student_dropout_risk.ipynb

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      "source": [
        "**Problem Statement:** To track specific variables, such as poor school performance and below-average attendance, to identify students at risk of attrition and dropout and proactively set up personalized initiatives aimed at motivating them and filling their knowledge gaps."
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              "      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",
              "855  946      Advik    Bansal  advik.bansal.946@example.com    male   \n",
              "856  947      Kriti       Rai     kriti.rai.947@example.com  female   \n",
              "857  948         Om    Sharma     om.sharma.948@example.com    male   \n",
              "858  949       Riya     Gupta    riya.gupta.949@example.com  female   \n",
              "859  950      Karan     Patel   karan.patel.950@example.com    male   \n",
              "\n",
              "     part_time_job  absence_days  extracurricular_activities  \\\n",
              "0            False           3.0                        True   \n",
              "1             True           1.0                       False   \n",
              "2            False           2.0                        True   \n",
              "3            False           0.0                        True   \n",
              "4             True           4.0                       False   \n",
              "..             ...           ...                         ...   \n",
              "855          False           2.5                        True   \n",
              "856          False           3.5                        True   \n",
              "857          False           1.5                       False   \n",
              "858          False           2.5                        True   \n",
              "859          False           4.5                       False   \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",
              "855                       10                Chef          89             88   \n",
              "856                       20              Artist          90             91   \n",
              "857                       15           Scientist          84             85   \n",
              "858                       10               Nurse          90             91   \n",
              "859                       12              Doctor          88             89   \n",
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              "0               88               90             80             85   \n",
              "1               92               87             83             88   \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 860,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 270,\n        \"min\": 1,\n        \"max\": 950,\n        \"num_unique_values\": 860,\n        \"samples\": [\n          765,\n          606,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"first_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 103,\n        \"samples\": [\n          \"Audrey\",\n          \"Vikram\",\n          \"Zara\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 59,\n        \"samples\": [\n          \"Johnson\",\n          \"Martinez\",\n          \"King\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 860,\n        \"samples\": [\n          \"simran.sharma.765@example.com\",\n          \"zoe.garcia.606@example.com\",\n          \"michael.wright.121@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\": 2.2194215061025604,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.0,\n          1.5\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Lawyer\",\n          \"Doctor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 95,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          71,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "\n",
        "# Load the dataset\n",
        "df = pd.read_csv('/content/trial_data.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Processing"
      ],
      "metadata": {
        "id": "6bTgnnf6xyEo"
      }
    },
    {
      "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": "wBnZ97Q-iUlu"
      },
      "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": "oKMowuuMivpP"
      },
      "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": "ufbiGq2K3X51"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Zqo79VTQ3mL9",
        "outputId": "26474d6b-714d-43cd-9402-5bc9a035e8a3"
      },
      "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       0   \n",
              "1   2       Liam     Smith     liam.smith.2@example.com       1   \n",
              "2   3       Emma     Brown     emma.brown.3@example.com       0   \n",
              "3   4       Noah    Garcia    noah.garcia.4@example.com       1   \n",
              "4   5     Olivia    Wilson  olivia.wilson.5@example.com       0   \n",
              "\n",
              "   part_time_job  absence_days  extracurricular_activities  \\\n",
              "0              0           3.0                           1   \n",
              "1              1           1.0                           0   \n",
              "2              0           2.0                           1   \n",
              "3              0           0.0                           1   \n",
              "4              1           4.0                           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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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Ava</td>\n",
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              "      <td>84</td>\n",
              "      <td>85</td>\n",
              "      <td>79</td>\n",
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              "      <td>80.571429</td>\n",
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              "      <td>Noah</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</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",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Olivia</td>\n",
              "      <td>Wilson</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 860,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 270,\n        \"min\": 1,\n        \"max\": 950,\n        \"num_unique_values\": 860,\n        \"samples\": [\n          765,\n          606,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"first_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 103,\n        \"samples\": [\n          \"Audrey\",\n          \"Vikram\",\n          \"Zara\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 59,\n        \"samples\": [\n          \"Johnson\",\n          \"Martinez\",\n          \"King\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 860,\n        \"samples\": [\n          \"simran.sharma.765@example.com\",\n          \"zoe.garcia.606@example.com\",\n          \"michael.wright.121@example.com\"\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\": \"absence_days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2194215061025604,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.0,\n          1.5\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Lawyer\",\n          \"Doctor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        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\"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.51399054518149,\n        \"min\": 19.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          34.142857142857146,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns={'absence_days': 'attendance_percent'})"
      ],
      "metadata": {
        "id": "OE3Wth4luLhX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['id','first_name','last_name','email'])"
      ],
      "metadata": {
        "id": "2jBBSvcQrO0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "RLuMDHVGrP6t",
        "outputId": "06191697-0f03-4bf6-d559-03f3f05bdd31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     gender  part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0         0              0                 3.0                           1   \n",
              "1         1              1                 1.0                           0   \n",
              "2         0              0                 2.0                           1   \n",
              "3         1              0                 0.0                           1   \n",
              "4         0              1                 4.0                           0   \n",
              "..      ...            ...                 ...                         ...   \n",
              "855       1              0                 2.5                           1   \n",
              "856       0              0                 3.5                           1   \n",
              "857       1              0                 1.5                           0   \n",
              "858       0              0                 2.5                           1   \n",
              "859       1              0                 4.5                           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",
              "855                       10                Chef          89             88   \n",
              "856                       20              Artist          90             91   \n",
              "857                       15           Scientist          84             85   \n",
              "858                       10               Nurse          90             91   \n",
              "859                       12              Doctor          88             89   \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",
              "855             87               86             85             84   \n",
              "856             92               93             94             95   \n",
              "857             86               87             88             89   \n",
              "858             92               93             94             95   \n",
              "859             90               91             92             93   \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  \n",
              "..               ...            ...  \n",
              "855               83      86.000000  \n",
              "856               96      93.000000  \n",
              "857               90      87.000000  \n",
              "858               96      93.000000  \n",
              "859               94      91.000000  \n",
              "\n",
              "[860 rows x 14 columns]"
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              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>95</td>\n",
              "      <td>96</td>\n",
              "      <td>93.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>857</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>84</td>\n",
              "      <td>85</td>\n",
              "      <td>86</td>\n",
              "      <td>87</td>\n",
              "      <td>88</td>\n",
              "      <td>89</td>\n",
              "      <td>90</td>\n",
              "      <td>87.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>Nurse</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>95</td>\n",
              "      <td>96</td>\n",
              "      <td>93.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>Doctor</td>\n",
              "      <td>88</td>\n",
              "      <td>89</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>91.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>860 rows × 14 columns</p>\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "\n",
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              "    0% {\n",
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              "      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",
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              "\n",
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              "\n",
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              "\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_32beafc8-018c-4e4d-95a4-ab8029b3b1d2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 860,\n  \"fields\": [\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\": 2.2194215061025604,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.0,\n          1.5\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Lawyer\",\n          \"Doctor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 95,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          71,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.51399054518149,\n        \"min\": 19.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          34.142857142857146,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labelencoder = LabelEncoder()\n",
        "df['career_aspiration'] = labelencoder.fit_transform(df['career_aspiration'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "Xs6zXbzazANF",
        "outputId": "74afefaa-8f42-45a8-9b1a-8dec2ba167f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     gender  part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0         0              0                 3.0                           1   \n",
              "1         1              1                 1.0                           0   \n",
              "2         0              0                 2.0                           1   \n",
              "3         1              0                 0.0                           1   \n",
              "4         0              1                 4.0                           0   \n",
              "..      ...            ...                 ...                         ...   \n",
              "855       1              0                 2.5                           1   \n",
              "856       0              0                 3.5                           1   \n",
              "857       1              0                 1.5                           0   \n",
