383_Group_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Project Update : Predicting Students' End-of-Term Performances"
      ],
      "metadata": {
        "id": "FZk7yCPr3gFl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Team members\n",
        "\n",
        "1) Saulloa@csumb.edu\n",
        "2) nmahanloo@csumb.edu\n",
        "3) chzavala@csumb.edu\n",
        "4) doj@csumb.edu\n"
      ],
      "metadata": {
        "id": "vZL9OmxdfF-O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Introduction**\n",
        "\n",
        "Our project focuses on utilizing machine learning to predict students' end-of-term performances. By gathering comprehensive data encompassing personal details, study habits, and family support, we aim to construct a predictive model capable of accurately forecasting final grades. Currently, we are in the process of meticulously refining our dataset to identify key variables. As our work progresses, we anticipate uncovering discernible patterns that will enhance the efficacy of our predictive model, potentially offering valuable insights for educators to better support student success."
      ],
      "metadata": {
        "id": "jvMhpuYF4dfX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Choice of Dataset**\n",
        "\n",
        "The datasets we've chosen play a vital role in training our model to predict students' GPAs accurately. They capture key aspects of a student's academic journey, from demographics to study habits and external factors. We omitted certain columns that didn't align with our research goals or lacked relevance. By focusing on these essential features, we aim to create a robust model that provides valuable insights for educators and stakeholders alike.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "The list with the provided numbering system:\n",
        "\n",
        "1. Student Age (1: 18-21, 2: 22-25, 3: above 26)\n",
        "3. Graduated high-school type: (1: private, 2: state, 3: other)\n",
        "4. Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full)\n",
        "5. Additional work: (1: Yes, 2: No)\n",
        "6. Regular artistic or sports activity: (1: Yes, 2: No)\n",
        "7. Do you have a partner: (1: Yes, 2: No)\n",
        "8. Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410)\n",
        "10. Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other)\n",
        "17. Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours)\n",
        "20. Attendance to the seminars/conferences related to the department: (1: Yes, 2: No)\n",
        "21. Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral)\n",
        "22. Attendance to classes (1: always, 2: sometimes, 3: never)\n",
        "23. Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable)\n",
        "24. Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never)\n",
        "25. Taking notes in classes: (1: never, 2: sometimes, 3: always)\n",
        "26. Listening in classes: (1: never, 2: sometimes, 3: always)\n",
        "27. Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always)\n",
        "30. Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49)\n",
        "32. OUTPUT Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "**What it is you are going to predict**\n",
        "\n",
        "\n",
        "The goal of our project is to use the information from the dataset to develop a forecast that will give you an accurate prediction of the end-of-term performance of the student.\n",
        "\n",
        "**What features you plan to use as predictors**\n",
        "\n",
        "\n",
        "To build an effective predictive model, we'll be using specific features as predictors. These include the student's Grade Point Average (GPA), the number of hours they dedicate to studying each week, their class attendance, how often they take notes during lectures, and their unique STUDENT ID. By considering these key factors, we hope to gain a deeper insight into students' academic behaviors and improve the accuracy of our predictions.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mlUSRZ2X4-vk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preliminary work on data preparation**\n",
        "\n",
        "\n",
        "*   Data cleaning: Identify missing values and remove unnecessary rows and columns.\n",
        "*   ---“StandardScaler” and/or “MinMaxScaler” for normalization from “sklearn.preprocessing”\n",
        "*  Scaling numeric features, since the dataset varies in size\n",
        "*  Scaling categorical features, we plan on adding value to the non-numeric features"
      ],
      "metadata": {
        "id": "LYD-HVa98Ykg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Preliminary work on data exploration and visualization**\n",
        "\n",
        "Presenting graphs below based on the dataset, highlighting the correlation with GPA as the primary focus."
      ],
      "metadata": {
        "id": "bZz5IcMH9LHv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas numpy matplotlib seaborn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0iv1m93k-fB",
