_train, X_test, y_train, y_test))\n",
        "\n",
        "# Support Vector Regressor\n",
        "from sklearn.svm import SVR\n",
        "svr_reg = SVR()\n",
        "results_regression.append(evaluate_regression_model(svr_reg, X_train, X_test, y_train, y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8_Nz1sUP1fh",
        "outputId": "a6430e6f-9d3a-48a0-d3fb-1247bfc10876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: LinearRegression\n",
            "MSE: 0.0088\n",
            "RMSE: 0.0939\n",
            "R²: 0.1098\n",
            "Model: Ridge\n",
            "MSE: 0.0088\n",
            "RMSE: 0.0939\n",
            "R²: 0.1098\n",
            "Model: Lasso\n",
            "MSE: 0.0098\n",
            "RMSE: 0.0991\n",
            "R²: 0.0077\n",
            "Model: DecisionTreeRegressor\n",
            "MSE: 0.0000\n",
            "RMSE: 0.0000\n",
            "R²: 1.0000\n",
            "Model: RandomForestRegressor\n",
            "MSE: 0.0002\n",
            "RMSE: 0.0140\n",
            "R²: 0.9802\n",
            "Model: GradientBoostingRegressor\n",
            "MSE: 0.0000\n",
            "RMSE: 0.0000\n",
            "R²: 1.0000\n",
            "Model: SVR\n",
            "MSE: 0.0100\n",
            "RMSE: 0.0998\n",
            "R²: -0.0058\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  from sklearn.model_selection import GridSearchCV\n",
        "  from sklearn.ensemble import RandomForestRegressor\n",
        "  from sklearn.metrics import mean_squared_error, r2_score\n",
        "  import numpy as np\n",
        "\n",
        "  # Prepare your features and target variable\n",
        "  X = df[['average_score', 'attendance_percent', 'part_time_job', 'extracurricular_activities', 'weekly_self_study_hours']]\n",
        "  y = df['attrition_score']\n",
        "\n",
        "  # Initialize the Random Forest Regressor\n",
        "  rf_model = RandomForestRegressor(random_state=42)\n",
        "\n",
        "  # Define the hyperparameters to tune\n",
        "  param_grid = {\n",
        "      'n_estimators': [50, 100, 200],\n",
        "      'max_depth': [None, 10, 20, 30],\n",
        "      'min_samples_split': [2, 5, 10],\n",
        "      'min_samples_leaf': [1, 2, 4],\n",
        "      'max_features': ['auto', 'sqrt']\n",
        "  }\n",
        "\n",
        "  # Set up Grid Search with Cross-Validation\n",
        "  grid_search = GridSearchCV(estimator=rf_model,\n",
        "                            param_grid=param_grid,\n",
        "                            scoring='neg_mean_squared_error',\n",
        "                            cv=5,\n",
        "                            verbose=2,\n",
        "                            n_jobs=-1)\n",
        "\n",
        "  # Fit the model\n",
        "  grid_search.fit(X, y)\n",
        "\n",
        "  # Get the best hyperparameters\n",
        "  best_params = grid_search.best_params_\n",
        "  best_model = grid_search.best_estimator_\n",
        "\n",
        "  # Evaluate the best model\n",
        "  y_pred = best_model.predict(X)\n",
        "  mse = mean_squared_error(y, y_pred)\n",
        "  rmse = np.sqrt(mse)\n",
        "  r2 = r2_score(y, y_pred)\n",
        "\n",
        "  print(f'Best Hyperparameters: {best_params}')\n",
        "  print(f'Best Model MSE: {mse:.4f}')\n",
        "  print(f'Best Model RMSE: {rmse:.4f}')\n",
        "  print(f'Best Model R²: {r2:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rBnJMMTTh4i",
        "outputId": "d5c24908-7fb0-4fd1-b827-306a50b75c51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:540: FitFailedWarning: \n",
            "540 fits failed out of a total of 1080.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "540 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 888, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1466, in wrapper\n",
            "    estimator._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 666, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/ma/core.py:2820: RuntimeWarning: invalid value encountered in cast\n",
            "  _data = np.array(data, dtype=dtype, copy=copy,\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1103: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan -0.00064832 -0.00063002 -0.00060083\n",
            " -0.0006499  -0.00069179 -0.00069568 -0.00080969 -0.00083918 -0.00081643\n",
            " -0.00075185 -0.00079872 -0.00077121 -0.00072541 -0.00076681 -0.00080339\n",
            " -0.00095971 -0.00096529 -0.00096042 -0.00120034 -0.00117293 -0.00117109\n",
            " -0.00120034 -0.00117293 -0.00117109 -0.00122669 -0.00121719 -0.00120201\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan -0.00061852 -0.0006223  -0.00061927\n",
            " -0.00063314 -0.00066433 -0.00065356 -0.00081409 -0.00084364 -0.00082618\n",
            " -0.00071046 -0.0007552  -0.0007278  -0.00076064 -0.0007877  -0.00079381\n",
            " -0.0009511  -0.00095602 -0.00094614 -0.00120714 -0.00118076 -0.00118217\n",
            " -0.00120714 -0.00118076 -0.00118217 -0.00123684 -0.00123002 -0.00120646\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan -0.00064832 -0.00063002 -0.00060083\n",
            " -0.0006499  -0.00069179 -0.00069568 -0.00080969 -0.00083918 -0.00081643\n",
            " -0.00075185 -0.00079872 -0.00077121 -0.00072541 -0.00076681 -0.00080339\n",
            " -0.00095971 -0.00096529 -0.00096042 -0.00120034 -0.00117293 -0.00117109\n",
            " -0.00120034 -0.00117293 -0.00117109 -0.00122669 -0.00121719 -0.00120201\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan         nan         nan         nan\n",
            "         nan         nan         nan -0.00064832 -0.00063002 -0.00060083\n",
            " -0.0006499  -0.00069179 -0.00069568 -0.00080969 -0.00083918 -0.00081643\n",
            " -0.00075185 -0.00079872 -0.00077121 -0.00072541 -0.00076681 -0.00080339\n",
            " -0.00095971 -0.00096529 -0.00096042 -0.00120034 -0.00117293 -0.00117109\n",
            " -0.00120034 -0.00117293 -0.00117109 -0.00122669 -0.00121719 -0.00120201]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
            "Best Model MSE: 0.0000\n",
            "Best Model RMSE: 0.0067\n",
            "Best Model R²: 0.9983\n"
          ]
        }
      ]
    }
  ]
}