              "858       0              0                 2.5                           1   \n",
              "859       1              0                 4.5                           0   \n",
              "\n",
              "     weekly_self_study_hours  career_aspiration  math_score  history_score  \\\n",
              "0                         25                 10          82             75   \n",
              "1                         30                  6          90             85   \n",
              "2                         20                  9          78             80   \n",
              "3                         35                  7          95             88   \n",
              "4                         28                 16          76             72   \n",
              "..                       ...                ...         ...            ...   \n",
              "855                       10                  3          89             88   \n",
              "856                       20                  1          90             91   \n",
              "857                       15                 14          84             85   \n",
              "858                       10                 11          90             91   \n",
              "859                       12                  6          88             89   \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",
              "855             87               86             85             84   \n",
              "856             92               93             94             95   \n",
              "857             86               87             88             89   \n",
              "858             92               93             94             95   \n",
              "859             90               91             92             93   \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  \n",
              "..               ...            ...  \n",
              "855               83      86.000000  \n",
              "856               96      93.000000  \n",
              "857               90      87.000000  \n",
              "858               96      93.000000  \n",
              "859               94      91.000000  \n",
              "\n",
              "[860 rows x 14 columns]"
            ],
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              "<p>860 rows × 14 columns</p>\n",
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              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 860,\n  \"fields\": [\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\": 2.2194215061025604,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.0,\n          1.5\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          10,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 95,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          71,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.51399054518149,\n        \"min\": 19.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          34.142857142857146,\n          59.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, r2_score\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import warnings\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "byaIZQLoyJtT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use .apply to iterate through the Series and apply the condition to each element\n",
        "df['attendance_percent'] = df['attendance_percent'].apply(lambda x: x * 20 if x < 5 else x * 10)"
      ],
      "metadata": {
        "id": "mHh8IML_smF-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "_jm0BCGMyAOy",
        "outputId": "2ead8cfc-84df-459b-f6d5-738871c47c35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0       0              0                60.0                           1   \n",
              "1       1              1                20.0                           0   \n",
              "2       0              0                40.0                           1   \n",
              "3       1              0                 0.0                           1   \n",
              "4       0              1                80.0                           0   \n",
              "\n",
              "   weekly_self_study_hours  career_aspiration  math_score  history_score  \\\n",
              "0                       25                 10          82             75   \n",
              "1                       30                  6          90             85   \n",
              "2                       20                  9          78             80   \n",
              "3                       35                  7          95             88   \n",
              "4                       28                 16          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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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 860,\n  \"fields\": [\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\": 23.348908073907193,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        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\"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      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     \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "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": "pShZg1WUx5AT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "fWZf44W8vcjW",
        "outputId": "4cb40e4e-f991-476c-b334-1d235f30b487"
      },
      "execution_count": null,
      "outputs": [
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              "     gender  part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0         0              0                60.0                           1   \n",
              "1         1              1                20.0                           0   \n",
              "2         0              0                40.0                           1   \n",
              "3         1              0                 0.0                           1   \n",
              "4         0              1                80.0                           0   \n",
              "..      ...            ...                 ...                         ...   \n",
              "855       1              0                50.0                           1   \n",
              "856       0              0                70.0                           1   \n",
              "857       1              0                30.0                           0   \n",
              "858       0              0                50.0                           1   \n",
              "859       1              0                90.0                           0   \n",
              "\n",
              "     weekly_self_study_hours  career_aspiration  math_score  history_score  \\\n",
              "0                         25                 10          82             75   \n",
              "1                         30                  6          90             85   \n",
              "2                         20                  9          78             80   \n",
              "3                         35                  7          95             88   \n",
              "4                         28                 16          76             72   \n",
              "..                       ...                ...         ...            ...   \n",
              "855                       10                  3          89             88   \n",
              "856                       20                  1          90             91   \n",
              "857                       15                 14          84             85   \n",
              "858                       10                 11          90             91   \n",
              "859                       12                  6          88             89   \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",
              "855             87               86             85             84   \n",
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              "857             86               87             88             89   \n",
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              "859             90               91             92             93   \n",
              "\n",
              "     geography_score  average_score  at_risk  \n",
              "0                 78      82.571429        0  \n",
              "1                 81      86.571429        1  \n",
              "2                 82      80.571429        1  \n",
              "3                 90      89.571429        1  \n",
              "4                 83      79.571429        0  \n",
              "..               ...            ...      ...  \n",
              "855               83      86.000000        0  \n",
              "856               96      93.000000        0  \n",
              "857               90      87.000000        1  \n",
              "858               96      93.000000        0  \n",
              "859               94      91.000000        0  \n",
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              "  [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-9e02edd2-32a2-4c73-bf9b-be58cd6958ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3cd2107f-c0d3-49e3-9c56-04c104f2aca3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3cd2107f-c0d3-49e3-9c56-04c104f2aca3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\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\": 860,\n  \"fields\": [\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\": 23.348908073907193,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"career_aspiration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          10,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 95,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          71,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.51399054518149,\n        \"min\": 19.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          34.142857142857146,\n          59.0\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          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "source": [
        "# @title attendance_percent\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "df['attendance_percent'].plot(kind='hist', bins=20, title='attendance_percent')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "1WRSX9T32KJB",
        "outputId": "1a0115d2-3703-475d-ef27-89231cee05a0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "df['weekly_self_study_hours'].plot(kind='hist', bins=20, title='weekly_self_study_hours')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "WCb8CDZM9MQ-",
        "outputId": "ad7f22c7-ce19-474e-b325-d9e3db168f27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"At risk students\")\n",
        "print(df['at_risk'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPxYsLml8Tlh",
        "outputId": "15024e99-b565-445d-d423-e6bcb7ca2cf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "At risk students\n",
            "at_risk\n",
            "1    524\n",
            "0    336\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Students with part time job\")\n",
        "print(df['part_time_job'].value_counts())\n",
        "\n",
        "print(\"Students with activites\")\n",
        "print(df['extracurricular_activities'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDBaxkeq9ASE",
        "outputId": "9109e245-cd42-454a-fedc-52a75507fa40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Students with part time job\n",
            "part_time_job\n",
            "0    469\n",
            "1    391\n",
            "Name: count, dtype: int64\n",
            "Students with activites\n",
            "extracurricular_activities\n",
            "1    552\n",
            "0    308\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Students with average score > 70%:\")\n",
        "print((df['average_score'] >= performance_threshold).value_counts())\n",
        "\n",
        "print(\"Students with attendance > 50%:\")\n",
        "print((df['attendance_percent'] >= attendance_threshold).value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOse_4lQyjtf",
        "outputId": "888a5c15-54ab-4eb2-b230-472cff61e782"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Students with average score > 70%:\n",
            "average_score\n",
            "True     666\n",
            "False    194\n",
            "Name: count, dtype: int64\n",
            "Students with attendance > 50%:\n",
            "attendance_percent\n",
            "True     521\n",
            "False    339\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "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": "c3eu_D1wyFr-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification Model (SVC)"
      ],
      "metadata": {
        "id": "eKrZCZDZx6dL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "base_models = [\n",
        "    ('svc', SVC(probability=True))  # Set probability=True to enable stacking\n",
        "]\n",
        "final_model = SVC()\n",
        "\n",
        "model = StackingClassifier(estimators=base_models, final_estimator=final_model)\n",
        "\n",
        "k = 10\n",
        "scores = cross_val_score(model, X, y, cv=k)\n",
        "evaluate_model(model, X_train, X_test, y_train, y_test)\n",
        "print(f\"Cross-Validation Scores: {scores}\")\n",
        "print(f\"Mean Cross-Validation Score: {scores.mean():.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "oheyKeXByO22",
        "outputId": "a04accb4-d152-49c0-af4f-4f3434d51aab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: StackingClassifier\n",
            "Accuracy: 0.9884\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.97      0.99        70\n",
            "           1       0.98      1.00      0.99       102\n",
            "\n",
            "    accuracy                           0.99       172\n",
            "   macro avg       0.99      0.99      0.99       172\n",