        "outputId": "7c77b8f4-44f3-4eb9-eef9-88d304c17355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lUd6WaJ4azOe"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure visualizations\n",
        "sns.set(style='whitegrid')\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/kd65541/CST383_project/main/archive/StudentsPerformance_with_headers.csv'\n",
        "df = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "xE6kPUHqfYvu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKChPtwLWh4_",
        "outputId": "b2473803-7865-4aa5-9544-898a4f672d3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  STUDENT ID  Student Age  Sex  Graduated high-school type  Scholarship type  \\\n",
            "0   STUDENT1            2    2                           3                 3   \n",
            "1   STUDENT2            2    2                           3                 3   \n",
            "2   STUDENT3            2    2                           2                 3   \n",
            "3   STUDENT4            1    1                           1                 3   \n",
            "4   STUDENT5            2    2                           1                 3   \n",
            "\n",
            "   Additional work  Regular artistic or sports activity  \\\n",
            "0                1                                    2   \n",
            "1                1                                    2   \n",
            "2                2                                    2   \n",
            "3                1                                    2   \n",
            "4                2                                    2   \n",
            "\n",
            "   Do you have a partner  Total salary if available  \\\n",
            "0                      2                          1   \n",
            "1                      2                          1   \n",
            "2                      2                          2   \n",
            "3                      1                          2   \n",
            "4                      1                          3   \n",
            "\n",
            "   Transportation to the university  ...  Preparation to midterm exams 1  \\\n",
            "0                                 1  ...                               1   \n",
            "1                                 1  ...                               1   \n",
            "2                                 4  ...                               1   \n",
            "3                                 1  ...                               1   \n",
            "4                                 1  ...                               2   \n",
            "\n",
            "   Preparation to midterm exams 2  Taking notes in classes  \\\n",
            "0                               1                        3   \n",
            "1                               1                        3   \n",
            "2                               1                        2   \n",
            "3                               2                        3   \n",
            "4                               1                        2   \n",
            "\n",
            "   Listening in classes  \\\n",
            "0                     2   \n",
            "1                     2   \n",
            "2                     2   \n",
            "3                     2   \n",
            "4                     2   \n",
            "\n",
            "   Discussion improves my interest and success in the course  Flip-classroom  \\\n",
            "0                                                  1                       2   \n",
            "1                                                  3                       2   \n",
            "2                                                  1                       1   \n",
            "3                                                  2                       1   \n",
            "4                                                  2                       1   \n",
            "\n",
            "   Cumulative grade point average in the last semester (/4.00)  \\\n",
            "0                                                  1             \n",
            "1                                                  2             \n",
            "2                                                  2             \n",
            "3                                                  3             \n",
            "4                                                  2             \n",
            "\n",
            "   Expected Cumulative grade point average in the graduation (/4.00)  \\\n",
            "0                                                  1                   \n",
            "1                                                  3                   \n",
            "2                                                  2                   \n",
            "3                                                  2                   \n",
            "4                                                  2                   \n",
            "\n",
            "   COURSE ID  GRADE  \n",
            "0          1      1  \n",
            "1          1      1  \n",
            "2          1      1  \n",
            "3          1      1  \n",
            "4          1      1  \n",
            "\n",
            "[5 rows x 33 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4H1KIiRW4zS",
        "outputId": "3ab080a8-6feb-4738-f6b4-caa8dfedcfaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 145 entries, 0 to 144\n",
            "Data columns (total 33 columns):\n",
            " #   Column                                                             Non-Null Count  Dtype \n",
            "---  ------                                                             --------------  ----- \n",
            " 0   STUDENT ID                                                         145 non-null    object\n",
            " 1   Student Age                                                        145 non-null    int64 \n",
            " 2   Sex                                                                145 non-null    int64 \n",
            " 3   Graduated high-school type                                         145 non-null    int64 \n",
            " 4   Scholarship type                                                   145 non-null    int64 \n",
            " 5   Additional work                                                    145 non-null    int64 \n",
            " 6   Regular artistic or sports activity                                145 non-null    int64 \n",
            " 7   Do you have a partner                                              145 non-null    int64 \n",
            " 8   Total salary if available                                          145 non-null    int64 \n",
            " 9   Transportation to the university                                   145 non-null    int64 \n",
            " 10  Accommodation type in Cyprus                                       145 non-null    int64 \n",
            " 11  Mother’s education                                                 145 non-null    int64 \n",
            " 12  Father’s education                                                 145 non-null    int64 \n",
            " 13  Number of sisters/brothers                                         145 non-null    int64 \n",
            " 14  Parental status                                                    145 non-null    int64 \n",
            " 15  Mother’s occupation                                                145 non-null    int64 \n",
            " 16  Father’s occupation                                                145 non-null    int64 \n",
            " 17  Weekly study hours                                                 145 non-null    int64 \n",
            " 18  Reading frequency                                                  145 non-null    int64 \n",
            " 19  Reading frequency.1                                                145 non-null    int64 \n",
            " 20  Attendance to the seminars/conferences related to the department   145 non-null    int64 \n",
            " 21  Impact of your projects/activities on your success                 145 non-null    int64 \n",
            " 22  Attendance to classes                                              145 non-null    int64 \n",
            " 23  Preparation to midterm exams 1                                     145 non-null    int64 \n",
            " 24  Preparation to midterm exams 2                                     145 non-null    int64 \n",
            " 25  Taking notes in classes                                            145 non-null    int64 \n",
            " 26  Listening in classes                                               145 non-null    int64 \n",
            " 27  Discussion improves my interest and success in the course          145 non-null    int64 \n",
            " 28  Flip-classroom                                                     145 non-null    int64 \n",
            " 29  Cumulative grade point average in the last semester (/4.00)        145 non-null    int64 \n",
            " 30  Expected Cumulative grade point average in the graduation (/4.00)  145 non-null    int64 \n",
            " 31  COURSE ID                                                          145 non-null    int64 \n",
            " 32  GRADE                                                              145 non-null    int64 \n",
            "dtypes: int64(32), object(1)\n",
            "memory usage: 37.5+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xoqowg2OW9Md",
        "outputId": "7e0ae159-ac77-44b0-805f-4d27ec630d08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Student Age         Sex  Graduated high-school type  Scholarship type  \\\n",
            "count   145.000000  145.000000                  145.000000        145.000000   \n",