            "weighted avg       0.99      0.99      0.99       172\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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37q1bbrklT9+rcTXXcpHT6VSPHj00b948LVq06JJVA0l67rnnNHLkSN188816++23tWLFCq1atUp16tQpcIVEUqE/aW3fvt31j/I333xToNc0bdpUkZGRrq0w92uIjIxUfHy81q9fr+rVq+u9997T+fPnL/uaffv2qUOHDjp27JgmTZqkjz/+WKtWrXKtpy/M+5M7jpYtW+qTTz7Rli1bCvVad/xsXFSzZk3t3r1bZ8+eLfRrJentt99W3759Va1aNb3xxhtavny5Vq1apfbt21velwoVKmjHjh1asmSJ7rjjDq1du1adO3dWQkKCq8/NN9+sffv2afbs2apbt65ef/11NWrUSK+//vrvii23i7Hcd999WrVqVb5bbGys5TVUDVBQTEh0g9tvv12zZs1ScnKyWrRocdm+0dHRysnJ0Z49eyyTjFJTU5WWluaabOYOoaGh+ZaY7dUJSfLx8VGHDh3UoUMHTZo0Sc8995yefPJJrV27VnFxcfleh3Rh3brdDz/8oHLlyikgIODqLyIf99xzj2bPni0fHx/Fx8dfst/ChQvVrl07vfHGG5b2tLQ01+Q8qeCJWkFkZmaqX79+ql27tlq2bKmJEyfqzjvvVNOmTS/7uvnz51tu8FS1atVCn7tUqVKqV6+e9uzZo2PHjikiIuKS17Z06VJlZWVpyZIllk/ta9eutfSrVq2aJOnbb7+9ZIXmIj8/Py1btkzt27fXrbfeqnXr1qlOnTqFvo78XPx527t3r+XT7/Hjx/NUnrp27ark5GR98MEH6tOnT6HPtXDhQlWtWlUffvih5f0bPXp0nr6+vr7q2rWrunbtqpycHD300EOaOXOmRo0a5Xq/wsLC1K9fP/Xr108nT57UzTffrDFjxmjAgAGFji238uXLKzAwUNnZ2fn+jgJXg8qBGzz++OMKCAjQgAEDlJqammf/vn379NJLL0m6UBaXpClTplj6TJo0SZLUpUsXt8VVrVo1paenW8qYhw8fzrMi4sSJE3lee3FGdlZWVr7HjoyMVIMGDTRv3jxLAvLtt99q5cqVrussCu3atdOzzz6r6dOnKyIi4pL9SpQokeeT5/vvv69ffvnF0nYxiXHH/QH+/ve/6+DBg5o3b54mTZqkKlWqKCEh4ZLv40WxsbGKi4tzbZdLDvbs2aODBw/maU9LS1NycrJCQ0NdKxIudW0XP6nnfn/S09M1Z84cS7+OHTsqMDBQSUlJeVYF5PepPjg4WCtWrFCFChV0yy23uIYlrlaHDh1UsmRJvfrqq5b26dOn5+k7ePBgRUZG6pFHHtHu3bvz7D9y5IjGjRt3yXPl995s3LhRycnJln7Hjx+3fO3j46N69epJ+t/vjb1PmTJldP3111/x56EgSpQooZ49e+qDDz7It3J58f4nwO9B5cANqlWrpnfeeUe9e/dWrVq1LHdI/PLLL/X++++77idfv359JSQkaNasWUpLS1ObNm20adMmzZs3T927d1e7du3cFld8fLz+/ve/684779Tf/vY3nTp1Sq+++qpq1Khhmag0duxYrV+/Xl26dFF0dLSOHDmiV155Rdddd51atWp1yeO/8MIL6ty5s1q0aKH+/fu7ljIGBwcX6f3ffXx89NRTT12x3+23366xY8eqX79+atmypb755hvNnz8/zx/eatWqKSQkRDNmzFBgYKACAgLUrFmzQo/PrlmzRq+88opGjx7tWlo5Z84ctW3bVqNGjdLEiRMLdbxL2blzp+655x517txZrVu3VlhYmH755RfNmzdPhw4d0pQpU1x/4C7eGfDJJ59UfHy8SpUqpa5du6pjx46uT72DBg3SyZMn9dprr6lChQo6fPiw61xBQUGaPHmyBgwYoKZNm+qee+5RaGiodu7cqVOnTmnevHl54itXrpxWrVqlVq1aKS4uThs2bFDFihWv6prDw8P18MMP6x//+IfuuOMO3Xrrrdq5c6c+/fRTlStXzvIJPzQ0VIsWLdJtt92mBg0aWO6QuG3bNi1YsOCyFb7bb79dH374oe6880516dJFBw4c0IwZM1S7dm3LXJABAwboxIkTat++va677jr9/PPPmjZtmho0aOCqCtauXVtt27ZV48aNFRYWpi1btmjhwoVuu5PphAkTtHbtWjVr1kwPPvigateurRMnTmjbtm367LPP8k38gQLx2DqJP6Hdu3ebBx980FSpUsX4+vqawMBAExsba6ZNm2a5wdG5c+fMM888Y2JiYkypUqVMpUqVLnsTJDv70qtLLWU05sLNjerWrWt8fX3NDTfcYN5+++08S6dWr15tunXrZqKiooyvr6+Jiooyffr0Mbt3785zDvtyv88++8zExsYaf39/ExQUZLp27XrJmyDZl0peXMaW3zK73HIvZbyUSy1lfOSRR1w3zYmNjTXJycn5LkH86KOPTO3atU3JkiXzvQlSfnIfJyMjw0RHR5tGjRq5bvhz0YgRI4yPj49JTk6+7DUUVGpqqpkwYYJp06aNiYyMNCVLljShoaGmffv2ZuHChXn6P/vss6ZixYrGx8fH8n4vWbLE1KtXz/j5+ZkqVaqY559/3syePTvf78mSJUtMy5YtXd/nm266ySxYsMDyXtjfp71795rIyEhTq1Yt1/f+UksZN2/ebHntxaV3F2/+Y8yFJYajRo0yERERxt/f37Rv397s2rXLlC1b1gwePDjPdR86dMiMGDHC1KhRw/j5+ZnSpUubxo0bm/Hjx5v09HRL7Ll/HnJycsxzzz1noqOjjdPpNA0bNjTLli3LszR44cKFpmPHjq6bD1WuXNkMGjTIHD582NVn3Lhx5qabbjIhISHG39/f1KxZ04wfP96cPXvW1edqljIac+HnYciQIaZSpUqmVKlSJiIiwnTo0MHMmjUrz/v5/vvv5zkmkB+HMb9jxg8AFANpaWkKDQ3VuHHj9OSTT3o6HOBPgzkHAP4Q8nsi58W5Ozw6G3Av5hwA+EN49913NXfuXNctwjds2KAFCxaoY8eOeZbsAbg6JAcA/hDq1aunkiVLauLEicrIyHBNUrzcygMAvw9zDgAAgAVzDgAAgAXJAQAAsCA5AAAAFsVmQmLzCes8HQJQ7Hw24mZPhwAUS2Wc7nsmSn78G7rnLpaSdHp73tt8F3fFJjkAAKDYcHh3Yd27rx4AAORBcgAAgJ3D4b6tENavX6+uXbsqKipKDodDixcvtuw3xujpp59WZGSk/P39FRcXpz179lj6nDhxQvfee6+CgoIUEhKi/v37Wx4aVhAkBwAA2Dl83LcVQmZmpurXr6+XX3453/0TJ07U1KlTNWPGDG3cuFEBAQHq1KmT5ZHq9957r7777jutWrVKy5Yt0/r16zVw4MBCxcGcAwAA7Ar5id9dOnfurM6dO+e7zxijKVOm6KmnnlK3bt0kSW+++abCw8O1ePFixcfHa9euXVq+fLk2b96sJk2aSJKmTZum2267TS+++KKioqIKFAeVAwAAilBWVpYyMjIsW1ZWVqGPc+DAAaWkpCguLs7VFhwcrGbNmik5OVmSlJycrJCQEFdiIElxcXHy8fHRxo0bC3wukgMAAOzcOKyQlJSk4OBgy5aUlFTokFJSUiRJ4eHhlvbw8HDXvpSUFFWoUMGyv2TJkgoLC3P1KQiGFQAAsHPjsEJiYqJGjhxpaXM6nW47flEgOQAAoAg5nU63JAMRERGSpNTUVEVGRrraU1NT1aBBA1efI0eOWF53/vx5nThxwvX6gmBYAQAAOw+tVricmJgYRUREaPXq1a62jIwMbdy4US1atJAktWjRQmlpadq6daurz5o1a5STk6NmzZoV+FxUDgAAsPPQaoWTJ09q7969rq8PHDigHTt2KCwsTJUrV9bw4cM1btw4Va9eXTExMRo1apSioqLUvXt3SVKtWrV066236sEHH9SMGTN07tw5DR06VPHx8QVeqSCRHAAAUGxs2bJF7dq1c319ca5CQkKC5s6dq8cff1yZmZkaOHCg0tLS1KpVKy1fvlx+fn6u18yfP19Dhw5Vhw4d5OPjo549e2rq1KmFisNhjDHuuaSrw4OXgLx48BKQvyJ/8FLL/3PbsU5/+ZzbjnWtUDkAAMDOQ8MKxQUTEgEAgAWVAwAA7Lz8kc0kBwAA2Hn5sALJAQAAdl5eOfDuqwcAAHlQOQAAwM7LKwckBwAA2Pl495wD706NAABAHlQOAACwY1gBAABYePlSRu9OjQAAQB5UDgAAsGNYAQAAWDCsAAAA8D9UDgAAsGNYAQAAWHj5sALJAQAAdl5eOfDuqwcAAHlQOQAAwI5hBQAAYMGwAgAAwP9QOQAAwI5hBQAAYMGwAgAAwP9QOQAAwM7LKwckBwAA2Hn5nAPvTo0AAEAeVA4AALBjWAEAAFh4+bACyQEAAHZeXjnw7qsHAAB5UDkAAMCOYQUAAJCbw8uTA4YVAACABZUDAABsvL1yQHIAAICdd+cGDCsAAAArKgcAANgwrAAAACy8PTlgWAEAAFhQOQAAwMbbKwckBwAA2JAcAAAAK+/ODZhzAAAArKgcAABgw7ACAACw8PbkgGEFAABgQeUAAAAbb68ckBwAAGDj7ckBwwoAAMCCygEAAHbeXTggOQAAwI5hBQAAgFyoHAAAYOPtlQOSAwAAbEgOAACAlXfnBsw5AAAAVlQOAACwYVgBAABYeHtywLACAACwoHIAAIANlQMAAGDhcDjcthVGdna2Ro0apZiYGPn7+6tatWp69tlnZYxx9THG6Omnn1ZkZKT8/f0VFxenPXv2uPX6SQ4AACgmnn/+eb366quaPn26du3apeeff14TJ07UtGnTXH0mTpyoqVOnasaMGdq4caMCAgLUqVMnnTlzxm1xMKwAAICdG0cVsrKylJWVZWlzOp1yOp15+n755Zfq1q2bunTpIkmqUqWKFixYoE2bNkm6UDWYMmWKnnrqKXXr1k2S9Oabbyo8PFyLFy9WfHy8W2KmcgAAgI07hxWSkpIUHBxs2ZKSkvI9b8uWLbV69Wrt3r1bkrRz505t2LBBnTt3liQdOHBAKSkpiouLc70mODhYzZo1U3Jystuun8oBAABFKDExUSNHjrS05Vc1kKQnnnhCGRkZqlmzpkqUKKHs7GyNHz9e9957ryQpJSVFkhQeHm55XXh4uGufO5AcAABg487VCpcaQsjPe++9p/nz5+udd95RnTp1tGPHDg0fPlxRUVFKSEhwW0xXQnIAAICNp5YyPvbYY3riiSdccwduvPFG/fzzz0pKSlJCQoIiIiIkSampqYqMjHS9LjU1VQ0aNHBbHMw5AADAzuHGrRBOnTolHx/rn+YSJUooJydHkhQTE6OIiAitXr3atT8jI0MbN25UixYtCnmRl0blAACAYqJr164aP368KleurDp16mj79u2aNGmSHnjgAUkXKhrDhw/XuHHjVL16dcXExGjUqFGKiopS9+7d3RYHyQEAADaeGlaYNm2aRo0apYceekhHjhxRVFSUBg0apKefftrV5/HHH1dmZqYGDhyotLQ0tWrVSsuXL5efn5/b4nCY3Ldd8qDmE9Z5OgSg2PlsxM2eDgEolso4i/aPd/TflrrtWD9P7eq2Y10rVA68VPkyvhrStqpaVAuTs6SP/vPraY375Ef9kHJSkuRfykcPta2qNtXLKci/pA6nn9F7W37Roh2HPRw5cO3Mfn2m1q5epZ8O7JfT6ad6DRrqb8MfUZWYqp4ODShSJAdeKNBZUrP+0lBbf07TiPe+0a+nzqlSqL9+O3Pe1efhDtXUODpUY5bt0uH0M7qpSpge61Rdx06e1b/2Hvdg9MC1s23LZt0Vf4/q1LlR2dnZmj51soYMHqCFi5bJv3RpT4eHIuTtD14iOfBCf2leSakZWRr3yY+utsPp1nty31gxWJ98k6JtB9MlSR/tPKw7G0aqdmQgyQG8xvQZr1u+fubZJMW1bald33+nRk2aeigqXAskB4V07NgxzZ49W8nJya67MUVERKhly5bq27evypcv7/Yg4V6tq5fVVwd+1fjutdWwUrCOnszSh9sO6aOd/7u71je/pKt19bJa9nWKjp48q0aVQ1Qp1F9TfvrVg5EDnnXy5G+SpKDgYA9HAhStQiUHmzdvVqdOnVS6dGnFxcWpRo0aki7cfGHq1KmaMGGCVqxYoSZNmlz2OPk9hCLn/Fn5lPQtZPj4PaJC/NWjob8WbPqP5iUfVK2IQI2Iu17nso0++TZVkvSPVXv1xK01tHRoC53PzlGOkZKW79aOf6d7OHrAM3JycvTixOdUv2EjXV+9hqfDQVHz7sJB4ZKDYcOG6a677tKMGTPylFyMMRo8eLCGDRt2xYc/JCUl6ZlnnrG0VeyQoOvi+hUmHPxOPg5p1+HfNGP9AUnS7tSTqla+tO5sGOVKDu5qXFF1o4L06MJvlZJ+Rg0qBevRW67Xsd+ytPnnNA9GD3jGhPFjtW/vHr0x9x1Ph4JrwNuHFQp1h8SdO3dqxIgR+b5pDodDI0aM0I4dO654nMTERKWnp1u2qLb3FiYUXIVjJ8/qp+OnLG0/HT+l8KAL9/52lvTRX9vE6KU1+7Rh73HtPZqphdsOafUPR3VPs0qeCBnwqOefG6sN6z/XzNffVPh/b18L/JkVqnIQERGhTZs2qWbNmvnu37RpU54nReUnv4dQMKRw7Xz9n3RVDrPOtK4UVlop/52UWMLHoVIlfGS/A0Z2jpGPdyfT8DLGGE1MelZr13ymWW+8qYrXXefpkHCNeHvloFDJwaOPPqqBAwdq69at6tChgysRSE1N1erVq/Xaa6/pxRdfLJJA4T7/3PyLXvtLAyW0qKzVu46odlSQuteP1ITlF54ffupstrYdTNPQdlWVdT5bh9Oz1KhysDrXDdfUNfs8HD1w7UwYP1bLP12mSS+9rNIBATp27KgkqUyZQLfejQ7Fj5fnBoW/Q+K7776ryZMna+vWrcrOzpZ04aEQjRs31siRI3X33Xf/rkC4Q+K1FVstTH9tE6NKYaV1OO20Fmz+j2W1QlhAKT3UpqpuiglVkF9JpWRk6aMdh7Vg8388GLX34Q6JntW4Xv5V0tHPPqc7uvW4xtEgt6K+Q2L1x5a77Vh7XrjVbce6Vn737ZPPnTunY8eOSZLKlSunUqVKXVUgJAdAXiQHQP5IDorW774JUqlSpSzPkgYA4M/C24cVuEMiAAA23j4hsVBLGQEAwJ8flQMAAGy8vHBAcgAAgJ2Pl9/UhWEFAABgQeUAAAAbhhUAAIAFqxUAAAByoXIAAICNlxcOSA4AALDz9mEFkgMAAGy8PTlgzgEAALCgcgAAgI2XFw5IDgAAsGNYAQAAIBcqBwAA2Hh54YDkAAAAO4YVAAAAcqFyAACAjZcXDkgOAACwY1gBAAAgFyoHAADYeHnhgOQAAAA7bx9WIDkAAMDGy3MD5hwAAAArKgcAANgwrAAAACy8PDdgWAEAAFhROQAAwIZhBQAAYOHluQHDCgAAwIrKAQAANgwrAAAAC29PDhhWAAAAFlQOAACw8fLCAckBAAB23j6sQHIAAICNl+cGzDkAAABWVA4AALBhWAEAAFh4eW7AsAIAALCicgAAgI2Pl5cOSA4AALDx8tyAYQUAAGBF5QAAABtWKwAAAAsf784NSA4AALDz9soBcw4AAIAFlQMAAGy8vHBA5QAAADuHG/8rrF9++UX33XefypYtK39/f914443asmWLa78xRk8//bQiIyPl7++vuLg47dmzx52XT3IAAEBx8euvvyo2NlalSpXSp59+qu+//17/+Mc/FBoa6uozceJETZ06VTNmzNDGjRsVEBCgTp066cyZM26Lg2EFAABsPLVa4fnnn1elSpU0Z84cV1tMTIzr/40xmjJlip566il169ZNkvTmm28qPDxcixcvVnx8vFvioHIAAICNw+Fw25aVlaWMjAzLlpWVle95lyxZoiZNmuiuu+5ShQoV1LBhQ7322muu/QcOHFBKSori4uJcbcHBwWrWrJmSk5Pddv0kBwAAFKGkpCQFBwdbtqSkpHz77t+/X6+++qqqV6+uFStW6K9//av+9re/ad68eZKklJQUSVJ4eLjldeHh4a597sCwAgAANu5crZCYmKiRI0da2pxOZ759c3Jy1KRJEz333HOSpIYNG+rbb7/VjBkzlJCQ4L6groDKAQAANj4Oh9s2p9OpoKAgy3ap5CAyMlK1a9e2tNWqVUsHDx6UJEVEREiSUlNTLX1SU1Nd+9xy/W47EgAAuCqxsbH68ccfLW27d+9WdHS0pAuTEyMiIrR69WrX/oyMDG3cuFEtWrRwWxwMKwAAYOOpmyCNGDFCLVu21HPPPae7775bmzZt0qxZszRr1qz/xuXQ8OHDNW7cOFWvXl0xMTEaNWqUoqKi1L17d7fFQXIAAICNp56t0LRpUy1atEiJiYkaO3asYmJiNGXKFN17772uPo8//rgyMzM1cOBApaWlqVWrVlq+fLn8/PzcFofDGGPcdrSr0HzCOk+HABQ7n4242dMhAMVSGWfR/vG+a+42tx3r/b6N3Hasa4U5BwAAwIJhBQAAbHy8/MlLJAcAANh4d2rAsAIAALChcgAAgI2nVisUFyQHAADYeOqpjMUFwwoAAMCCygEAADYMKwAAAAsvzw0YVgAAAFZUDgAAsGFYAQAAWHj7agWSAwAAbLy9csCcAwAAYEHlAAAAG++uG5AcAACQh7c/lZFhBQAAYEHlAAAAGy8vHJAcAABgx2oFAACAXKgcAABg4+WFA5IDAADsWK0AAACQC5UDAABsvLxwQHIAAICdt69WKDbJweePtvF0CECxE9p0qKdDAIql09unF+nxvX3M3duvHwAA2BSbygEAAMUFwwoAAMDCx7tzA4YVAACAFZUDAABsvL1yQHIAAICNt885YFgBAABYUDkAAMCGYQUAAGDh5aMKDCsAAAArKgcAANh4+yObSQ4AALDx9rI6yQEAADZeXjjw+uQIAADYUDkAAMCGOQcAAMDCy3MDhhUAAIAVlQMAAGy4QyIAALDw9jkHDCsAAAALKgcAANh4eeGA5AAAADtvn3PAsAIAALCgcgAAgI1D3l06IDkAAMDG24cVSA4AALDx9uSAOQcAAMCCygEAADYOL1/LSHIAAIANwwoAAAC5UDkAAMDGy0cVSA4AALDjwUsAAAC5UDkAAMDG2yckkhwAAGDj5aMKDCsAAFAcTZgwQQ6HQ8OHD3e1nTlzRkOGDFHZsmVVpkwZ9ezZU6mpqW4/N8kBAAA2PnK4bfs9Nm/erJkzZ6pevXqW9hEjRmjp0qV6//33tW7dOh06dEg9evRwxyVbkBwAAGDjcLhvK6yTJ0/q3nvv1WuvvabQ0FBXe3p6ut544w1NmjRJ7du3V+PGjTVnzhx9+eWX+uqrr9x49SQHAADk4eNw35aVlaWMjAzLlpWVdclzDxkyRF26dFFcXJylfevWrTp37pylvWbNmqpcubKSk5Pde/1uPRoAALBISkpScHCwZUtKSsq37z//+U9t27Yt3/0pKSny9fVVSEiIpT08PFwpKSlujZnVCgAA2LjzJkiJiYkaOXKkpc3pdObp9+9//1sPP/ywVq1aJT8/P7ed//cgOQAAwMadSxmdTme+yYDd1q1bdeTIETVq1MjVlp2drfXr12v69OlasWKFzp49q7S0NEv1IDU1VREREe4LWCQHAAAUCx06dNA333xjaevXr59q1qypv//976pUqZJKlSql1atXq2fPnpKkH3/8UQcPHlSLFi3cGgvJAQAANp54tkJgYKDq1q1raQsICFDZsmVd7f3799fIkSMVFhamoKAgDRs2TC1atFDz5s3dGgvJAQAANsX1DomTJ0+Wj4+PevbsqaysLHXq1EmvvPKK28/jMMYYtx/1dzhz3tMRAMVPaNOhng4BKJZOb59epMefvfmg2471QNPKbjvWtULlAAAAG29f509yAACAjaO4jitcI96eHAEAABsqBwAA2Hh33YDkAACAPDyxlLE4ITkAAMDGu1MD5hwAAAAbKgcAANh4+agCyQEAAHYsZQQAAMiFygEAADbe/smZ5AAAABuGFQAAAHKhcgAAgI131w1IDgAAyINhBQAAgFyoHAAAYOPtn5xJDgAAsPH2YQWSAwAAbLw7NaByAgAAbKgcAABg4+WjCiQHAADY+Xj5wALDCgAAwILKAQAANgwrAAAACwfDCgAAAP9D5QAAABuGFQAAgAWrFQAAAHKhcgAAgA3DCgAAwILkAAAAWLCUEQAAIBcqBwAA2Ph4d+GA5AAAADuGFQAAAHKhcgAAgA2rFQAAgAXDCgAAALlQOQAAwIbVCgAAwIJhBeC//vnOfHW+pb2aNrxR98bfpW++/trTIQFFKrZRNS2cMkj7V47X6e3T1bVtvTx9Rv21i/avHK8TyZP08Yyhqla5vGtf5cgwvTr6Hu1aNkYnkifpuyWj9dTg21SqZIlreRmA25EcQJK0/NNP9OLEJA16aIj++f4i3XBDTf11UH8dP37c06EBRSbA36lvdv+i4Unv5rv/kb5xeqhPG/3tuX/q5vtfVObps1r68hA5fS8UXW+ICZePw0dDx/1TjXqN1+P/+FADerXS2GF3XMvLQBFwONy3/RExrABJ0lvz5qhHr7vV/c6ekqSnRj+j9es/1+IPP1D/Bwd6ODqgaKz84nut/OL7S+4fck87Pf/aCi37/BtJ0oBRb+rnz5J0R7v6en/FVq36cpdWfbnL1f+nX46rRnQFPXhXayVOXlTk8aPo/EH/prsNlQPo3Nmz2vX9d2reoqWrzcfHR82bt9TXO7d7MDLAc6pULKvI8sFas/EHV1vGyTPa/O1PalavyiVfF1TGXycyTl2DCFGUfBwOt21/RG5PDv7973/rgQceuGyfrKwsZWRkWLasrCx3h4IC+jXtV2VnZ6ts2bKW9rJly+rYsWMeigrwrIhyQZKkIyd+s7QfOf6bwssG5fuaqpXK6a/xbfTGwg1FHh9QlNyeHJw4cULz5s27bJ+kpCQFBwdbtheeT3J3KABwzUSVD9aS6UP04WfbNWfRl54OB1fJ4cbtj6jQcw6WLFly2f379++/4jESExM1cuRIS5sp4SxsKHCT0JBQlShRIs/kw+PHj6tcuXIeigrwrJRjGZKkCmGBrv+XpAplA/X1j/+x9I0sH6zlrz2sr77eryHPLrimcaKI/FH/qrtJoZOD7t27y+FwyBhzyT6OK4yxOJ1OOZ3WZODM+cJGAncp5eurWrXraONXyWrfIU6SlJOTo40bkxXf5z4PRwd4xk+/HNfho+lq1+wGfb37F0lSYICfmtatotfe/9+wQdR/E4Ptuw5q4Oi3L/tvI/BHUehhhcjISH344YfKycnJd9u2bVtRxIki9peEfvpw4XtasniR9u/bp3Fjx+j06dPqfmcPT4cGFJkAf1/Vq1FR9WpUlHRhEmK9GhVVKSJUkvTyO2v19wG3qkubG1Xn+ii98exfdPhoupas3SnpQmKw4vWH9e+UE0qctEjlQ8sovGygwssGeuya4B4ON/73R1ToykHjxo21detWdevWLd/9V6oqoHi6tfNt+vXECb0yfaqOHTuqG2rW0iszX1dZhhXwJ9aodrRWvv6w6+uJj15YyvvWkq80cPTb+sfcz1Ta36npT/VRSKC/vtyxT3cMeUVZZy+UOts3r6nrK1fQ9ZUraN/K8ZZj+zcceu0uBG73B11k4DYOU8i/5P/617+UmZmpW2+9Nd/9mZmZ2rJli9q0aVOoQBhWAPIKbcofGCA/p7dPL9Ljb9qf7rZj3VQ12G3HulYKXTlo3br1ZfcHBAQUOjEAAKA48fLCAXdIBAAgDy/PDrhDIgAAsKByAACAzR91lYG7kBwAAGDj7asVSA4AALDx8tyAOQcAAMCKygEAAHZeXjogOQAAwMbbJyQyrAAAACxIDgAAsHE43LcVRlJSkpo2barAwEBVqFBB3bt3148//mjpc+bMGQ0ZMkRly5ZVmTJl1LNnT6Wmprrx6kkOAADIw+HGrTDWrVunIUOG6KuvvtKqVat07tw5dezYUZmZma4+I0aM0NKlS/X+++9r3bp1OnTokHr0cO8TdAv94KWiwoOXgLx48BKQv6J+8NLOg7+57Vj1K//+R3gfPXpUFSpU0Lp163TzzTcrPT1d5cuX1zvvvKNevXpJkn744QfVqlVLycnJat68uVtipnIAAICdG0sHWVlZysjIsGxZWVkFCiM9/cLTIcPCwiRJW7du1blz5xQXF+fqU7NmTVWuXFnJyclXe9UuJAcAANg43PhfUlKSgoODLVtSUtIVY8jJydHw4cMVGxurunXrSpJSUlLk6+urkJAQS9/w8HClpKS47fpZyggAQBFKTEzUyJEjLW1Op/OKrxsyZIi+/fZbbdiwoahCuySSAwAAbNz5bAWn01mgZCC3oUOHatmyZVq/fr2uu+46V3tERITOnj2rtLQ0S/UgNTVVERER7gqZYQUAAOw8tVrBGKOhQ4dq0aJFWrNmjWJiYiz7GzdurFKlSmn16tWuth9//FEHDx5UixYtCn2dl0LlAAAAOw/dIHHIkCF655139NFHHykwMNA1jyA4OFj+/v4KDg5W//79NXLkSIWFhSkoKEjDhg1TixYt3LZSQSI5AACg2Hj11VclSW3btrW0z5kzR3379pUkTZ48WT4+PurZs6eysrLUqVMnvfLKK26Ng/scAMUY9zkA8lfU9zn47pfMK3cqoDoVA9x2rGuFygEAADbunJD4R8SERAAAYEHlAAAAGy8vHJAcAACQh5dnBwwrAAAACyoHAADYOLy8dEByAACADasVAAAAcqFyAACAjZcXDkgOAADIw8uzA5IDAABsvH1CInMOAACABZUDAABsvH21AskBAAA2Xp4bMKwAAACsqBwAAGDn5aUDkgMAAGxYrQAAAJALlQMAAGxYrQAAACy8PDdgWAEAAFhROQAAwM7LSwckBwAA2Hj7agWSAwAAbLx9QiJzDgAAgAWVAwAAbLy8cEByAACAHcMKAAAAuVA5AAAgD+8uHZAcAABgw7ACAABALlQOAACw8fLCAckBAAB2DCsAAADkQuUAAAAbnq0AAACsvDs3IDkAAMDOy3MD5hwAAAArKgcAANh4+2oFkgMAAGy8fUIiwwoAAMCCygEAAHbeXTggOQAAwM7LcwOGFQAAgBWVAwAAbFitAAAALFitAAAAkAuVAwAAbLx9WIHKAQAAsKByAACADZUDAACAXKgcAABg4+2rFUgOAACwYVgBAAAgFyoHAADYeHnhgOQAAIA8vDw7YFgBAABYUDkAAMCG1QoAAMCC1QoAAAC5UDkAAMDGywsHVA4AAMjD4catkF5++WVVqVJFfn5+atasmTZt2nS1V1NoJAcAANg43PhfYbz77rsaOXKkRo8erW3btql+/frq1KmTjhw5UkRXmj+SAwAAiolJkybpwQcfVL9+/VS7dm3NmDFDpUuX1uzZs69pHMw5AADAxp2rFbKyspSVlWVpczqdcjqdlrazZ89q69atSkxMdLX5+PgoLi5OycnJ7guoAIpNcuBXbCLxbllZWUpKSlJiYmKeH1xce6e3T/d0CBC/F97InX+TxoxL0jPPPGNpGz16tMaMGWNpO3bsmLKzsxUeHm5pDw8P1w8//OC+gArAYYwx1/SMKNYyMjIUHBys9PR0BQUFeTocoFjg9wJXo6CVg0OHDqlixYr68ssv1aJFC1f7448/rnXr1mnjxo3XJF6pGFUOAAD4M8ovEchPuXLlVKJECaWmplraU1NTFRERUVTh5YsJiQAAFAO+vr5q3LixVq9e7WrLycnR6tWrLZWEa4HKAQAAxcTIkSOVkJCgJk2a6KabbtKUKVOUmZmpfv36XdM4SA5g4XQ6NXr0aCZdAbnwe4FrpXfv3jp69KiefvpppaSkqEGDBlq+fHmeSYpFjQmJAADAgjkHAADAguQAAABYkBwAAAALkgMAAGBBcgAAACxIDuBSHJ4hDhQn69evV9euXRUVFSWHw6HFixd7OiTgmiA5gKTi8wxxoDjJzMxU/fr19fLLL3s6FOCa4j4HkCQ1a9ZMTZs21fTpF54CmJOTo0qVKmnYsGF64oknPBwd4HkOh0OLFi1S9+7dPR0KUOSoHMD1DPG4uDhXm6eeIQ4A8DySA1z2GeIpKSkeigoA4CkkBwAAwILkAMXqGeIAAM8jOUCxeoY4AMDzeGQzJBWfZ4gDxcnJkye1d+9e19cHDhzQjh07FBYWpsqVK3swMqBosZQRLtOnT9cLL7zgeob41KlT1axZM0+HBXjM559/rnbt2uVpT0hI0Ny5c699QMA1QnIAAAAsmHMAAAAsSA4AAIAFyQEAALAgOQAAABYkBwAAwILkAAAAWJAcAAAAC5IDAABgQXIAAAAsSA4AAIAFyQEAALD4fzTAWR2mpCoEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy\n",