            "mean      1.620690    1.600000                    1.944828          3.572414   \n",
            "std       0.613154    0.491596                    0.537216          0.805750   \n",
            "min       1.000000    1.000000                    1.000000          1.000000   \n",
            "25%       1.000000    1.000000                    2.000000          3.000000   \n",
            "50%       2.000000    2.000000                    2.000000          3.000000   \n",
            "75%       2.000000    2.000000                    2.000000          4.000000   \n",
            "max       3.000000    2.000000                    3.000000          5.000000   \n",
            "\n",
            "       Additional work  Regular artistic or sports activity  \\\n",
            "count       145.000000                           145.000000   \n",
            "mean          1.662069                             1.600000   \n",
            "std           0.474644                             0.491596   \n",
            "min           1.000000                             1.000000   \n",
            "25%           1.000000                             1.000000   \n",
            "50%           2.000000                             2.000000   \n",
            "75%           2.000000                             2.000000   \n",
            "max           2.000000                             2.000000   \n",
            "\n",
            "       Do you have a partner  Total salary if available  \\\n",
            "count             145.000000                 145.000000   \n",
            "mean                1.579310                   1.627586   \n",
            "std                 0.495381                   1.020245   \n",
            "min                 1.000000                   1.000000   \n",
            "25%                 1.000000                   1.000000   \n",
            "50%                 2.000000                   1.000000   \n",
            "75%                 2.000000                   2.000000   \n",
            "max                 2.000000                   5.000000   \n",
            "\n",
            "       Transportation to the university  Accommodation type in Cyprus  ...  \\\n",
            "count                        145.000000                    145.000000  ...   \n",
            "mean                           1.620690                      1.731034  ...   \n",
            "std                            1.061112                      0.783999  ...   \n",
            "min                            1.000000                      1.000000  ...   \n",
            "25%                            1.000000                      1.000000  ...   \n",
            "50%                            1.000000                      2.000000  ...   \n",
            "75%                            2.000000                      2.000000  ...   \n",
            "max                            4.000000                      4.000000  ...   \n",
            "\n",
            "       Preparation to midterm exams 1  Preparation to midterm exams 2  \\\n",
            "count                      145.000000                      145.000000   \n",
            "mean                         1.337931                        1.165517   \n",
            "std                          0.614870                        0.408483   \n",
            "min                          1.000000                        1.000000   \n",
            "25%                          1.000000                        1.000000   \n",
            "50%                          1.000000                        1.000000   \n",
            "75%                          2.000000                        1.000000   \n",
            "max                          3.000000                        3.000000   \n",
            "\n",
            "       Taking notes in classes  Listening in classes  \\\n",
            "count               145.000000            145.000000   \n",
            "mean                  2.544828              2.055172   \n",
            "std                   0.564940              0.674736   \n",
            "min                   1.000000              1.000000   \n",
            "25%                   2.000000              2.000000   \n",
            "50%                   3.000000              2.000000   \n",
            "75%                   3.000000              3.000000   \n",
            "max                   3.000000              3.000000   \n",
            "\n",
            "       Discussion improves my interest and success in the course  \\\n",
            "count                                         145.000000           \n",
            "mean                                            2.393103           \n",
            "std                                             0.604343           \n",
            "min                                             1.000000           \n",
            "25%                                             2.000000           \n",
            "50%                                             2.000000           \n",
            "75%                                             3.000000           \n",
            "max                                             3.000000           \n",
            "\n",
            "       Flip-classroom  \\\n",
            "count      145.000000   \n",
            "mean         1.806897   \n",
            "std          0.810492   \n",
            "min          1.000000   \n",
            "25%          1.000000   \n",
            "50%          2.000000   \n",
            "75%          2.000000   \n",
            "max          3.000000   \n",
            "\n",
            "       Cumulative grade point average in the last semester (/4.00)  \\\n",
            "count                                         145.000000             \n",
            "mean                                            3.124138             \n",
            "std                                             1.301083             \n",
            "min                                             1.000000             \n",
            "25%                                             2.000000             \n",
            "50%                                             3.000000             \n",
            "75%                                             4.000000             \n",
            "max                                             5.000000             \n",
            "\n",
            "       Expected Cumulative grade point average in the graduation (/4.00)  \\\n",
            "count                                         145.000000                   \n",
            "mean                                            2.724138                   \n",
            "std                                             0.916536                   \n",
            "min                                             1.000000                   \n",
            "25%                                             2.000000                   \n",
            "50%                                             3.000000                   \n",
            "75%                                             3.000000                   \n",
            "max                                             4.000000                   \n",
            "\n",
            "        COURSE ID       GRADE  \n",
            "count  145.000000  145.000000  \n",
            "mean     4.131034    3.227586  \n",
            "std      3.260145    2.197678  \n",
            "min      1.000000    0.000000  \n",
            "25%      1.000000    1.000000  \n",
            "50%      3.000000    3.000000  \n",
            "75%      7.000000    5.000000  \n",
            "max      9.000000    7.000000  \n",
            "\n",
            "[8 rows x 32 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDJ3Tq4qXETJ",
        "outputId": "3d35be81-17a9-4cee-dce7-81c51f46edaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "STUDENT ID                                                           0\n",
            "Student Age                                                          0\n",
            "Sex                                                                  0\n",
            "Graduated high-school type                                           0\n",
            "Scholarship type                                                     0\n",
            "Additional work                                                      0\n",
            "Regular artistic or sports activity                                  0\n",
            "Do you have a partner                                                0\n",
            "Total salary if available                                            0\n",
            "Transportation to the university                                     0\n",
            "Accommodation type in Cyprus                                         0\n",
            "Mother’s education                                                   0\n",
            "Father’s education                                                   0\n",
            "Number of sisters/brothers                                           0\n",
            "Parental status                                                      0\n",
            "Mother’s occupation                                                  0\n",
            "Father’s occupation                                                  0\n",
            "Weekly study hours                                                   0\n",
            "Reading frequency                                                    0\n",
            "Reading frequency.1                                                  0\n",
            "Attendance to the seminars/conferences related to the department     0\n",
            "Impact of your projects/activities on your success                   0\n",
            "Attendance to classes                                                0\n",
            "Preparation to midterm exams 1                                       0\n",