            "0.9883720930232558\n",
            "Training Accuracy\n",
            "0.9781976744186046\n",
            "Cross-Validation Scores: [1.         1.         1.         1.         1.         0.94186047\n",
            " 0.95348837 0.96511628 0.91860465 0.94186047]\n",
            "Mean Cross-Validation Score: 0.9721\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## References"
      ],
      "metadata": {
        "id": "aGwO1uWYyFBh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['career_aspiration'])"
      ],
      "metadata": {
        "id": "u4TiP7jHWBxT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()\n",
        "plt.figure(figsize=(10, 8))  # Adjust the size as needed\n",
        "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "ecNlvTfxVT8r",
        "outputId": "bf9dc102-654f-4476-ee34-28e421db8f8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['gender'])"
      ],
      "metadata": {
        "id": "nq1Zg34P88s5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_attrition_score(row):\n",
        "    score_factor = (100 - row['average_score']) / 100  # Higher average score increases attrition\n",
        "    attendance_factor = (100 - row['attendance_percent']) / 100  # Higher attendance increases attrition\n",
        "    job_factor = row['part_time_job'] * 0.1\n",
        "    activity_factor = row['extracurricular_activities'] * 0.1  # If involved in extracurriculars, increase attrition\n",
        "    weekly_study_factor = row['weekly_self_study_hours'] / 100\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": "30kViKo8fzk8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "iFWRzvzwf0ez",
        "outputId": "86d0dcb3-2da2-47d0-d7cc-331f67845201"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0              0                60.0                           1   \n",
              "1              1                20.0                           0   \n",
              "2              0                40.0                           1   \n",
              "3              0                 0.0                           1   \n",
              "4              1                80.0                           0   \n",
              "\n",
              "   weekly_self_study_hours  math_score  history_score  physics_score  \\\n",
              "0                       25          82             75             88   \n",
              "1                       30          90             85             92   \n",
              "2                       20          78             80             84   \n",
              "3                       35          95             88             90   \n",
              "4                       28          76             72             88   \n",
              "\n",
              "   chemistry_score  biology_score  english_score  geography_score  \\\n",
              "0               90             80             85               78   \n",
              "1               87             83             88               81   \n",
              "2               85             79             76               82   \n",
              "3               92             85             87               90   \n",
              "4               80             77             81               83   \n",
              "\n",
              "   average_score  at_risk  attrition_score  \n",
              "0      82.571429        0         0.924286  \n",
              "1      86.571429        1         1.334286  \n",
              "2      80.571429        1         1.094286  \n",
              "3      89.571429        1         1.554286  \n",
              "4      79.571429        0         0.784286  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>part_time_job</th>\n",
              "      <th>attendance_percent</th>\n",
              "      <th>extracurricular_activities</th>\n",
              "      <th>weekly_self_study_hours</th>\n",
              "      <th>math_score</th>\n",
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              "      <th>biology_score</th>\n",
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              "      <th>geography_score</th>\n",
              "      <th>average_score</th>\n",
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              "      <th>attrition_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>60.0</td>\n",
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              "      <td>40.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.554286</td>\n",
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              "      <td>80.0</td>\n",
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              "      <td>28</td>\n",
              "      <td>76</td>\n",
              "      <td>72</td>\n",
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              "      <td>80</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>83</td>\n",
              "      <td>79.571429</td>\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2d211a43-1474-4176-b29f-11d3ce96a06b 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-2d211a43-1474-4176-b29f-11d3ce96a06b');\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",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97b2665e-c71a-47a0-a3f2-2491d5663eab')\"\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",
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              "    </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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      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-97b2665e-c71a-47a0-a3f2-2491d5663eab 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\": 860,\n  \"fields\": [\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\": 23.348908073907193,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 95,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          71,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.51399054518149,\n        \"min\": 19.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          34.142857142857146,\n          59.0\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          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attrition_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26029536863143804,\n        \"min\": 0.31,\n        \"max\": 1.7400000000000002,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          0.7357142857142858,\n          0.5428571428571429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: plot attrition_score\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df['attrition_score'], bins=20, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Attrition Score')\n",
        "plt.ylabel('Number of Students')\n",
        "plt.title('Distribution of Attrition Scores')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WdyZn2JC8ljO",
        "outputId": "808b9721-1d01-43c5-c727-e911b51b806a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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YUS1atCj2+3WqVq2qO+64QwMHDlRycrJiY2N1ww03OF1K/vHHH9dnn32mv/3tb+rVq5cOHjyof/3rX04XnXA127333qt27drppZde0pEjRxQTE6P169fr888/14gRI/Jtu7iGDBmid999V48++qh27dqlyMhIffbZZ9q6datiY2Mv+564wixcuFCNGzdWREREgfd369ZNTz/9tHbv3q1bb71VTZo00Zw5czR58mTdcMMNCg0N1V133aXGjRvLx8dHU6dO1ZkzZ+Tv76+77rqrwM+JuhxXtlMSx6MgU6dO1a5du9SjRw/dfPPNkqTdu3fr448/VtWqVTVixAhJUnBwsGbMmKHHH39czZo1U9++fXXNNddo7969yszM1EcffSQ/Pz9NnTpVAwcOVJs2bfTQQw85LtMeGRmpkSNHXjFPmzZt9MQTT2jKlCmKj49Xx44d5efnp/3792vZsmV666239MADD+ijjz7S7Nmzdf/996tOnTpKT0/X+++/r+DgYHXp0sWUYwMATtx5CUMA8GR5l2nP+6pQoYIRFhZmdOjQwXjrrbecLgee59LLtG/YsMG47777jPDwcKNChQpGeHi48dBDDxm//fab0+M+//xz46abbjJ8fX2dLovepk0bo0GDBgXmK+wy7YsXLzbGjBljhIaGGjabzejatavx+++/53v8m2++adSoUcPw9/c3WrVqZezcuTPfNi+X7dLLtBuGYaSnpxsjR440wsPDDT8/PyM6Otp4/fXXHZfnziPJGDp0aL5MhV0+/lLJycnGwIEDjZCQEKNChQpGo0aNCryUfFEu075r1y5DkjF27NhC1zly5IghyRg5cqRhGIZx/Phxo2vXrkZQUJAhyemYvf/++8b1119v+Pj4OF1q/XJZCnrehW2noBkV5XjkXab99ddfz7d/Scb48eMLff6GYRhbt241hg4dajRs2NCoXLmy4efnZ9SqVct49NFHjYMHD+Zbf9WqVcbtt99u2Gw2Izg42GjevLmxePFip3WWLFli3HLLLYa/v79RtWpVo1+/fsaff/7ptM6AAQOMihUrFprrvffeM5o0aWLYbDYjKCjIaNSokfH8888bR48eNQzDMHbv3m089NBDRq1atQx/f38jNDTUuOeee4ydO3de9vkCQHFZDMNN7yYGAAAAgDKG92ABAAAAgEkoWAAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJKFgAAAAAYBI+aFiS3W7X0aNHFRQUJIvF4u44AAAAANzEMAylp6crPDxcVqvr56MoWJKOHj2qiIgId8cAAAAA4CH++OMP1axZ0+XHUbAkBQUFSbpwEIODg92cxrtkZ2dr/fr16tixo/z8/Nwdp1xjFp6BOXgOZuEZmIPnYBaegTl4jsJmkZaWpoiICEdHcBUFS3K8LDA4OJiC5aLs7GwFBgYqODiYHxJuxiw8A3PwHMzCMzAHz8EsPANz8BxXmkVx3zrERS4AAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk7i1YG3ZskX33nuvwsPDZbFYtHLlSsd92dnZeuGFF9SoUSNVrFhR4eHheuSRR3T06FGnbZw8eVL9+vVTcHCwqlSpokGDBikjI6OUnwkAAAAAuLlgnT17VjExMZo1a1a++zIzM7V7926NHTtWu3fv1vLly5WYmKhu3bo5rdevXz/t27dPcXFxWr16tbZs2aIhQ4aU1lMAAAAAAAe3Xqa9c+fO6ty5c4H3Va5cWXFxcU7L3nnnHTVv3lxJSUmqVauWEhIStHbtWu3YsUNNmzaVJL399tvq0qWL3njjDYWHhxe47XPnzuncuXOO22lpaZIunDXLzs4246mVG3nHi+PmfszCMzAHz8EsPANz8BzMwjMwB89R2CyudjYWwzCMq9qCSSwWi1asWKHu3bsXus5XX32ljh076vTp0woODtaHH36oZ599VqdOnXKsk5OTo4CAAC1btkz3339/gduZMGGCJk6cmG/5okWLFBgYeNXPBQAAAIB3yszMVN++fXXmzJlifUau13zQ8F9//aUXXnhBDz30kOOJHj9+XKGhoU7r+fr6qmrVqjp+/Hih2xozZoxGjRrluJ33ac0dO3bkg4ZdlJ2drbi4OHXo0IEPy3MzZuEZmIPnYBaegTl4DmbhGZiD5yhsFnmvbisuryhY2dnZ6tWrlwzD0Jw5c656e/7+/vL398+33M/Pj2/0YuLYeQ5m4RmYg+dgFp6BOXgOZuEZmIPnuHQWVzsXjy9YeeXq999/18aNG53OMIWFhSklJcVp/ZycHJ08eVJhYWGlHRUAAABAOefRn4OVV67279+vr776Stdee63T/S1bttTp06e1a9cux7KNGzfKbrerRYsWpR0XAAAAQDnn1jNYGRkZOnDggOP24cOHFR8fr6pVq6p69ep64IEHtHv3bq1evVq5ubmO91VVrVpVFSpUUP369fW3v/1NgwcP1ty5c5Wdna1hw4apT58+hV5BEAAAAABKilsL1s6dO9WuXTvH7bwLTwwYMEATJkzQqlWrJEmNGzd2etymTZvUtm1bSdLChQs1bNgw3X333bJarerZs6dmzpxZKvkBAAAA4GJuLVht27bV5a4SX5QryFetWlWLFi0yMxYAAAAAFItHvwcLAAAAALwJBQsAAAAATELBAgAAAACTePznYAFAcSUlJSk1NbVU92m32yVJe/fuldX6f/+HFRISolq1apVqFgAAUPooWADKpKSkJNWrX19ZmZmlul+bzabFixerdevWysrK+r/lgYH6NSGBkgUAQBlHwQJQJqWmpiorM1O9Js9RaFR0qe3XR4aksxrywSrlyiJJSjm8X0tfflKpqakULAAAyjgKFoAyLTQqWjXqx5Ta/qz2HOnP7Qqv21B2Kz9iAQAob7jIBQAAAACYhIIFAAAAACahYAEAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEn4kBYAADxQUlKSUlNT3R3DISQkhA/KBoAioGABAOBhkpKSVK9+fWVlZro7ioMtMFC/JiRQsgDgCihYAAB4mNTUVGVlZqrX5DkKjYp2dxylHN6vpS8/qdTUVAoWAFwBBQsAAA8VGhWtGvVj3B0DAOACLnIBAAAAACahYAEAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJKFgAAAAAYBIKFgAAAACYhIIFAAAAACahYAEAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJKFgAAAAAYBIKFgAAAACYhIIFAAAAACahYAEAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJKFgAAAAAYBIKFgAAAACYhIIFAAAAACahYAEAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJ3FqwtmzZonvvvVfh4eGyWCxauXKl0/2GYWjcuHGqXr26bDab2rdvr/379zutc/LkSfXr10/BwcGqUqWKBg0apIyMjFJ8FgAAAABwgVsL1tmzZxUTE6NZs2YVeP+0adM0c+ZMzZ07V9u3b1fFihXVqVMn/fXXX451+vXrp3379ikuLk6rV6/Wli1bNGTIkNJ6CgAAAADg4OvOnXfu3FmdO3cu8D7DMBQbG6uXX35Z9913nyTp448/VrVq1bRy5Ur16dNHCQkJWrt2rXbs2KGmTZtKkt5++2116dJFb7zxhsLDw0vtuQAAAACAWwvW5Rw+fFjHjx9X+/btHcsqV66sFi1aaNu2berTp4+2bdumKlWqOMqVJLVv315Wq1Xbt2/X/fffX+C2z507p3Pnzjlup6WlSZKys7OVnZ1dQs+obMo7Xhw392MWzux2u2w2m3xkyGrPKbX95u3r4n36yJDNZpPdbmc+pcib/0646/u3MFfzPezNcyhrmIVnYA6eo7BZXO1sLIZhGFe1BZNYLBatWLFC3bt3lyR99913atWqlY4eParq1as71uvVq5csFouWLFmi1157TR999JESExOdthUaGqqJEyfqySefLHBfEyZM0MSJE/MtX7RokQIDA817UgAAAAC8SmZmpvr27aszZ84oODjY5cd77BmskjRmzBiNGjXKcTstLU0RERHq2LFjsQ5ieZadna24uDh16NBBfn5+7o5TrjELZ3v37lXr1q015INVCq/bsNT2a7XnKProLu0PbyK79cKP2KOJP+u9x7tpy5YtiomJKbUs5Z03/51w1/dvYa7me9ib51DWMAvPwBw8R2GzyHt1W3F5bMEKCwuTJCUnJzudwUpOTlbjxo0d66SkpDg9LicnRydPnnQ8viD+/v7y9/fPt9zPz49v9GLi2HkOZnGB1WpVVlaWcmVxFJ3SZLf6OvabK4uysrJktVqZjRt4498Jd3//XsqM72FvnENZxSw8A3PwHJfO4mrn4rGfgxUVFaWwsDBt2LDBsSwtLU3bt29Xy5YtJUktW7bU6dOntWvXLsc6GzdulN1uV4sWLUo9MwAAAIDyza3/LZaRkaEDBw44bh8+fFjx8fGqWrWqatWqpREjRmjy5MmKjo5WVFSUxo4dq/DwcMf7tOrXr6+//e1vGjx4sObOnavs7GwNGzZMffr04QqCAAAAAEqdWwvWzp071a5dO8ftvPdFDRgwQAsWLNDzzz+vs2fPasiQITp9+rTuuOMOrV27VgEBAY7HLFy4UMOGDdPdd98tq9Wqnj17aubMmaX+XAAAAADArQWrbdu2utxFDC0WiyZNmqRJkyYVuk7VqlW1aNGikogHAGVWUlKSUlNT3R1DkhQSEqJatWq5OwYAAKZw/ztnAQClKikpSfXq11dWZqa7o0iSbIGB+jUhgZIFACgTKFgAUM6kpqYqKzNTvSbPUWhUtFuzpBzer6UvP6nU1FQKFgCgTKBgAUA5FRoVrRr1+VwuAADM5LGXaQcAAAAAb0PBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACT+Lo7AAAACQkJpm/TbrdLkvbu3SurtWj/nxgSEqJatWqZngUAUH5QsAAAbpOemiyL1ar+/fubvm2bzabFixerdevWysrKKtpjAgP1a0ICJQsAUGwULACA22Slp8mw29Vr8hyFRkWbum0fGZLOasgHq5QryxXXTzm8X0tfflKpqakULABAsVGwAABuFxoVrRr1Y0zdptWeI/25XeF1G8pu5dcdAKB0cJELAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk3h0wcrNzdXYsWMVFRUlm82mOnXq6JVXXpFhGI51DMPQuHHjVL16ddlsNrVv31779+93Y2oAAAAA5ZVHF6ypU6dqzpw5euedd5SQkKCpU6dq2rRpevvttx3rTJs2TTNnztTcuXO1fft2VaxYUZ06ddJff/3lxuQAAAAAyiNfdwe4nO+++0733XefunbtKkmKjIzU4sWL9cMPP0i6cPYqNjZWL7/8su677z5J0scff6xq1app5cqV6tOnj9uyAwAAACh/PLpg3X777Xrvvff022+/6cYbb9TevXv17bffavr06ZKkw4cP6/jx42rfvr3jMZUrV1aLFi20bdu2QgvWuXPndO7cOcfttLQ0SVJ2drays7NL8BmVPXnHi+PmfszCmd1ul81mk48MWe05pbbfvH1dvE8fGbLZbLLb7R4xH3cdm4L4Wi0llqWgWVyOJ83Jk2YkXd2x4WeT52AWnoE5eI7CZnG1s7EYF7+hycPY7Xa9+OKLmjZtmnx8fJSbm6tXX31VY8aMkXThDFerVq109OhRVa9e3fG4Xr16yWKxaMmSJQVud8KECZo4cWK+5YsWLVJgYGDJPBkAAAAAHi8zM1N9+/bVmTNnFBwc7PLjTTmDdfr0aVWpUsWMTTlZunSpFi5cqEWLFqlBgwaKj4/XiBEjFB4ergEDBhR7u2PGjNGoUaMct9PS0hQREaGOHTsW6yCWZ9nZ2YqLi1OHDh3k5+fn7jjlGrNwtnfvXrVu3VpDPlil8LoNS22/VnuOoo/u0v7wJrJbL/yIPZr4s957vJu2bNmimJiYUstSGHcdmwKzrP9cK14ZWSJZCprF5XjSnDxpRtLVHRt+NnkOZuEZmIPnKGwWea9uKy6XC9bUqVMVGRmp3r17S7pwtujf//63wsLC9OWXX5r6S+m5557T6NGjHS/1a9SokX7//XdNmTJFAwYMUFhYmCQpOTnZ6QxWcnKyGjduXOh2/f395e/vn2+5n58f3+jFxLHzHMziAqvVqqysLOXKUqR/XJvNbvV17DdXFmVlZclqtXrEbNx9bC6WYzdKPMvFs7gcT5qTJ81IMufY8LPJczALz8AcPMels7jaubh8FcG5c+cqIiJCkhQXF6e4uDitWbNGnTt31nPPPXdVYS6VmZkpq9U5oo+Pj+x2uyQpKipKYWFh2rBhg+P+tLQ0bd++XS1btjQ1CwAAAABcicv/LXb8+HFHwVq9erV69eqljh07KjIyUi1atDA13L333qtXX31VtWrVUoMGDbRnzx5Nnz5djz32mCTJYrFoxIgRmjx5sqKjoxUVFaWxY8cqPDxc3bt3NzULAAAAAFyJywXrmmuu0R9//KGIiAitXbtWkydPlnThkum5ubmmhnv77bc1duxYPfXUU0pJSVF4eLieeOIJjRs3zrHO888/r7Nnz2rIkCE6ffq07rjjDq1du1YBAQGmZgEAAACAK3G5YPXo0UN9+/ZVdHS0Tpw4oc6dO0uS9uzZoxtuuMHUcEFBQYqNjVVsbGyh61gsFk2aNEmTJk0ydd8AAAAA4CqXC9aMGTMUGRmpP/74Q9OmTVOlSpUkSceOHdNTTz1lekAAAAAA8BYuF6xt27ZpxIgR8vV1fujTTz+t7777zrRgAAAAAOBtXL6KYLt27XTy5Ml8y8+cOaN27dqZEgoAAAAAvJHLBcswDFkslnzLT5w4oYoVK5oSCgAAAAC8UZFfItijRw9JFy4q8eijjzp9UG9ubq5+/PFH3X777eYnBAAAAAAvUeSCVblyZUkXzmAFBQXJZrM57qtQoYJuu+02DR482PyEAAAAAOAlilyw5s+fL0mKjIzUP/7xD14OCAAAAACXcPkqguPHjy+JHAAAAADg9Vy+yEVycrIefvhhhYeHy9fXVz4+Pk5fAAAAAFBeuXwG69FHH1VSUpLGjh2r6tWrF3hFQQAAAAAoj1wuWN9++62++eYbNW7cuATiAAAAAID3cvklghERETIMoySyAAAAAIBXc7lgxcbGavTo0Tpy5EgJxAEAAAAA7+XySwR79+6tzMxM1alTR4GBgfLz83O6/+TJk6aFAwAAAABv4nLBio2NLYEYAAAAAOD9XC5YAwYMKIkcAAAAAOD1XH4PliQdPHhQL7/8sh566CGlpKRIktasWaN9+/aZGg4AAAAAvInLBWvz5s1q1KiRtm/fruXLlysjI0OStHfvXo0fP970gAAAAADgLVwuWKNHj9bkyZMVFxenChUqOJbfdddd+v77700NBwAAAADexOWC9dNPP+n+++/Ptzw0NFSpqammhAIAAAAAb+RywapSpYqOHTuWb/mePXtUo0YNU0IBAAAAgDdyuWD16dNHL7zwgo4fPy6LxSK73a6tW7fqH//4hx555JGSyAgAAAAAXsHlgvXaa6+pXr16ioiIUEZGhm666Sa1bt1at99+u15++eWSyAgAAAAAXsHlz8GqUKGC3n//fY0dO1Y///yzMjIydMsttyg6Orok8gEAAACA13C5YOWpVauWatWqZWYWAAAAAPBqRSpYo0aNKvIGp0+fXuwwAAAAAODNilSw9uzZ43R79+7dysnJUd26dSVJv/32m3x8fNSkSRPzEwIAAACAlyhSwdq0aZPjz9OnT1dQUJA++ugjXXPNNZKkU6dOaeDAgbrzzjtLJiUAAAAAeAGXryL45ptvasqUKY5yJUnXXHONJk+erDfffNPUcAAAAADgTVwuWGlpafrf//6Xb/n//vc/paenmxIKAAAAALyRywXr/vvv18CBA7V8+XL9+eef+vPPP/Xvf/9bgwYNUo8ePUoiIwAAAAB4BZcv0z537lz94x//UN++fZWdnX1hI76+GjRokF5//XXTAwIAAACAt3C5YAUGBmr27Nl6/fXXdfDgQUlSnTp1VLFiRdPDAQAAAIA3KfYHDVesWFE333yzmVkAAACuKCkpSampqe6OIUkKCQlRrVq13B0DgAdxuWC1a9dOFoul0Ps3btx4VYEAAAAKk5SUpHr16ysrM9PdUSRJtsBA/ZqQQMkC4OBywWrcuLHT7ezsbMXHx+vnn3/WgAEDzMoFAACQT2pqqrIyM9Vr8hyFRkW7NUvK4f1a+vKTSk1NpWABcHC5YM2YMaPA5RMmTFBGRsZVBwIAALiS0Kho1agf4+4YAJBPsd+Ddan+/furefPmeuONN8zaJAAAgMdLSEhwdwRJvB8M8BSmFaxt27YpICDArM0BAAB4tPTUZFmsVvXv39/dUSTxfjDAU7hcsC79MGHDMHTs2DHt3LlTY8eONS0YAACAJ8tKT5Nht/N+MABOXC5YwcHBTlcRtFqtqlu3riZNmqSOHTuaGg4AAMDT8X4wABdzuWAtWLCgBGIAAAAAgPezuvqA66+/XidOnMi3/PTp07r++utNCQUAAAAA3sjlgnXkyBHl5ubmW37u3Dn997//NSUUAAAAAHijIr9EcNWqVY4/r1u3TpUrV3bczs3N1YYNGxQZGWlqOAAAAADwJkUuWN27d5ckWSwWDRgwwOk+Pz8/RUZG6s033zQ1HAAAAAB4kyIXLLvdLkmKiorSjh07FBISUmKhAAAAAMAbuXwVwcOHD5dEDgAAAADwekW+yMW2bdu0evVqp2Uff/yxoqKiFBoaqiFDhujcuXOmBwQAAAAAb1HkgjVp0iTt27fPcfunn37SoEGD1L59e40ePVpffPGFpkyZUiIhAQAAAMAbFLlgxcfH6+6773bc/vTTT9WiRQu9//77GjVqlGbOnKmlS5eWSEgAAAAA8AZFLlinTp1StWrVHLc3b96szp07O243a9ZMf/zxh7npAAAAAMCLFPkiF9WqVdPhw4cVERGh8+fPa/fu3Zo4caLj/vT0dPn5+ZVISAAASktCQoK7I3hEBgBA8RS5YHXp0kWjR4/W1KlTtXLlSgUGBurOO+903P/jjz+qTp06JRISAICSlp6aLIvVqv79+7s7CgDAixW5YL3yyivq0aOH2rRpo0qVKumjjz5ShQoVHPd/+OGH6tixY4mEBACgpGWlp8mw29Vr8hyFRkW7NUvi1g2Km82FowDAGxW5YIWEhGjLli06c+aMKlWqJB8fH6f7ly1bpkqVKpkeEACA0hQaFa0a9WPcmiHl8H637h8AUHwuf9Bw5cqVC1xetWrVqw4DAAAAAN6syFcRBAAAAABcHgULAAAAAExCwQIAAAAAkxSpYN166606deqUJGnSpEnKzMws0VAAAAAA4I2KVLASEhJ09uxZSdLEiROVkZFRoqEAAAAAwBsV6SqCjRs31sCBA3XHHXfIMAy98cYbhV6Sfdy4caYGBAAAAABvUaSCtWDBAo0fP16rV6+WxWLRmjVr5Oub/6EWi4WCBQAAAKDcKtJLBOvWratPP/1UO3bskGEY2rBhg/bs2ZPva/fu3aYH/O9//6v+/fvr2muvlc1mU6NGjbRz507H/YZhaNy4capevbpsNpvat2+v/fv5gEYAAAAApc/lqwja7XaFhoaWRJZ8Tp06pVatWsnPz09r1qzRL7/8ojfffFPXXHONY51p06Zp5syZmjt3rrZv366KFSuqU6dO+uuvv0olIwAAAADkKdJLBC918OBBxcbGKiEhQZJ00003afjw4apTp46p4aZOnaqIiAjNnz/fsSwqKsrxZ8MwFBsbq5dffln33XefJOnjjz9WtWrVtHLlSvXp08fUPAAAAABwOS4XrHXr1qlbt25q3LixWrVqJUnaunWrGjRooC+++EIdOnQwLdyqVavUqVMnPfjgg9q8ebNq1Kihp556SoMHD5YkHT58WMePH1f79u0dj6lcubJatGihbdu2FVqwzp07p3Pnzjlup6WlSZKys7OVnZ1tWv7yIO94cdzcj1k4s9vtstls8pEhqz2n1Pabt6+L9+kjQzabTXa73SPm465jUxBfq6XEshQ0C3dlcZUnZZGu7nvY7J9N5eX711VFmRG/JzwDc/Achc3iamdjMQzDcOUBt9xyizp16qR//vOfTstHjx6t9evXm/o+rICAAEnSqFGj9OCDD2rHjh0aPny45s6dqwEDBui7775Tq1atdPToUVWvXt3xuF69eslisWjJkiUFbnfChAmaOHFivuWLFi1SYGCgafkBAAAAeJfMzEz17dtXZ86cUXBwsMuPd7lgBQQE6KefflJ0dLTT8t9++00333yzqe99qlChgpo2barvvvvOseyZZ57Rjh07tG3btmIXrILOYEVERCg1NbVYB7E8y87OVlxcnDp06CA/Pz93xynXmIWzvXv3qnXr1hrywSqF121Yavu12nMUfXSX9oc3kd164UUCRxN/1nuPd9OWLVsUExNTalkK465jU2CW9Z9rxSsjSyRLQbNwVxZXeVIW6eq+h83+2VRevn9dVZQZ8XvCMzAHz1HYLNLS0hQSElLsguXySwSvu+46xcfH5ytY8fHxpl/8onr16rrpppucltWvX1///ve/JUlhYWGSpOTkZKeClZycrMaNGxe6XX9/f/n7++db7ufnxzd6MXHsPAezuMBqtSorK0u5shTpH9dms1t9HfvNlUVZWVmyWq0eMRt3H5uL5diNEs9y8SzcnaWoPCmLZM73sFk/m8rb929RuTIjfk94BubgOS6dxdXOxeWfBoMHD9aQIUN06NAh3X777ZIuvAdr6tSpGjVq1FWFuVSrVq2UmJjotOy3335T7dq1JV244EVYWJg2bNjgKFRpaWnavn27nnzySVOzAAAAAMCVuFywxo4dq6CgIL355psaM2aMJCk8PFwTJkzQM888Y2q4kSNH6vbbb9drr72mXr166YcfftB7772n9957T9KFDzYeMWKEJk+erOjoaEVFRWns2LEKDw9X9+7dTc0CAAAAAFficsGyWCwaOXKkRo4cqfT0dElSUFCQ6cEkqVmzZlqxYoXGjBmjSZMmKSoqSrGxserXr59jneeff15nz57VkCFDdPr0ad1xxx1au3at4wIZAAAAAFBaruoFwyVVrC52zz336J577in0fovFokmTJmnSpEklngUAAAAALsfq7gAAAAAAUFZQsAAAAADAJBQsAAAAADCJSwUrOztbd999t/bv319SeQAAAADAa7lUsPz8/PTjjz+WVBYAAAAA8Gouv0Swf//+mjdvXklkAQAAAACv5vJl2nNycvThhx/qq6++UpMmTVSxYkWn+6dPn25aOAAAAADwJi4XrJ9//lm33nqrJOm3335zus9isZiTCgAAAAC8kMsFa9OmTSWRAwAAAAC8XrEv037gwAGtW7dOWVlZkiTDMEwLBQAAAADeyOWCdeLECd1999268cYb1aVLFx07dkySNGjQID377LOmBwQAAAAAb+FywRo5cqT8/PyUlJSkwMBAx/LevXtr7dq1poYDAAAAAG/i8nuw1q9fr3Xr1qlmzZpOy6Ojo/X777+bFgwAAAAAvI3LZ7DOnj3rdOYqz8mTJ+Xv729KKAAAAADwRi4XrDvvvFMff/yx47bFYpHdbte0adPUrl07U8MBAAAAgDdx+SWC06ZN0913362dO3fq/Pnzev7557Vv3z6dPHlSW7duLYmMAAAAAOAVXD6D1bBhQ/3222+64447dN999+ns2bPq0aOH9uzZozp16pRERgAAAADwCi6fwZKkypUr66WXXjI7CwAAAAB4tWIVrFOnTmnevHlKSEiQJN10000aOHCgqlatamo4AAAAAPAmLr9EcMuWLYqMjNTMmTN16tQpnTp1SjNnzlRUVJS2bNlSEhkBAAAAwCu4fAZr6NCh6t27t+bMmSMfHx9JUm5urp566ikNHTpUP/30k+khAQAAAMAbuHwG68CBA3r22Wcd5UqSfHx8NGrUKB04cMDUcAAAAADgTVwuWLfeeqvjvVcXS0hIUExMjCmhAAAAAMAbFeklgj/++KPjz88884yGDx+uAwcO6LbbbpMkff/995o1a5b++c9/lkxKAAAAAPACRSpYjRs3lsVikWEYjmXPP/98vvX69u2r3r17m5cOAAAAALxIkQrW4cOHSzoHAAAAAHi9IhWs2rVrl3QOAAAAAPB6xfqg4aNHj+rbb79VSkqK7Ha7033PPPOMKcEAAAAAwNu4XLAWLFigJ554QhUqVNC1114ri8XiuM9isVCwAAAAAJRbLhessWPHaty4cRozZoysVpev8g4AAAAAZZbLDSkzM1N9+vShXAEAAADAJVxuSYMGDdKyZctKIgsAAAAAeDWXXyI4ZcoU3XPPPVq7dq0aNWokPz8/p/unT59uWjgAAAAA8CbFKljr1q1T3bp1JSnfRS4AAAVLSEhwdwRJnpMDAICyyOWC9eabb+rDDz/Uo48+WgJxAKDsSU9NlsVqVf/+/d0dBQAAlDCXC5a/v79atWpVElkAoEzKSk+TYber1+Q5Co2KdnccJW7doLjZU9wdAwCAMsnlgjV8+HC9/fbbmjlzZknkAYAyKzQqWjXqx7g7hlIO73d3BAAAyiyXC9YPP/ygjRs3avXq1WrQoEG+i1wsX77ctHAAAAAA4E1cLlhVqlRRjx49SiILAAAAAHg1lwvW/PnzSyIHAAAAAHg9lz9oGAAAAABQMJfPYEVFRV32864OHTp0VYEAAAAAwFu5XLBGjBjhdDs7O1t79uzR2rVr9dxzz5mVCwAAAAC8TrEu016QWbNmaefOnVcdCAAAAAC8lWnvwercubP+/e9/m7U5AAAAAPA6phWszz77TFWrVjVrcwAAAADgdVx+ieAtt9zidJELwzB0/Phx/e9//9Ps2bNNDQcAAAAA3sTlgtW9e3en21arVdddd53atm2revXqmZULAAAAALyOywVr/PjxJZEDAAAAALweHzQMAAAAACYp8hksq9V62Q8YliSLxaKcnJyrDgUAAAAA3qjIBWvFihWF3rdt2zbNnDlTdrvdlFAAAAAA4I2KXLDuu+++fMsSExM1evRoffHFF+rXr58mTZpkajgAAAAA8CbFeg/W0aNHNXjwYDVq1Eg5OTmKj4/XRx99pNq1a5udDwAAAAC8hksF68yZM3rhhRd0ww03aN++fdqwYYO++OILNWzYsKTyAQAAAIDXKPJLBKdNm6apU6cqLCxMixcvLvAlgwAAAABQnhW5YI0ePVo2m0033HCDPvroI3300UcFrrd8+XLTwgEAAACANylywXrkkUeueJl2AAAAACjPilywFixYUIIxAAAAAMD7FesqggAAAACA/ChYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEm8qmD985//lMVi0YgRIxzL/vrrLw0dOlTXXnutKlWqpJ49eyo5Odl9IQEAAACUW15TsHbs2KF3331XN998s9PykSNH6osvvtCyZcu0efNmHT16VD169HBTSgAAAADlmVcUrIyMDPXr10/vv/++rrnmGsfyM2fOaN68eZo+fbruuusuNWnSRPPnz9d3332n77//3o2JAQAAAJRHRf6gYXcaOnSounbtqvbt22vy5MmO5bt27VJ2drbat2/vWFavXj3VqlVL27Zt02233Vbg9s6dO6dz5845bqelpUmSsrOzlZ2dXULPomzKO14cN/djFs7sdrtsNpt8ZMhqzym1/ebt6+J9+lotbslSGE/KU5JZCpqFu7K4ypOySJKPDNlsNtntdpd/xpj9s8ldf7cL4klzKsqM+D3hGZiD5yhsFlc7G4thGMZVbaGEffrpp3r11Ve1Y8cOBQQEqG3btmrcuLFiY2O1aNEiDRw40KksSVLz5s3Vrl07TZ06tcBtTpgwQRMnTsy3fNGiRQoMDCyR5wEAAADA82VmZqpv3746c+aMgoODXX68R5/B+uOPPzR8+HDFxcUpICDAtO2OGTNGo0aNctxOS0tTRESEOnbsWKyDWJ5lZ2crLi5OHTp0kJ+fn7vjlGvMwtnevXvVunVrDflglcLrNiy1/VrtOYo+ukv7w5vIbr3wI3bv+s+14pWRpZ6lMJ6UpySzFDQLd2VxlSdlkaSjiT/rvce7acuWLYqJiXHpsWb/bHLX3+0Cs3jQnIoyI35PeAbm4DkKm0Xeq9uKy6ML1q5du5SSkqJbb73VsSw3N1dbtmzRO++8o3Xr1un8+fM6ffq0qlSp4lgnOTlZYWFhhW7X399f/v7++Zb7+fnxjV5MHDvPwSwusFqtysrKUq4sRfrHtdnsVl/HfnPshluzXMqT8pRGlotn4e4sReVJWSQpVxZlZWXJarUW++eLWT+b3P13+2KeNCdXZsTvCc/AHDzHpbO42rm4/6f2Zdx999366aefnJYNHDhQ9erV0wsvvKCIiAj5+flpw4YN6tmzpyQpMTFRSUlJatmypTsiAwBQZiUkJLj8GLvdLunCmSer9eqvrVWcDABQmjy6YAUFBalhQ+dT7hUrVtS1117rWD5o0CCNGjVKVatWVXBwsJ5++mm1bNmy0AtcAAAA16SnJstitap///4uP9Zms2nx4sVq3bq1srKySiAdAHgWjy5YRTFjxgxZrVb17NlT586dU6dOnTR79mx3xwIAoMzISk+TYber1+Q5Co2KdumxPjIkndWQD1YpV5arzpK4dYPiZk+56u0AQEnxuoL19ddfO90OCAjQrFmzNGvWLPcEAgCgnAiNilaN+q5d5MJqz5H+3K7wug1NeZ9SyuH9V70NAChJXvFBwwAAAADgDShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJKFgAAAAAYBIKFgAAAACYhIIFAAAAACahYAEAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJjE190BAG+VlJSk1NRUd8eQJIWEhKh69erujgEAAFDuUbCAYkhKSlK9+vWVlZnp7iiSJFtgoPb9/LO7YwAAAJR7FCygGFJTU5WVmalek+coNCrarVlSDu/X0pef1IkTJ9yaAwAAABQs4KqERkWrRv0Yd8cAAACAh+AiFwAAAABgEgoWAAAAAJiEggUAAAAAJqFgAQAAAIBJKFgAAAAAYBIKFgAAAACYhIIFAAAAACbhc7AAAADKiISEhELvs9vtkqS9e/fKai35/2MPCQlRrVq1Snw/gKehYAEAAHi59NRkWaxW9e/fv9B1bDabFi9erNatWysrK6vEM9kCA/VrQgIlC+UOBQsAAMDLZaWnybDb1WvyHIVGRRe4jo8MSWc15INVypWlRPOkHN6vpS8/qdTUVAoWyh0KFgAAQBkRGhWtGvVjCrzPas+R/tyu8LoNZbfyT0CgpHCRCwAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATOLr7gAAypakpCSlpqa6O4YSEhLcHQEAAJRDFCwApklKSlK9+vWVlZnp7igAAABuQcECYJrU1FRlZWaq1+Q5Co2KdmuWxK0bFDd7ilszAACA8oeCBcB0oVHRqlE/xq0ZUg7vd+v+AQBA+cRFLgAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQeXbCmTJmiZs2aKSgoSKGhoerevbsSExOd1vnrr780dOhQXXvttapUqZJ69uyp5ORkNyUGAAAAUJ55dMHavHmzhg4dqu+//15xcXHKzs5Wx44ddfbsWcc6I0eO1BdffKFly5Zp8+bNOnr0qHr06OHG1AAAAADKK4/+HKy1a9c63V6wYIFCQ0O1a9cutW7dWmfOnNG8efO0aNEi3XXXXZKk+fPnq379+vr+++912223uSM2AAAAgHLKowvWpc6cOSNJqlq1qiRp165dys7OVvv27R3r1KtXT7Vq1dK2bdsKLVjnzp3TuXPnHLfT0tIkSdnZ2crOzi6p+GVS3vEqb8fNbrfLZrPJR4as9hy3ZvGRIZvNJrvdLsm9s/Ck4+JrtbglS96+Lt6nu7IUxpPylGSWgmbhriyu8qQsV5vH1TmUZBazeVsWs2dxORf/bipv/0a4kvL6bydPVNgsrnY2FsMwjKvaQimx2+3q1q2bTp8+rW+//VaStGjRIg0cONCpLElS8+bN1a5dO02dOrXAbU2YMEETJ07Mt3zRokUKDAw0PzwAAAAAr5CZmam+ffvqzJkzCg4OdvnxXnMGa+jQofr5558d5epqjBkzRqNGjXLcTktLU0REhDp27Fisg1ieZWdnKy4uTh06dJCfn5+745SavXv3qnXr1hrywSqF123o1ixHE3/We49309dff61jx465dRaedFz2rv9cK14ZWepZrPYcRR/dpf3hTWS3+ro1S2E8KU9JZiloFu7K4ipPynK1eVydQ0lmMZu3ZTF7FpeT97tpy5YtiomJKdF9eZvy+m8nT1TYLPJe3VZcXlGwhg0bptWrV2vLli2qWbOmY3lYWJjOnz+v06dPq0qVKo7lycnJCgsLK3R7/v7+8vf3z7fcz8+Pb/RiKm/Hzmq1KisrS7mylPgvqSvJlUVZWVmyWi9cs8ads/Ck45JjN9yaxW71dezX3Vku5Ul5SiPLxbNwd5ai8qQsZuUp6hxKI4tZvDWLWbO4nIt/N5Wnfx+4orz928mTXTqLq52LR19F0DAMDRs2TCtWrNDGjRsVFRXldH+TJk3k5+enDRs2OJYlJiYqKSlJLVu2LO24AAAAAMo59/+32GUMHTpUixYt0ueff66goCAdP35cklS5cmXZbDZVrlxZgwYN0qhRo1S1alUFBwfr6aefVsuWLbmCIAAAAIBS59EFa86cOZKktm3bOi2fP3++Hn30UUnSjBkzZLVa1bNnT507d06dOnXS7NmzSzkpAAAAAHh4wSrKBQ4DAgI0a9YszZo1qxQSAZ4rMTFRlSpV0t69ex3vxyptCQkJbtkvAACAp/DoggXgytJTk2WxWjV48GAtXrxYrVu3VlZWlrtjAQAAlEsULMDLZaWnybDbdf/YGZKkIR+sUq4sbsmSuHWD4mZPccu+AQAAPAEFCygjrqtdR9JZhddt6LbLBacc3u+W/QIAAHgKChYAAABKhKe8NzckJES1atVydwyUExQsAAAAmCrv/cH9+/d3dxRJki0wUL8mJFCyUCooWAAAADBV3vuDe02eo9CoaLdmSTm8X0tfflKpqakULJQKChYAAABKRGhUtGrUj3F3DKBUuefDcgAAAACgDKJgAQAAAIBJKFgAAAAAYBIKFgAAAACYhIIFAAAAACahYAEAAACASShYAAAAAGASPgcLAAAAKCV//vmnJGnv3r2yWt17riMkJIQPXy4BFCwAAACgFCQlJalps2b6cN48tW7dWllZWW7NYwsM1K8JCZQsk1GwAAAAgFKQmpqqrMxMSdKQD1YpVxa3ZUk5vF9LX35SqampFCyTUbAAAACAUhZet6HsVv4pXhZxkQsAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJP4ujsAAAAAAPdISEhwdwRJUkhIiGrVquXuGKagYAEAAADlTHpqsixWq/r37+/uKJIkW2Cgfk1IKBMli4IFAAAAlDNZ6Wky7Hb1mjxHoVHRbs2Scni/lr78pFJTUylYAAAAALxXaFS0atSPcXeMMoWLXAAAAACASShYAAAAAGASChYAAAAAmISCBQAAAAAmoWABAAAAgEkoWAAAAABgEgoWAAAAAJiEz8ECAABAmZeQkODuCB6RASWPggUAAIAyKz01WRarVf3793d3FEmSzWZzdwSUMAoWAAAAyqys9DQZdrt6TZ6j0Khot2ZJ3LpB386PdWsGlDwKFgAAAMq80Kho1agf49YMKYf3u3X/KB1c5AIAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCRe5gNdISkpSamqqu2NI4nMsAAAAUDAKFrxCUlKS6tWvr6zMTHdHAQAAAApFwYJXSE1NVVZmpkd8hoV04XMs4mZPcXcMAAAAeBgKFryKJ3yGhcTnWAAAAKBgXOQCAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJHwOlgdKSkpSamqqu2NIkkJCQlSrVi13xwAAAAC8QpkpWLNmzdLrr7+u48ePKyYmRm+//baaN2/u7lguS0pKUr369ZWVmenuKJIkW2Cgfk1IoGQBAAAARVAmCtaSJUs0atQozZ07Vy1atFBsbKw6deqkxMREhYaGujueS1JTU5WVmalek+coNCrarVlSDu/X0pefVGpqKgULAAAAKIIyUbCmT5+uwYMHa+DAgZKkuXPn6j//+Y8+/PBDjR492s3piic0Klo16se4OwYAAAAAF3h9wTp//rx27dqlMWPGOJZZrVa1b99e27ZtK/Ax586d07lz5xy3z5w5I0k6efKksrOzSzbwFaSlpSkgIEDJiT8pJzPDrVlO/HFYAQEB2rVrl9LS0gpcx263KzMzU998842s1pK7Zsr+/fs95rhI0qk/DnlMHkeW/fuUeWOokvZ8r1xZ3JvFk45LKWfxkaGIillOc/Ck4+JpeUoyS0GzcFcWV3lSlqvN4+ocSjKL2bwti9mzuNo8pcUTs2RmZrr19/XFWTzhuOT9mzMtLU0nTpwotf1mZ2crMzNTJ06ckJ+fn2N5enq6JMkwjGJt12IU95Ee4ujRo6pRo4a+++47tWzZ0rH8+eef1+bNm7V9+/Z8j5kwYYImTpxYmjEBAAAAeJE//vhDNWvWdPlxXn8GqzjGjBmjUaNGOW7b7XadPHlS1157rSwW9/1PgjdKS0tTRESE/vjjDwUHB7s7TrnGLDwDc/AczMIzMAfPwSw8A3PwHIXNwjAMpaenKzw8vFjb9fqCFRISIh8fHyUnJzstT05OVlhYWIGP8ff3l7+/v9OyKlWqlFTEciE4OJgfEh6CWXgG5uA5mIVnYA6eg1l4BubgOQqaReXKlYu9Pa//oOEKFSqoSZMm2rBhg2OZ3W7Xhg0bnF4yCAAAAAAlzevPYEnSqFGjNGDAADVt2lTNmzdXbGyszp4967iqIAAAAACUhjJRsHr37q3//e9/GjdunI4fP67GjRtr7dq1qlatmrujlXn+/v4aP358vpdcovQxC8/AHDwHs/AMzMFzMAvPwBw8R0nNwuuvIggAAAAAnsLr34MFAAAAAJ6CggUAAAAAJqFgAQAAAIBJKFgAAAAAYBIKFq5o1qxZioyMVEBAgFq0aKEffvihSI/79NNPZbFY1L1795INWI64OovTp09r6NChql69uvz9/XXjjTfqyy+/LKW0ZZerc4iNjVXdunVls9kUERGhkSNH6q+//iqltGXTli1bdO+99yo8PFwWi0UrV6684mO+/vpr3XrrrfL399cNN9ygBQsWlHjO8sDVWSxfvlwdOnTQddddp+DgYLVs2VLr1q0rnbBlWHH+TuTZunWrfH191bhx4xLLV54UZxbnzp3TSy+9pNq1a8vf31+RkZH68MMPSz5sGVacOSxcuFAxMTEKDAxU9erV9dhjj+nEiRMu75uChctasmSJRo0apfHjx2v37t2KiYlRp06dlJKSctnHHTlyRP/4xz905513llLSss/VWZw/f14dOnTQkSNH9NlnnykxMVHvv/++atSoUcrJyxZX57Bo0SKNHj1a48ePV0JCgubNm6clS5boxRdfLOXkZcvZs2cVExOjWbNmFWn9w4cPq2vXrmrXrp3i4+M1YsQIPf744/zD3gSuzmLLli3q0KGDvvzyS+3atUvt2rXTvffeqz179pRw0rLN1TnkOX36tB555BHdfffdJZSs/CnOLHr16qUNGzZo3rx5SkxM1OLFi1W3bt0STFn2uTqHrVu36pFHHtGgQYO0b98+LVu2TD/88IMGDx7s+s4N4DKaN29uDB061HE7NzfXCA8PN6ZMmVLoY3Jycozbb7/d+OCDD4wBAwYY9913XykkLftcncWcOXOM66+/3jh//nxpRSwXXJ3D0KFDjbvuustp2ahRo4xWrVqVaM7yRJKxYsWKy67z/PPPGw0aNHBa1rt3b6NTp04lmKz8KcosCnLTTTcZEydOND9QOeXKHHr37m28/PLLxvjx442YmJgSzVUeFWUWa9asMSpXrmycOHGidEKVQ0WZw+uvv25cf/31Tstmzpxp1KhRw+X9cQYLhTp//rx27dql9u3bO5ZZrVa1b99e27ZtK/RxkyZNUmhoqAYNGlQaMcuF4sxi1apVatmypYYOHapq1aqpYcOGeu2115Sbm1tascuc4szh9ttv165duxwvIzx06JC+/PJLdenSpVQy44Jt27Y5zU2SOnXqdNmfZSgddrtd6enpqlq1qrujlDvz58/XoUOHNH78eHdHKddWrVqlpk2batq0aapRo4ZuvPFG/eMf/1BWVpa7o5UrLVu21B9//KEvv/xShmEoOTlZn332WbF+X/uWQD6UEampqcrNzVW1atWcllerVk2//vprgY/59ttvNW/ePMXHx5dCwvKjOLM4dOiQNm7cqH79+unLL7/UgQMH9NRTTyk7O5tfpsVUnDn07dtXqampuuOOO2QYhnJycvT3v/+dlwiWsuPHjxc4t7S0NGVlZclms7kpGd544w1lZGSoV69e7o5Sruzfv1+jR4/WN998I19f/jnoTocOHdK3336rgIAArVixQqmpqXrqqad04sQJzZ8/393xyo1WrVpp4cKF6t27t/766y/l5OTo3nvvdflltxLvwYKJ0tPT9fDDD+v9999XSEiIu+OUe3a7XaGhoXrvvffUpEkT9e7dWy+99JLmzp3r7mjlytdff63XXntNs2fP1u7du7V8+XL95z//0SuvvOLuaIDbLVq0SBMnTtTSpUsVGhrq7jjlRm5urvr27auJEyfqxhtvdHeccs9ut8tisWjhwoVq3ry5unTpounTp+ujjz7iLFYp+uWXXzR8+HCNGzdOu3bt0tq1a3XkyBH9/e9/d3lb/JcFChUSEiIfHx8lJyc7LU9OTlZYWFi+9Q8ePKgjR47o3nvvdSyz2+2SJF9fXyUmJqpOnTolG7qMcnUWklS9enX5+fnJx8fHsax+/fo6fvy4zp8/rwoVKpRo5rKoOHMYO3asHn74YT3++OOSpEaNGuns2bMaMmSIXnrpJVmt/D9XaQgLCytwbsHBwZy9cpNPP/1Ujz/+uJYtW5bv5ZsoWenp6dq5c6f27NmjYcOGSbrw+9owDPn6+mr9+vW666673Jyy/Khevbpq1KihypUrO5bVr19fhmHozz//VHR0tBvTlR9TpkxRq1at9Nxzz0mSbr75ZlWsWFF33nmnJk+erOrVqxd5W/xmR6EqVKigJk2aaMOGDY5ldrtdGzZsUMuWLfOtX69ePf3000+Kj493fHXr1s1x1a6IiIjSjF+muDoL6cKp7gMHDjhKriT99ttvql69OuWqmIozh8zMzHwlKq/0XnjfLUpDy5YtneYmSXFxcYXODSVr8eLFGjhwoBYvXqyuXbu6O065ExwcnO/39d///nfVrVtX8fHxatGihbsjliutWrXS0aNHlZGR4Vj222+/yWq1qmbNmm5MVr6Y+vva5ctioFz59NNPDX9/f2PBggXGL7/8YgwZMsSoUqWKcfz4ccMwDOPhhx82Ro8eXejjuYqgeVydRVJSkhEUFGQMGzbMSExMNFavXm2EhoYakydPdtdTKBNcncP48eONoKAgY/HixcahQ4eM9evXG3Xq1DF69erlrqdQJqSnpxt79uwx9uzZY0gypk+fbuzZs8f4/fffDcMwjNGjRxsPP/ywY/1Dhw4ZgYGBxnPPPWckJCQYs2bNMnx8fIy1a9e66ymUGa7OYuHChYavr68xa9Ys49ixY46v06dPu+splAmuzuFSXEXQPK7OIj093ahZs6bxwAMPGPv27TM2b95sREdHG48//ri7nkKZ4Ooc5s+fb/j6+hqzZ882Dh48aHz77bdG06ZNjebNm7u8bwoWrujtt982atWqZVSoUMFo3ry58f333zvua9OmjTFgwIBCH0vBMpers/juu++MFi1aGP7+/sb1119vvPrqq0ZOTk4ppy57XJlDdna2MWHCBKNOnTpGQECAERERYTz11FPGqVOnSj94GbJp0yZDUr6vvGM/YMAAo02bNvke07hxY6NChQrG9ddfb8yfP7/Uc5dFrs6iTZs2l10fxVOcvxMXo2CZpzizSEhIMNq3b2/YbDajZs2axqhRo4zMzMzSD1+GFGcOM2fONG666SbDZrMZ1atXN/r162f8+eefLu/bYhi8RgUAAAAAzMB7sAAAAADAJBQsAAAAADAJBQsAAAAATELBAgAAAACTULAAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAABepW3bthoxYsRl11mwYIGqVKlSKnkAALgYBQsAYKpt27bJx8dHXbt2zXffhAkT1Lhx43zLLRaLVq5cWaTtL1++XK+88orjdmRkpGJjY53W6d27t3777TdXYhfL4cOH1bdvX4WHhysgIEA1a9bUfffdp19//bXE9w0A8EwULACAqebNm6enn35aW7Zs0dGjR03b7vnz5yVJVatWVVBQ0GXXtdlsCg0NNW3fBcnOzlaHDh105swZLV++XImJiVqyZIkaNWqk06dPl+h+AQCei4IFADBNRkaGlixZoieffFJdu3bVggULHPctWLBAEydO1N69e2WxWGSxWLRgwQJFRkZKku6//35ZLBbH7byzXR988IGioqIUEBAgyfklgm3bttXvv/+ukSNHOraZt69LXyI4Z84c1alTRxUqVFDdunX1ySefON1vsVj0wQcf6P7771dgYKCio6O1atWqQp/rvn37dPDgQc2ePVu33XabateurVatWmny5Mm67bbbHOv9+eefeuihh1S1alVVrFhRTZs21fbt213KNWfOHHXr1k0VK1bUq6++Kkn6/PPPdeuttyogIEDXX3+9Jk6cqJycnMsPCABQ4ihYAADTLF26VPXq1VPdunXVv39/ffjhhzIMQ9KFl+09++yzatCggY4dO6Zjx46pd+/e2rFjhyRp/vz5OnbsmOO2JB04cED//ve/tXz5csXHx+fb3/Lly1WzZk1NmjTJsc2CrFixQsOHD9ezzz6rn3/+WU888YQGDhyoTZs2Oa03ceJE9erVSz/++KO6dOmifv366eTJkwVu87rrrpPVatVnn32m3NzcAtfJyMhQmzZt9N///lerVq3S3r179fzzz8tut7uUa8KECbr//vv1008/6bHHHtM333yjRx55RMOHD9cvv/yid999VwsWLHCULwCAGxkAAJjk9ttvN2JjYw3DMIzs7GwjJCTE2LRpk+P+8ePHGzExMfkeJ8lYsWKF07Lx48cbfn5+RkpKitPyNm3aGMOHD3fcrl27tjFjxgyndebPn29UrlzZKdfgwYOd1nnwwQeNLl26OGV4+eWXHbczMjIMScaaNWsKfb7vvPOOERgYaAQFBRnt2rUzJk2aZBw8eNBx/7vvvmsEBQUZJ06cKPDxRc01YsQIp3Xuvvtu47XXXnNa9sknnxjVq1cvNCsAoHRwBgsAYIrExET98MMPeuihhyRJvr6+6t27t+bNm1fsbdauXVvXXXfdVWdLSEhQq1atnJa1atVKCQkJTstuvvlmx58rVqyo4OBgpaSkFLrdoUOH6vjx41q4cKFatmypZcuWqUGDBoqLi5MkxcfH65ZbblHVqlWvKlfTpk2dbu/du1eTJk1SpUqVHF+DBw/WsWPHlJmZWWheAEDJ83V3AABA2TBv3jzl5OQoPDzcscwwDPn7++udd95R5cqVXd5mxYoVzYx4RX5+fk63LRaL4+V8hQkKCtK9996re++9V5MnT1anTp00efJkdejQQTabzZRclx6HjIwMTZw4UT169Mi3bt571QAA7sEZLADAVcvJydHHH3+sN998U/Hx8Y6vvXv3Kjw8XIsXL5YkVahQocD3K/n5+RX6PqYrKWybF6tfv762bt3qtGzr1q266aabirXPwlgsFtWrV09nz56VdOGMWHx8fKHv4ypurltvvVWJiYm64YYb8n1ZrfxqBwB34gwWAOCqrV69WqdOndKgQYPynanq2bOn5s2bp7///e+KjIzU4cOHFR8fr5o1ayooKEj+/v6KjIzUhg0b1KpVK/n7++uaa64p8r4jIyO1ZcsW9enTR/7+/goJCcm3znPPPadevXrplltuUfv27fXFF19o+fLl+uqrr4r9nOPj4zV+/Hg9/PDDuummm1ShQgVt3rxZH374oV544QVJ0kMPPaTXXntN3bt315QpU1S9enXt2bNH4eHhatmyZbFzjRs3Tvfcc49q1aqlBx54QFarVXv37tXPP/+syZMnF/s5AQCuHv/NBQC4avPmzVP79u0LfBlgz549tXPnTv3444/q2bOn/va3v6ldu3a67rrrHGe23nzzTcXFxSkiIkK33HKLS/ueNGmSjhw5ojp16hT6fq3u3bvrrbfe0htvvKEGDRro3Xff1fz589W2bVuXn2uemjVrKjIyUhMnTlSLFi1066236q233tLEiRP10ksvSbpwdm39+vUKDQ1Vly5d1KhRI/3zn/+Uj4/PVeXq1KmTVq9erfXr16tZs2a67bbbNGPGDNWuXbvYzwcAYA6LYfz/6+cCAAAAAK4KZ7AAAAAAwCQULAAAAAAwCQULAAAAAExCwQIAAAAAk1CwAAAAAMAkFCwAAAAAMAkFCwAAAABMQsECAAAAAJNQsAAAAADAJBQsAAAAADAJBQsAAAAATPL/AO0TV7dQHBegAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print rows hvaing -ve attrition score\n",
        "df[df['attrition_score'] < 0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "3Tt1juFG9f3n",
        "outputId": "84914e50-8b64-48ef-f135-5dd771eda47b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [part_time_job, attendance_percent, extracurricular_activities, weekly_self_study_hours, math_score, history_score, physics_score, chemistry_score, biology_score, english_score, geography_score, average_score, at_risk, attrition_score]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cda6e1b0-7d11-4f0c-b97d-ba96dbb728b2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>part_time_job</th>\n",
              "      <th>attendance_percent</th>\n",
              "      <th>extracurricular_activities</th>\n",
              "      <th>weekly_self_study_hours</th>\n",
              "      <th>math_score</th>\n",
              "      <th>history_score</th>\n",
              "      <th>physics_score</th>\n",
              "      <th>chemistry_score</th>\n",
              "      <th>biology_score</th>\n",
              "      <th>english_score</th>\n",
              "      <th>geography_score</th>\n",
              "      <th>average_score</th>\n",
              "      <th>at_risk</th>\n",
              "      <th>attrition_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df['attrition_score'] < 0]\",\n  \"rows\": 0,\n  \"fields\": [\n    {\n      \"column\": \"part_time_job\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attendance_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"extracurricular_activities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekly_self_study_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"at_risk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attrition_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "above = len(df[df['attrition_score'] > 1.0])\n",
        "below = len(df[df['attrition_score'] <= 1.0])\n",
        "\n",
        "print(f\"Number of rows with attrition score above threshold: {above}\")\n",
        "print(f\"Number of rows with attrition score less than or equal to threshold: {below}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfefpgwfz5HW",
        "outputId": "8e9e3229-d9c4-4de0-ccce-c786b9c4c871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows with attrition score above threshold: 458\n",
            "Number of rows with attrition score less than or equal to threshold: 402\n"
          ]
        }
      ]
    },
    {
      "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": "DTDdrrDLUHp_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Regression Model (RFR)"
      ],
      "metadata": {
        "id": "ld-yRh19yI7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "from sklearn.model_selection import KFold\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 with default parameters\n",
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "\n",
        "# Set up K-Fold Cross-Validation\n",
        "kf = KFold(n_splits=4, shuffle=True, random_state=42)\n",
        "\n",
        "# Train the model using cross-validation\n",
        "for train_index, test_index in kf.split(X):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "    # Fit the model on the training data\n",
        "    rf_model.fit(X_train, y_train)\n",
        "\n",
        "    # Predict on the test data\n",
        "    y_pred = rf_model.predict(X_test)\n",
        "\n",
        "    # Evaluate the model\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "    print(f'MSE: {mse:.4f}, RMSE: {rmse:.4f}, R²: {r2:.4f}')\n",
        "\n",
        "# Fit the final model on the entire dataset\n",
        "rf_model.fit(X, y)\n",
        "\n",
        "evaluate_regression_model(rf_model, X_train, X_test, y_train, y_test)"
      ],
      "metadata": {
        "id": "t6KPqA1KeVYa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2bd8377-5a3e-4311-d5cb-4c9d523a5a09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 0.0012, RMSE: 0.0343, R²: 0.9808\n",
            "MSE: 0.0011, RMSE: 0.0328, R²: 0.9848\n",
            "MSE: 0.0017, RMSE: 0.0415, R²: 0.9743\n",
            "MSE: 0.0016, RMSE: 0.0405, R²: 0.9767\n",
            "Model: RandomForestRegressor\n",
            "MSE: 0.0016\n",
            "RMSE: 0.0405\n",
            "R²: 0.9767\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model': 'RandomForestRegressor',\n",
              " 'MSE': 0.0016418041936402407,\n",
              " 'RMSE': 0.04051918303273452,\n",
              " 'R²': 0.9767312936040985}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## References"
      ],
      "metadata": {
        "id": "CDhQzfAUyP9S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "9B_Z9VBlgIyH",
        "outputId": "02671b8d-ccf1-4d1e-873d-345e41bc2ac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     part_time_job  attendance_percent  extracurricular_activities  \\\n",
              "0                0                60.0                           1   \n",
              "1                1                20.0                           0   \n",
              "2                0                40.0                           1   \n",
              "3                0                 0.0                           1   \n",
              "4                1                80.0                           0   \n",
              "..             ...                 ...                         ...   \n",
              "855              0                50.0                           1   \n",
              "856              0                70.0                           1   \n",
              "857              0                30.0                           0   \n",
              "858              0                50.0                           1   \n",
              "859              0                90.0                           0   \n",
              "\n",
              "     weekly_self_study_hours  math_score  history_score  physics_score  \\\n",
              "0                         25          82             75             88   \n",
              "1                         30          90             85             92   \n",
              "2                         20          78             80             84   \n",
              "3                         35          95             88             90   \n",
              "4                         28          76             72             88   \n",
              "..                       ...         ...            ...            ...   \n",
              "855                       10          89             88             87   \n",
              "856                       20          90             91             92   \n",
              "857                       15          84             85             86   \n",
              "858                       10          90             91             92   \n",
              "859                       12          88             89             90   \n",
              "\n",
              "     chemistry_score  biology_score  english_score  geography_score  \\\n",
              "0                 90             80             85               78   \n",
              "1                 87             83             88               81   \n",
              "2                 85             79             76               82   \n",
              "3                 92             85             87               90   \n",
              "4                 80             77             81               83   \n",
              "..               ...            ...            ...              ...   \n",
              "855               86             85             84               83   \n",
              "856               93             94             95               96   \n",
              "857               87             88             89               90   \n",
              "858               93             94             95               96   \n",
              "859               91             92             93               94   \n",
              "\n",
              "     average_score  at_risk  attrition_score  \n",
              "0        82.571429        0         0.924286  \n",
              "1        86.571429        1         1.334286  \n",
              "2        80.571429        1         1.094286  \n",
              "3        89.571429        1         1.554286  \n",
              "4        79.571429        0         0.784286  \n",
              "..             ...      ...              ...  \n",
              "855      86.000000        0         0.840000  \n",
              "856      93.000000        0         0.670000  \n",
              "857      87.000000        1         0.980000  \n",
              "858      93.000000        0         0.770000  \n",
              "859      91.000000        0         0.310000  \n",
              "\n",
              "[860 rows x 14 columns]"
            ],
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              "\n",
              "  <div id=\"df-8b313f6e-8c8c-440a-a26c-dfa5bd7480cf\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>part_time_job</th>\n",
              "      <th>attendance_percent</th>\n",
              "      <th>extracurricular_activities</th>\n",
              "      <th>weekly_self_study_hours</th>\n",
              "      <th>math_score</th>\n",
              "      <th>history_score</th>\n",
              "      <th>physics_score</th>\n",
              "      <th>chemistry_score</th>\n",
              "      <th>biology_score</th>\n",
              "      <th>english_score</th>\n",
              "      <th>geography_score</th>\n",
              "      <th>average_score</th>\n",
              "      <th>at_risk</th>\n",
              "      <th>attrition_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>60.0</td>\n",
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              "      <td>85</td>\n",
              "      <td>92</td>\n",
              "      <td>87</td>\n",
              "      <td>83</td>\n",
              "      <td>88</td>\n",
              "      <td>81</td>\n",
              "      <td>86.571429</td>\n",
              "      <td>1</td>\n",
              "      <td>1.334286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>78</td>\n",
              "      <td>80</td>\n",
              "      <td>84</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
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              "      <td>72</td>\n",
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              "      <td>77</td>\n",
              "      <td>81</td>\n",
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              "      <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>855</th>\n",
              "      <td>0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>89</td>\n",
              "      <td>88</td>\n",
              "      <td>87</td>\n",
              "      <td>86</td>\n",
              "      <td>85</td>\n",
              "      <td>84</td>\n",
              "      <td>83</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>856</th>\n",
              "      <td>0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>95</td>\n",
              "      <td>96</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.670000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>857</th>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>84</td>\n",
              "      <td>85</td>\n",
              "      <td>86</td>\n",
              "      <td>87</td>\n",
              "      <td>88</td>\n",
              "      <td>89</td>\n",
              "      <td>90</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>95</td>\n",
              "      <td>96</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>88</td>\n",
              "      <td>89</td>\n",
              "      <td>90</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.310000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>860 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b313f6e-8c8c-440a-a26c-dfa5bd7480cf')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8b313f6e-8c8c-440a-a26c-dfa5bd7480cf 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-8b313f6e-8c8c-440a-a26c-dfa5bd7480cf');\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-a18ee4ed-f45c-4c00-b0f5-e42473ec2ead\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a18ee4ed-f45c-4c00-b0f5-e42473ec2ead')\"\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-a18ee4ed-f45c-4c00-b0f5-e42473ec2ead button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3ea604df-b71d-4a68-92ef-a44a4954180f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3ea604df-b71d-4a68-92ef-a44a4954180f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\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\": 860,\n  \"fields\": [\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\": 23.348908073907193,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.0,\n          20.0\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\": 7,\n        \"min\": 3,\n        \"max\": 40,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"math_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 22,\n        \"max\": 99,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          25,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"history_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 95,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          71,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"physics_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 20,\n        \"max\": 96,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          50,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chemistry_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 19,\n        \"max\": 97,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          80,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biology_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 18,\n        \"max\": 98,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          77,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"english_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 17,\n        \"max\": 97,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          81,\n          56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geography_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 16,\n        \"max\": 98,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          83,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.51399054518149,\n        \"min\": 19.0,\n        \"max\": 96.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          34.142857142857146,\n          59.0\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          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attrition_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26029536863143804,\n        \"min\": 0.31,\n        \"max\": 1.7400000000000002,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          0.7357142857142858,\n          0.5428571428571429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "F6gPvv2F0BZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Input values\n",
        "def get_student_input():\n",
        "    # Gather the necessary inputs from the user\n",
        "    while True:\n",
        "        try:\n",
        "            math_score = float(input(\"Enter Math score (0-100): \"))\n",
        "            if math_score < 0 or math_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            history_score = float(input(\"Enter History score (0-100): \"))\n",
        "            if history_score < 0 or history_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            physics_score = float(input(\"Enter Physics score (0-100): \"))\n",
        "            if physics_score < 0 or physics_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            chemistry_score = float(input(\"Enter Chemistry score (0-100): \"))\n",
        "            if chemistry_score < 0 or chemistry_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            biology_score = float(input(\"Enter Biology score (0-100): \"))\n",
        "            if biology_score < 0 or biology_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            english_score = float(input(\"Enter English score (0-100): \"))\n",
        "            if english_score < 0 or english_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            geography_score = float(input(\"Enter Geography score (0-100): \"))\n",
        "            if geography_score < 0 or geography_score > 100:\n",
        "                raise ValueError(\"Score must be between 0 and 100.\")\n",
        "\n",
        "            # Other fields\n",
        "            absence_days = float(input(\"Enter attendance percentage (0-100): \"))\n",
        "            if absence_days < 0 or absence_days > 100:\n",
        "                raise ValueError(\"Attendance percentage must be between 0 and 100.\")\n",
        "\n",
        "            part_time_job = int(input(\"Do you have a part-time job? (1 for Yes, 0 for No): \"))\n",
        "            if part_time_job not in [0, 1]:\n",
        "                raise ValueError(\"Invalid input. Please enter 1 for Yes or 0 for No.\")\n",
        "\n",
        "            extracurricular_activities = int(input(\"Do you participate in extracurricular activities? (1 for Yes, 0 for No): \"))\n",
        "            if extracurricular_activities not in [0, 1]:\n",
        "                raise ValueError(\"Invalid input. Please enter 1 for Yes or 0 for No.\")\n",
        "\n",
        "            weekly_self_study_hours = float(input(\"Enter weekly self-study hours: \"))\n",
        "            if weekly_self_study_hours < 0:\n",
        "                raise ValueError(\"Weekly self-study hours cannot be negative.\")\n",
        "\n",
        "            # Calculate average score based on inputs\n",
        "            average_score = np.mean([math_score, history_score, physics_score, chemistry_score, biology_score, english_score, geography_score])\n",
        "\n",
        "            # Create a single input vector\n",
        "            student_input = pd.DataFrame([[average_score, absence_days, part_time_job, extracurricular_activities, weekly_self_study_hours]],\n",
        "                                         columns=['average_score', 'attendance_percent', 'part_time_job', 'extracurricular_activities', 'weekly_self_study_hours'])\n",
        "            # DataFrame for all subject marks\n",
        "            student_marks = pd.DataFrame([[math_score, history_score, physics_score, chemistry_score, biology_score, english_score, geography_score]],\n",
        "                                         columns=['math_score', 'history_score', 'physics_score', 'chemistry_score', 'biology_score', 'english_score', 'geography_score'])\n",
        "\n",
        "            return student_input, student_marks\n",
        "\n",
        "        except ValueError as e:\n",
        "            print(e)  # Print the error message\n",
        "        except Exception as e:\n",
        "            print(f\"An unexpected error occurred: {e}\")  # Catch all other exceptions\n"
      ],
      "metadata": {
        "id": "JY2rCjhcwMCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_risk_and_attrition(student_input):\n",
        "    # Classification prediction (At risk or not)\n",
        "    risk_prediction = model.predict(student_input)[0]\n",
        "\n",
        "    # Regression prediction (Attrition score)\n",
        "    attrition_prediction = rf_model.predict(student_input)[0]\n",
        "\n",
        "    return risk_prediction, attrition_prediction"
      ],
      "metadata": {
        "id": "fvHh81xouYwt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to suggest specific subjects to focus on based on average scores\n",
        "def suggest_subjects(student_input):\n",
        "    subjects = ['Math', 'History', 'Physics', 'Chemistry', 'Biology', 'English', 'Geography']\n",
        "    low_scores = []\n",
        "\n",
        "    for subject in subjects:\n",
        "        if student_input[subject.lower() + '_score'].values[0] < 60:\n",
        "            low_scores.append(subject)\n",
        "\n",
        "    return low_scores\n",
        "\n",
        "def check_consistency(user_input):\n",
        "    # Example rule: if study hours are high but average score is low, flag as inconsistent\n",
        "    # Accessing values from the DataFrame using column names\n",
        "    study_hours = user_input['weekly_self_study_hours'].values[0]\n",
        "    avg_score = user_input['average_score'].values[0]\n",
        "    attendance = user_input['attendance_percent'].values[0]\n",
        "\n",
        "    # Example threshold\n",
        "    if study_hours > 30 and avg_score < 50:  # You can adjust thresholds\n",
        "        return 1\n",
        "\n",
        "# Recommend based on Predictions (with Risk and Dropout Suggestion)\n",
        "def generate_recommendations(attrition_score, risk_prediction, student_input, student_marks):\n",
        "    attendance = student_input['attendance_percent'].values[0]\n",
        "    average_score = student_input['average_score'].values[0]\n",
        "    part_time_job = student_input['part_time_job'].values[0]\n",
        "    extracurricular_activities = student_input['extracurricular_activities'].values[0]\n",
        "    self_study_hours = student_input['weekly_self_study_hours'].values[0]\n",
        "\n",
        "    # Identify subjects with low marks (below 60)\n",
        "    low_mark_subjects = suggest_subjects(student_marks)\n",
        "    print(\"\\n=== Recommendations ===\\n\")\n",
        "\n",
        "    # Risk Prediction Logic: Whether the student is at risk based on classification\n",
        "    # if risk_prediction == 1:\n",
        "    #     print(\"⚠️ You have been flagged as 'at risk' of dropping out. Immediate intervention is recommended.\\n\")\n",
        "    # Check for DROP recommendation first\n",
        "    if attrition_score > 1.4 and risk_prediction == 1:\n",
        "        print(\"❗ Consider taking a DROP as it is too difficult to cover up.\")\n",
        "        return\n",
        "\n",
        "    if attrition_score <= 0.6 and risk_prediction == 0:  # Low risk based on both models\n",
        "        print(\"✅ Your attrition risk is low, and you're not flagged as 'at risk.' Keep up the good work!\")\n",
        "        if average_score > 85:\n",
        "            print(\"- You're excelling academically.\")\n",
        "        if extracurricular_activities:\n",
        "            print(\"- Great job balancing studies and extracurricular activities.\")\n",
        "    elif 0.6 <= attrition_score < 1.0 and risk_prediction == 0:  # Moderate risk but not flagged 'at risk'\n",
        "        print(\"⚠️ You have a moderate risk of attrition. Here are some suggestions to reduce your risk:\")\n",
        "        if average_score < 70:\n",
        "            print(\"- Focus on improving your academic scores. Consider setting a structured study plan.\")\n",
        "        if attendance < 75:\n",
        "            print(\"- Work on improving your attendance. Set reminders or alarms to ensure regular class participation.\")\n",
        "        if extracurricular_activities:\n",
        "            print(\"- Joining extracurricular activities could relieve stress but attending lectures is neccessary to keep your attendance.\")\n",
        "        if self_study_hours < 10:\n",
        "            print(\"- Increase your weekly self-study hours to strengthen your understanding of subjects.\")\n",
        "        if low_mark_subjects:\n",
        "            print(f\"- Focus on improving in these subjects: {', '.join(low_mark_subjects)}.\")\n",
        "\n",
        "    elif 0.6 <= attrition_score < 1.0 and risk_prediction == 1:  # Moderate attrition but flagged as 'at risk'\n",
        "        print(\"⚠️ Although your attrition score is moderate, you're flagged as 'at risk.' Immediate action is required to prevent further decline.\")\n",
        "        if average_score < 70:\n",