            "Preparation to midterm exams 2                                       0\n",
            "Taking notes in classes                                              0\n",
            "Listening in classes                                                 0\n",
            "Discussion improves my interest and success in the course            0\n",
            "Flip-classroom                                                       0\n",
            "Cumulative grade point average in the last semester (/4.00)          0\n",
            "Expected Cumulative grade point average in the graduation (/4.00)    0\n",
            "COURSE ID                                                            0\n",
            "GRADE                                                                0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "b9CgekVGXSIe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['Weekly study hours', 'Attendance to classes','Preparation to midterm exams 1','Preparation to midterm exams 2' ,'Attendance to the seminars/conferences related to the department']\n",
        "target = 'GRADE'\n",
        "\n",
        "X = df[features]\n",
        "y = df[target]"
      ],
      "metadata": {
        "id": "UAKF9MKfXX06"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "L7ToaPZWZbAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "bjzByFcFZ2ts",
        "outputId": "049ff138-b053-4312-c4ed-3c583581639b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model.predict(X_test)"
      ],
      "metadata": {
        "id": "-ngg6e64aBQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear Regression model"
      ],
      "metadata": {
        "id": "C2Sg3eVmbEGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, prediction)\n",
        "r2 = r2_score(y_test, prediction)\n",
        "\n",
        "print(f'MSE: {mse}')\n",
        "print(f'R_Squared:{r2}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qez2ViaZaQLb",
        "outputId": "5ffa11db-0a79-45b2-fadf-1cfd521e5fa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 4.256013874818368\n",
            "R_Squared:0.07415735418462255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Model"
      ],
      "metadata": {
        "id": "8ih2nQHYbTK4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestRegressor(random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "rf_y_pred = rf_model.predict(X_test)"
      ],
      "metadata": {
        "id": "LcR94-R1bVoh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing the results"
      ],
      "metadata": {
        "id": "FB1MEu87b7Vr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "k45_owk6bzow",
        "outputId": "ac48ea0c-26f8-44d1-fa38-a8c26b39fef1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,2,1)\n",
        "sns.scatterplot(x=X_test['Weekly study hours'], y=y_test, label= 'Actual')\n",
        "sns.scatterplot(x=X_test['Weekly study hours'], y= rf_y_pred, label ='Predicted (Random Forest)')\n",
        "plt.title ('Weekly Study Hours vs GPA (Random Forest)')\n",
        "plt.xlabel('Weekly Study Hours')\n",
        "plt.ylabel('GPA')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.scatterplot(x=X_test['Weekly study hours'], y=y_test, label= 'Actual')\n",
        "sns.scatterplot(x=X_test['Weekly study hours'], y= rf_y_pred, label ='Predicted (Random Forest)')\n",
        "plt.title ('Weekly Study Hours vs GPA (Random Forest)')\n",
        "plt.xlabel('Weekly Study Hours')\n",
        "plt.ylabel('GPA')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.subplots_adjust(wspace=1.25)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "0aU_-EpscWPY",
        "outputId": "e94a4bbf-70a6-468c-f954-c12304e2e2eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GPA Distibution: If the distribution is normal, the model might perform well without transformation.\n",
        "\n",
        "Weekly Study Hours: Understanding how the hours are being ditributed can help identify study patterns and their impact on GPA."
      ],
      "metadata": {
        "id": "jU_vun-3sNeO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['GRADE'], kde=True)\n",
        "plt.title('GPA Distribution')\n",
        "plt.show()\n",
        "\n",
        "sns.histplot(df['Weekly study hours'], kde=True)\n",
        "plt.title('Weekly Study Hours Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 943
        },
        "id": "Ef1K-LPJq_sv",