        "            print(\"- You need to significantly improve your academic performance.\")\n",
        "        if attendance < 75:\n",
        "            print(\"- Your attendance is a major concern. Make a conscious effort to attend all classes from now.\")\n",
        "        if self_study_hours < 10:\n",
        "            print(\"- You should allocate more time for self-study. Aim for at least 10 hours of focused study per week.\")\n",
        "        if part_time_job:\n",
        "            print(\"- Consider reducing your part-time job hours as it might be affecting your academic performance.\")\n",
        "        if low_mark_subjects:\n",
        "            print(f\"- Pay special attention to these subjects where you're scoring low: {', '.join(low_mark_subjects)}.\")\n",
        "\n",
        "    elif 1.2 <= attrition_score <= 1.4 and risk_prediction == 1:  # High attrition score or high risk\n",
        "        print(\"🚨 Your attrition risk is high. Immediate intervention is strongly recommended.\")\n",
        "        if average_score < 50:\n",
        "            print(\"- Your academic scores are very low. It’s crucial to seek help from teachers or academic counselors immediately.\")\n",
        "        if attendance < 60:\n",
        "            print(\"- Very low attendance is contributing to your high attrition risk. Work on attending classes on regular basis.\")\n",
        "        if self_study_hours < 10:\n",
        "            print(\"- Increase self-study hours and work with mentors to create a better study plan.\")\n",
        "        if part_time_job:\n",
        "            print(\"- Your part-time job may be affecting your studies. Consider reducing work hours and pay attention to studies.\")\n",
        "        if low_mark_subjects:\n",
        "            print(f\"- You are struggling in these subjects: {', '.join(low_mark_subjects)}.\")\n"
      ],
      "metadata": {
        "id": "FRXgzReUuaJo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Testing\n",
        "# Example for debugging\n",
        "sample_input = {\n",
        "    'average_score': 90,  # For example\n",
        "    'attendance_percent': 32,\n",
        "    'part_time_job': 0,\n",
        "    'extracurricular_activities': 1,\n",
        "    'weekly_self_study_hours': 12\n",
        "}\n",
        "\n",
        "# Manually compute the attrition score\n",
        "manual_score = calculate_attrition_score(sample_input)\n",
        "\n",
        "# Get model prediction\n",
        "input_features = np.array([[sample_input['average_score'],\n",
        "                            sample_input['attendance_percent'],\n",
        "                            sample_input['part_time_job'],\n",
        "                            sample_input['extracurricular_activities'],\n",
        "                            sample_input['weekly_self_study_hours']]])\n",
        "\n",
        "model_prediction = rf_model.predict(input_features)[0]\n",
        "\n",
        "print(f'Manual Attrition Score: {manual_score}')\n",
        "print(f'Model Prediction: {model_prediction}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rFTnZlYc6rd3",
        "outputId": "ae5f6dbb-7959-4db7-ff4f-9080934f0dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Manual Attrition Score: 1.0\n",
            "Model Prediction: 1.0193714285714284\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "3643# Get user input1\n",
        "student_input, student_marks = get_student_input()\n",
        "1\n",
        "# print(student_input)\n",
        "# print(student_marks)\n",
        "if check_consistency(student_input):\n",
        "    print(\"\")\n",
        "    print(\"Warning: The user shows potential inconsistency.\")\n",
        "\n",
        "risk_prediction, attrition_score = predict_risk_and_attrition(student_input)\n",
        "# Generate recommendations\n",
        "recommendations = generate_recommendations(attrition_score, risk_prediction, student_input, student_marks)\n",
        "\n",
        "# Output the results and recommendat9ions\n",
        "print(f\"Risk Prediction: {'At Risk' if risk_prediction == 1 else 'Not At Risk'}\")\n",
        "print(f\"Attrition Score: {attrition_score:.2f}\")"
      ],
      "metadata": {
        "id": "xELfN2zrucF_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b95f0eea-29b2-4fe2-ec7f-195333abc71d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Math score (0-100): 50\n",
            "Enter History score (0-100): 55\n",
            "Enter Physics score (0-100): 86\n",
            "Enter Chemistry score (0-100): 94\n",
            "Enter Biology score (0-100): 46\n",
            "Enter English score (0-100): 88\n",
            "Enter Geography score (0-100): 75\n",
            "Enter attendance percentage (0-100): 48\n",
            "Do you have a part-time job? (1 for Yes, 0 for No): 0\n",
            "Do you participate in extracurricular activities? (1 for Yes, 0 for No): 1\n",
            "Enter weekly self-study hours: 12\n",
            "\n",
            "=== Recommendations ===\n",
            "\n",
            "⚠️ Although your attrition score is moderate, you're flagged as 'at risk.' Immediate action is required to prevent further decline.\n",
            "- Your attendance is a major concern. Make a conscious effort to attend all classes from now.\n",
            "- Pay special attention to these subjects where you're scoring low: Math, History, Biology.\n",
            "Risk Prediction: At Risk\n",
            "Attrition Score: 0.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title GAP Analysis for Attrition value\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Assuming you already have the trained RandomForestRegressor model\n",
        "# regression_model.fit(X_train_reg, y_train_reg)\n",
        "\n",
        "# Function to plot feature contributions and ideal values\n",
        "def plot_feature_comparison(model, user_input, feature_names, ideal_values):\n",
        "    # Calculate the user's actual feature values contribution\n",
        "    user_values = user_input[0]\n",
        "    user_contributions = user_values\n",
        "\n",
        "    # Normalize user contributions for better comparison\n",
        "    user_contributions = user_contributions / np.sum(user_contributions)\n",
        "\n",
        "    # Normalize the ideal values\n",
        "    ideal_contributions = ideal_values / np.sum(ideal_values)\n",
        "\n",
        "    # Plotting two pie charts side by side\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))\n",
        "\n",
        "    # Pie chart for user's feature contributions\n",
        "    ax1.pie(user_contributions, labels=feature_names, autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)\n",
        "    ax1.set_title(\"User's Feature Contribution to Attrition Score\")\n",
        "\n",
        "    # Pie chart for ideal contributions\n",
        "    ax2.pie(ideal_contributions, labels=feature_names, autopct='%1.1f%%', startangle=90, colors=plt.cm.Paired.colors)\n",
        "    ax2.set_title(\"Ideal Feature Values to Minimize Attrition\")\n",
        "\n",
        "    # Show the plots\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Define the feature names (matching the columns in your dataframe)\n",
        "feature_names = ['average_score', 'attendance_percent', 'part_time_job', 'extracurricular_activities', 'weekly_self_study_hours']\n",
        "\n",
        "# Example of ideal values to keep attrition low (based on expert opinion or data)\n",
        "ideal_values = np.array([0.3, 0.5, 0.3, 0.2, 0.1])  # Hypothetical ideal values 70 score, 50 attendance, 0.3 job, 0.2 activities, 0.1 weekly study hours.\n",
        "\n",
        "# Call the function to plot both the user's contribution and ideal values\n",
        "\n",
        "attendance = (student_input['attendance_percent'].values[0]) / 100\n",
        "average_score = (student_input['average_score'].values[0]) / 100\n",
        "part_time_job = student_input['part_time_job'].values[0] * 0.1\n",
        "extracurricular_activities = student_input['extracurricular_activities'].values[0] * 0.1\n",
        "self_study_hours = student_input['weekly_self_study_hours'].values[0] / 100\n",
        "\n",
        "user_input = np.array([[average_score, attendance, part_time_job, extracurricular_activities, self_study_hours]])\n",
        "# print(user_input)\n",
        "plot_feature_comparison(rf_model, user_input, feature_names, ideal_values)\n",
        "\n",
        "print(f\"Risk Prediction: {'At Risk' if risk_prediction == 1 else 'Not At Risk'}\")\n",
        "print(f\"Attrition Score: {attrition_score:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "a7efffa9-0235-436f-d440-84d53e70a4db",
        "id": "r9bHxt88b8ai"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Risk Prediction: At Risk\n",
            "Attrition Score: 0.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## USER INPUT"
      ],
      "metadata": {
        "id": "VpCBkefTyU_R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get user input\n",
        "student_input, student_marks = get_student_input()\n",
        "\n",
        "# Predict risk and attrition score\n",
        "risk_prediction, attrition_score = predict_risk_and_attrition(student_input)\n",
        "\n",
        "# Generate recommendations\n",
        "recommendations = generate_recommendations(attrition_score, risk_prediction, student_input, student_marks)\n",
        "\n",
        "# Output the results and recommendations\n",
        "print(f\"Risk Prediction: {'At Risk' if risk_prediction == 1 else 'Not At Risk'}\")\n",
        "print(f\"Attrition Score: {attrition_score:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd1413c2-3ea7-49c7-a8aa-43edb1b71158",
        "id": "UswBkJbnb8aj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Math score (0-100): 10\n",
            "Enter History score (0-100): 7\n",
            "Enter Physics score (0-100): 9\n",
            "Enter Chemistry score (0-100): 12\n",
            "Enter Biology score (0-100): 15\n",
            "Enter English score (0-100): 11\n",
            "Enter Geography score (0-100): 20\n",
            "Enter attendance percentage (0-100): 15\n",
            "Do you have a part-time job? (1 for Yes, 0 for No): 0\n",
            "Do you participate in extracurricular activities? (1 for Yes, 0 for No): 1\n",
            "Enter weekly self-study hours: 8\n",
            "\n",
            "=== Recommendations ===\n",
            "\n",
            "❗ Consider taking a DROP as it is too difficult to cover up.\n",
            "Risk Prediction: At Risk\n",
            "Attrition Score: 1.60\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title GAP Analysis for Attrition value\n",
        "user_input = np.array([[average_score, attendance, part_time_job, extracurricular_activities, self_study_hours]])\n",
        "# print(user_input)\n",
        "plot_feature_comparison(rf_model, user_input, feature_names, ideal_values)\n",
        "\n",
        "print(f\"Risk Prediction: {'At Risk' if risk_prediction == 1 else 'Not At Risk'}\")\n",
        "print(f\"Attrition Score: {attrition_score:.2f}\")\n"
      ],
      "metadata": {
        "id": "mRqrqqnW930b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "c19a0df3-56dd-4a17-9c5b-32b37bb1d090"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Risk Prediction: At Risk\n",
            "Attrition Score: 1.60\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get user input\n",
        "student_input, student_marks = get_student_input()\n",
        "\n",
        "# Predict risk and attrition score\n",
        "risk_prediction, attrition_score = predict_risk_and_attrition(student_input)\n",
        "\n",
        "# Generate recommendations\n",
        "recommendations = generate_recommendations(attrition_score, risk_prediction, student_input, student_marks)\n",
        "\n",
        "# Output the results and recommendations\n",
        "print(f\"Risk Prediction: {'At Risk' if risk_prediction == 1 else 'Not At Risk'}\")\n",
        "print(f\"Attrition Score: {attrition_score:.2f}\")\n"
      ],
      "metadata": {
        "id": "J5rES-JhQw6Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3854537d-bc96-4ffb-e0f7-3c46a938e3d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Math score (0-100): 98\n",
            "Enter History score (0-100): 96\n",
            "Enter Physics score (0-100): 95\n",
            "Enter Chemistry score (0-100): 89\n",
            "Enter Biology score (0-100): 88\n",
            "Enter English score (0-100): 87\n",
            "Enter Geography score (0-100): 94\n",
            "Enter attendance percentage (0-100): 87\n",
            "Do you have a part-time job? (1 for Yes, 0 for No): 0\n",
            "Do you participate in extracurricular activities? (1 for Yes, 0 for No): 1\n",
            "Enter weekly self-study hours: 18\n",
            "\n",
            "=== Recommendations ===\n",
            "\n",
            "✅ Your attrition risk is low, and you're not flagged as 'at risk.' Keep up the good work!\n",
            "- You're excelling academically.\n",
            "- Great job balancing studies and extracurricular activities.\n",
            "Risk Prediction: Not At Risk\n",
            "Attrition Score: 0.41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title GAP Analysis for Attrition value\n",
        "user_input = np.array([[average_score, attendance, part_time_job, extracurricular_activities, self_study_hours]])\n",
        "# print(user_input)\n",
        "plot_feature_comparison(rf_model, user_input, feature_names, ideal_values)\n",
        "\n",
        "print(f\"Risk Prediction: {'At Risk' if risk_prediction == 1 else 'Not At Risk'}\")\n",
        "print(f\"Attrition Score: {attrition_score:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "uB11Zfs7HC2J",
        "outputId": "91acaa72-4331-4547-e51d-358e825f5d93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Risk Prediction: Not At Risk\n",
            "Attrition Score: 0.41\n"
          ]
        }
      ]
    }
  ]
}