        "outputId": "d0ae53d2-9481-4e2d-a696-fa51c2e0c880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAHPCAYAAABX4QCwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABcPElEQVR4nO3dd3wUdf4/8NdsSzZ1k5BCCklogQAhQSAgUkUFwUPPL/agno07seBZOE898fx+9Wf5WkDxpImKiPUrgYAgVQFp0qUkhJBGQtomu8km2+b3x5pIDCXZ7O7sTl7Px3Fxd2Zn35/Jllc+8/nMCKIoiiAiIiKSMYXUBRARERG5GwMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8R+YQJEyZgzpw5bn+e4uJipKSk4Jtvvmm5b86cOcjIyHD7czdLSUnBvHnzPPZ8RF2BSuoCiMi9ioqKsHTpUmzfvh1lZWUAgLi4OGRmZuLWW29Fv379WtadN28e5s+f33Lb398fcXFxuOaaa/DAAw8gKCio1baXL1+Ol156CWlpafjyyy/bXVNWVhZ2794NABAEAQEBAYiMjERaWhpuvPFGjBo1qjNNbrF161YcOnQIjzzyiEu250reXBuRHDHwEMnY5s2bMXv2bCiVStxwww3o168fFAoF8vPzsX79eqxYsQIbN25EXFxcq8e9+OKLCAgIQENDA7Zv344PPvgAu3btwooVKyAIQst62dnZiIuLw6FDh3DmzBkkJia2u7aYmBg88cQTAACTyYQzZ85gw4YNWLVqFSZPnozXX38darW6Zf1169a1eu722Lp1K5YvX96hUNHcHpXKvR+Pl6rt0KFDUCqVbn1+oq6GgYdIpgoLC/HEE08gNjYWH330EaKiolotf/LJJ/HZZ59BoWh7ZPu6665DeHg4AOD222/HI488gvXr1+PAgQMth3aKioqwf/9+zJ8/Hy+88AKys7Mxa9asdtcXHByMadOmtanp5ZdfxmeffYa4uDg89dRTLcs0Gk27t+0Mq9UKu90OjUYDPz8/tz7X5Uj9/ERyxDE8RDK1aNEiNDQ04JVXXmkTdgBApVJhxowZ6N69+2W3NWLECACO8S3NsrOzERoairFjx+K6665DdnZ2p2tWKpV47rnn0Lt3byxfvhwGg6Fl2R/H8FgsFsyfPx/XXnstBg0ahMzMTNx+++3Yvn07AMe4m+XLlwNwjIlp/tfcjpSUFCxevBgfffQRJk6ciEGDBuHUqVMXHMPTrKioCPfddx/S09Nx1VVXYf78+RBFsWX5rl27kJKSgl27drV63B+3eanamu/74xieX3/9Fffffz+GDBmCjIwM3H333Thw4ECrdb755hukpKRg3759eOWVVzBixAikp6fj4YcfRnV1dft+CUQyxR4eIpnavHkzEhMTMXjw4E5vq7CwEACg0+la7svOzsY111wDjUaDqVOnYsWKFTh06BDS0tI69VxKpRJTpkzBO++8g3379mHcuHEXXG/+/Pn4z3/+g+nTpyMtLQ1GoxFHjhzB0aNHMWrUKNx66604d+4ctm/fjtdee+2C2/jmm2/Q1NSEW265BRqNBqGhobDb7Rdc12az4f7778fgwYPx1FNP4ccff8S8efNgs9nw2GOPdaiN7antfLm5ubjzzjsRGBiI+++/HyqVCitXrkRWVhY+/fTTNr/jl19+GSEhIZg1axZKSkqwbNkyvPTSS3j77bc7VCeRnDDwEMmQ0WjEuXPnMHHixDbL6urqYLVaW24HBATA39+/1Tq1tbUA0DKG57PPPkO3bt0wdOhQAMCRI0eQn5+P559/HgBwxRVXICYmBtnZ2Z0OPADQt29fAL8HrQvZsmULxo4di3//+98XXJ6RkYGkpCRs3769zaGzZmVlZdiwYUPL4TugdS/W+ZqamjB69Gg899xzAIA77rgDM2fOxMKFC5GVldVqG5fTntrO9/bbb8NisWDFihVISEgAANx4442YNGkSXn/9dXz66aet1tfpdFiyZEnLmCe73Y5PPvkEBoMBwcHB7a6TSE54SItIhoxGIwBHmPmjrKwsjBw5suVf86GV802aNAkjR47E1VdfjRdeeAGJiYn4z3/+A61WC8DRu9OtWzdkZmYCcMy0uv7665GTkwObzdbp+pvrrq+vv+g6ISEhyM3NRUFBgdPPc+2113YoqNx5550t/y0IAu68805YLBbs3LnT6Roux2azYfv27Zg4cWJL2AGAqKgoTJ06Ffv27Wv5fTe75ZZbWg3wHjp0KGw2G0pKStxWJ5G3Yw8PkQwFBgYCcPTQ/NFLL72E+vp6VFZWthoUfL558+YhKCgIKpUKMTEx6NGjR8sym82GNWvWIDMzs1VvSFpaGpYsWYKdO3fiqquu6lT9zXU3t+NCHn30Ufztb3/Dddddh759++Kqq67CtGnTWk2zv5z4+Ph2r6tQKFoFDgBITk4GALcGierqaphMppbnOl+vXr1gt9tx9uxZ9OnTp+X+2NjYVuuFhIQAcPTuEXVVDDxEMhQcHIzIyEjk5ua2WdY83uNih24AR4/AxXo+fv75Z1RUVGDNmjVYs2ZNm+XZ2dmdDjwnT54EgEtOcx82bBg2bNiAjRs3Yvv27fjqq6+wbNkyzJ07F9OnT2/X8/zxUF5nXWza/MXGBbnLhWbeAWg1wJqoq2HgIZKpcePG4csvv3TJQOLzZWdnIyIiAi+88EKbZRs2bMCGDRswd+5cp8OEzWbD6tWrodVqccUVV1xyXZ1Oh5tvvhk333wz6uvrcdddd2HevHktgaej5+25FLvdjqKiolY9LadPnwaAlvMYNfeknD+7DLhwD1B7awsPD4dWq215rvPl5+dDoVC0a6YdUVfHMTxEMnX//fdDq9Xi2WefRWVlZZvlzvy139jYiPXr12PcuHGYNGlSm3933nkn6uvrsWnTJqdqttlsePnll3Hq1ClkZWW1ObPz+WpqalrdDgwMRI8ePWA2m1vuax5z5KpDOeePdxJFEcuXL4darcbIkSMBOIKPUqnEnj17Wj1uxYoVbbbV3tqUSiVGjRqFjRs3tuqVq6ysxOrVq3HFFVdccj8RkQN7eIhkKikpCW+88Qb+/ve/Y9KkSS1nWhZFEcXFxVi9ejUUCgViYmLavc1Nmzahvr4eEyZMuODy9PR0hIeHY9WqVbj++usvuS2DwYDvvvsOgCNINZ9pubCwEFOmTLnsVO8pU6Zg+PDhGDBgAHQ6HQ4fPozvv/8ed911V8s6AwYMAOCYpn3VVVe1THl3hp+fH3788Uc888wzSEtLw48//ogtW7Zg5syZLYf/goODMWnSJHz66acQBAEJCQnYsmULqqqq2myvI7U9/vjj2LFjB+644w7ccccdUCqVWLlyJcxm80XHYRFRaww8RDI2ceJEZGdnY8mSJdi+fTu+/vprCIKA2NhYjB07FrfffnuHBvmuWrUKfn5+F73WlUKhwLhx45CdnY2amhqEhYVddFtlZWV4+umnAThmZUVFRSE9PR0vvvhiu66llZWVhU2bNmH79u0wm82IjY3F448/jvvuu69lnWuvvRZZWVlYs2YNVq1aBVEUnQ48SqUSixYtwosvvojXX38dgYGBmDVrFh5++OFW6z333HOwWq34/PPPodFoMGnSJDz99NOYOnVqq/U6UlufPn2wfPlyvPnmm/jPf/4DURSRlpaG119/3SXnWSLqCgSRo9iIiIhI5jiGh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI/n4QGwf/9+iKIItVotdSlERETUThaLBYIgICMj47LrsocHjlPEu+t0RKIowmw2d9mL9nX19gPcB2x/124/wH3Q1dsPuG8fdOT7mz08QEvPzqBBg1y+7YaGBhw7dgy9e/dGQECAy7fv7bp6+wHuA7a/a7cf4D7o6u0H3LcPDh8+3O512cNDREREssfAQ0RERLLHwENERESyx8BDREREssfAQ0RERLLHwENERESyx8BDREREssfAQ0RERLLHwENERESyx8BDREREsudVl5bYunUrFi5ciLy8PBiNRkRHR2PixImYNWsWgoODAQBz5szBt99+2+axCxcuxJgxYzxdMhEREfkArwo8er0eaWlpyMrKgk6nQ25uLubNm4fc3FwsWbKkZb2EhAS88cYbrR7bq1cvT5dLREREPsKrAs+0adNa3c7MzIRGo8Hzzz+P8vJyREdHAwD8/f2Rnp4uQYVERETki7x+DI9OpwMAWCwWaQshIiIin+VVPTzNbDYbrFYr8vLy8N5772HChAmIj49vWX7mzBlcccUVaGpqQt++ffG3v/0NEydO7NRziqKIhoaGzpbehslkavXTlwmC0OHHmM1maLVamM1mpx7vLqIoeuy55PQacAbb37XbD3AfdPX2A+7bB6Iotvu7RRA9+cnfTmPGjEF5eTkAYPTo0Xj33XcREBAAAFi2bBlUKhV69+4Ng8GAFStW4KeffsI777yDSZMmOfV8hw8fhtlsdln9cqRWqzFgwAAolUqpS+k0m82Go0ePsteQiEgGNBoNBg0adNn1vDLwHD9+HCaTCXl5eViwYAHi4+OxdOnSC37Z2u123HbbbTAajcjJyXHq+Q4fPgxRFNG7d+/Olt6GyWRCQUEBkpKSoNVqXb59TxEEAVqtFtv2F6HW0Njux1mtVhiMBgQHBUOlkr5DMTTYH2MyEmAymTzWyyOX14Cz2P6u3X6A+6Crtx9w3z7Iy8uDIAjtCjzSfwNdQL9+/QAAGRkZGDRoEKZNm4YNGzZcsAdHoVDg2muvxeuvv47Gxkb4+/s79ZyCILT0IrmDVqt16/Y9pd5khcFka/f6FosVFVVGiAot1GrpD2mpVFYAkORDRy6vAWex/V27/QD3QVdvP+D6fdCRoRJeP2g5JSUFarUahYWFUpdCREREPsrrA8/BgwdhsVhaDVo+n91ux7p169CnTx+ne3eIiIhI3rzqkNasWbMwcOBApKSkwN/fH8ePH8fixYuRkpKCiRMnoqSkBHPmzMGUKVOQmJiI2tparFixAkeOHMG8efOkLp+IiIi8lFcFnrS0NOTk5ODDDz+EKIqIi4vD9OnTcd9990Gj0SAwMBBBQUFYsGABqqqqoFarMXDgQCxcuBCjR4+WunwiIiLyUl4VeB588EE8+OCDF12u0+mwYMECD1ZEREREcuD1Y3iIiIiIOouBh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZM+rAs/WrVtx1113YcSIERg4cCCuvvpqvPLKKzAYDK3W27RpE/70pz9h0KBBuO666/D1119LVDERERH5ApXUBZxPr9cjLS0NWVlZ0Ol0yM3Nxbx585Cbm4slS5YAAPbu3YtZs2bhv/7rv/Dss8/i559/xj//+U8EBgZi0qRJEreAiIiIvJFXBZ5p06a1up2ZmQmNRoPnn38e5eXliI6OxoIFC5CWloaXXnoJADBixAgUFRXh3XffZeAhIiKiC/KqQ1oXotPpAAAWiwVmsxm7du1qE2yuv/56nDp1CsXFxRJUSERERN7OKwOPzWZDU1MTjh49ivfeew8TJkxAfHw8CgsLYbFY0LNnz1br9+rVCwCQn58vRblERETk5bzqkFaz8ePHo7y8HAAwevRovPnmmwCA2tpaAEBISEir9ZtvNy93hiiKaGhocPrxF2MymVr99FWCIECr1cJqtcJisbT7cc3rduQx7mS1KgE4fh+iKHrkOeXyGnAW29+12w9wH3T19gPu2weiKEIQhHat65WB58MPP4TJZEJeXh4WLFiAmTNnYunSpW59TovFgmPHjrlt+wUFBW7btidotVqkpqaiRl+Diipjhx+v1+tdX5QTBHsQAOD06dMe//Dx9ddAZ7H9BVKXILmuvg+6evsB9+wDjUbTrvW8MvD069cPAJCRkYFBgwZh2rRp2LBhA3r37g0Abaap19XVAQBCQ0Odfk61Wt2yfVcymUwoKChAUlIStFqty7fvKc0JOkwXBlHR/nZYLBbo9XrodDqo1Wp3ldduYSH+AIDk5GSP9vDI4TXgLLa/a7cf4D7o6u0H3LcP8vLy2r2uVwae86WkpECtVqOwsBATJkyAWq1Gfn4+Ro8e3bJO89idP47t6QhBEBAQENDpei9Gq9W6dfueolKpnAouarXaKwKPSuV4yUvxoSOX14Cz2P6u3X6A+6Crtx9w/T5o7+EswEsHLZ/v4MGDsFgsiI+Ph0ajQWZmJr7//vtW6+Tk5KBXr16Ij4+XqEoiIiLyZl7VwzNr1iwMHDgQKSkp8Pf3x/Hjx7F48WKkpKRg4sSJAIC//vWvmDFjBl588UVMnjwZu3btwurVq/HWW29JXD0RERF5K68KPGlpacjJycGHH34IURQRFxeH6dOn47777msZlDR06FDMmzcPb7/9Nr766ivExsbi5ZdfxuTJkyWunoiIiLyVVwWeBx98EA8++OBl17v66qtx9dVXe6AiIiIikgOvH8NDRERE1FkMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPERERCR7KqkLON/atWuxatUqHD16FHV1dUhMTERWVhZuvvlmCIIAAMjKysLu3bvbPDYnJwe9evXydMlERETkA7wq8Hz00UeIi4vDnDlzEBYWhh07duD5559HWVkZZs2a1bLekCFD8Mwzz7R6bHx8vKfLJSIiIh/hVYFnwYIFCA8Pb7k9cuRI6PV6LF26FH/729+gUDiOwIWEhCA9PV2iKomIiMjXeNUYnvPDTrP+/fvDaDSioaFBgoqIiIhIDryqh+dC9u3bh+joaAQFBbXct3v3bqSnp8Nms2Hw4MF47LHHMGzYsE49jyiKbglVJpOp1U9fJQgCtFotrFYrLBZLux/XvG5HHuNOVqsSgOP3IYqiR55TLq8BZ7H9Xbv9APdBV28/4L59IIpiyxjfy/HqwLN3717k5OS0Gq8zbNgwTJs2DUlJSTh37hwWL16Me++9F5988gkyMjKcfi6LxYJjx465ouwLKigocNu2PUGr1SI1NRU1+hpUVBk7/Hi9Xu/6opwg2B3B+fTp0x7/8PH110Bnsf0FUpcgua6+D7p6+wH37AONRtOu9QTRU3/mdlBZWRmmT5+OXr16YcmSJS3jd/6ooaEBU6dORa9evbBw4UKnnuvw4cMQRRG9e/fuTMkXZDKZUFBQgKSkJGi1Wpdv31Oae3iyt+Wiuq6x3Y+zWCzQ6/XQ6XRQq9VurLB9wkP8ccOYPh7v4ZHDa8BZbH/Xbj/AfdDV2w+4bx/k5eVBEAQMGjTosut6ZQ9PXV0dHnjgAeh0OsybN++iYQcAAgICMHbsWHz//fedek5BEBAQENCpbVyKVqt16/Y9RaVSORVc1Gq1VwQelcrxkpfiQ0curwFnsf1du/0A90FXbz/g+n3Q3sNZgBcGnsbGRjz00EMwGAxYuXIlgoODpS6JiIiIfJxXBR6r1YrHH38c+fn5WL58OaKjoy/7mIaGBmzZsqVd3VlERETUNXlV4Jk7dy42b96MOXPmwGg04sCBAy3LUlNTcejQISxatAjXXHMN4uLicO7cOSxduhQVFRV45513pCuciIiIvJpXBZ7t27cDAF599dU2yzZu3IjIyEhYLBa89dZb0Ov10Gq1yMjIwNy5c5GWlubpcomIiMhHeFXg2bRp02XXWbx4sQcqISIiIjnxqjMtExEREbkDAw+RmzWfw6gj0yeJiMi1vOqQFpG7+WuUHToVuSs0n6Xa1TzdDiIiX8bAQ12KRq2EIAjYcagUtcYmjzyn1WpFjb4GYbqwlhMfdlZokB+uTIt1ybaIiLoCBh7qkmqNTagxeCbwWCwWVFQZISq0XnG2aSKirohjeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iIiISPYYeIiIiEj2GHiIiIhI9hh4iMitBEGAVquFIAhSl0JEXZhK6gKIqOP8NUqIougTIUKr1SI1NfWiy32lHUTk2xh4SFZEUYRddPy3AEAQIMsvU41aCUEQsONQKWqNTVKXc0lWqxU1+hqE6cKgUrX+yAkN8sOVabESVUZEXQkDD/kcq82O6rpG6A1NqDU2wWiyoKHRisYmKyw2O0Tx93UVggC1WgE/tRIB/irEhAdAb2hCeU0DlAoF1CrfPqpba2xCjcG7A4/FYkFFlRGiQgu1Wi11OUTURTHwkNcTRRE1hiaUVhhxtrIe1XWNLb04l2MXRTSZbWgy21BXb0ZZVQMO5Fa2LA8OUKObTovuEYGIiQiEn0bpplYQEZGUGHjIaxlNFpwurUVBaR2MJkurZVo/JcJD/BEa5IfgAA0C/FXQ+qmgVimgUjp6bUQ4eoMsFjtMZisaGq1QCoDFJuLX01VoaLTC0GCBocGC06V1AIDwEH907xaIHtHB0AX7ebrJRETkJk4HnhkzZuCvf/0rRo4cecHlP//8M95//318/PHH7d7m2rVrsWrVKhw9ehR1dXVITExEVlYWbr755lbjML788kssWrQIpaWlSE5OxuzZszF+/Hhnm0JeRBRFVNSYcKKwBiXnjGjuyFEqBMREBCIuMhDR4QEI1KrbNTbHT60E/AEdHOElMSYYowbHYe2O0yitrEdNXSPKqxtwtqoetUYzqusaUV3XiKP5VdAF+yGpewgSY4IR4M9DMUREvszpwLN7925Mnz79osurq6uxZ8+eDm3zo48+QlxcHObMmYOwsDDs2LEDzz//PMrKyjBr1iwAwJo1a/D8889j5syZGDFiBHJycjBr1iwsX74c6enpzjaHvEB5dQMO51WiQm9quS86PADJsSFIiAqGysXjbbR+KmgjgxAbGYQMAA2NFpRVNaD4nBFnK43QG5pwwFCBAycrEBMRgD4JYYiNDIRChoOgiYjkrlOHtC71F/aZM2cQGBjYoe0tWLAA4eHhLbdHjhwJvV6PpUuX4m9/+xsUCgXeffddTJkyBY8//jgAYMSIETh58iTee+89LFy40Kl2kLTq6pvwy4kKnK2sBwAoFAKSY0OQ0iMMoUGeO6wU4K9Gz7hQ9IwLRZPFhqIyAwrO1qFCb0JZVQPKqhoQqFWjT4IOveJCoVFzvA8Rka/oUOD59ttv8e2337bcXrBgAb744os26xkMBpw4cQJjxozpUDHnh51m/fv3xxdffIGGhgbU1NSgoKAATz31VKt1rr/+erz22mswm83QaDQdek6SjtVqx+FTlThRWANRBBQC0Cteh9TkcMkPIfmpleidoEPvBB2MDWbkFuuRX1yLepMFB05W4HBeJXrFh6J/kvS1EhHR5XUo8JhMJtTU1LTcrq+vh0LR9jBDQEAAbrvtNjz88MOdLnDfvn2Ijo5GUFAQ9u3bBwBITk5utU6vXr1gsVhQVFSEXr16OfU8oiiioaGh0/X+kclkavXTVzWfLddqtcJisVz+Ab9pXvePjzlXY8K+4xWob7QCALpHBCCtdwSCA9QXXN9VbDYbAHSoHX5qAQOTw9CvRyiKyo3IK6lDrdGMk4V65BbpkRQTjJREHYK0Fw4+F9sHneFMO6RyqfZbrY5eMpPJBFFs59Q7HyOXz4DO6Or7oKu3H3DfPujIiUs7FHjuuOMO3HHHHQCACRMm4J///CeuvvrqjlfYTnv37kVOTg6eeeYZAEBtbS0AICQkpNV6zbeblzvDYrHg2LFjTj/+cgoKCty2bU9oPltujb4GFVXGDj9er9cDAOx2EafLm1Bc5fjy81ML6BPrj4hgJRrr9Wisd2XVbUUEO94YBqMBFRX6Dj8+SA0MTtRAX6/EmQozauttOH3WgNNnDYjWqZAY5Qet5sJjjZr3gSt0th1SuFD7BXsQAOD06dOy/zLw9c8AV+jq+6Crtx9wzz5o75Edp8fwbNq0ydmHtktZWRlmz56NzMxMzJgxw63PBQBqtRq9e/d2+XZNJhMKCgqQlJQErVbr8u17SnOCDtOFQVS0vx0WiwV6vR46nQ6NFmDX0XLojY6w0zM2GIN6RXj05H8hwY5wHBwUjEi784eiogD0TQYq9Y04dqYG5dUmlOutOFdrRa+4EPRLDIP/b+f0OX8fuOrEe65qhydcqv1hIf4AHL22cu7hkcNnQGd09X3Q1dsPuG8f5OXltXvdTp+Hx2g0orS0FHV1dRf8wBo2bFiHt1lXV4cHHngAOp0O8+bNazlsFhoaCsAxRigyMrLV+ucvd4YgCAgICHD68Zej1Wrdun1PUalUTn1pV9SasedYBSxWOzRqJUYMiEFcVJAbKrw0pdIRQpxtxx91j1Sje2QwqmpNOJhbifLqBuQV16HgrAH9EsPRLykczU+jVqtdFnhc3Q5PuFD7my810RW+BOTyGdAZXX0fdPX2A67fBx25dJDTgae6uhovv/wy1q9f3zKe4HzNx9U6epiosbERDz30EAwGA1auXIng4OCWZT179gQA5Ofnt/x38221Wo2EhAQnW0PuIooizpxrQsE5AwCgm84fo9LiEOAvr3NeRoRqMWFoAsqq6nEwtxLVdY04kl+FUyV6DOwZDq1Cnr0XRES+wulvnRdeeAGbN29GVlYWhg4d2mZcjTOsVisef/xx5OfnY/ny5YiOjm61PCEhAUlJSVi3bh0mTpzYcn9OTg5GjhzJGVpexmYXsfd4Bc6cMwMA+iTokJESBaVCvuexiYlwnBixqNyIg7kVMJos2HOsAsFaBYb5NyI6wjd6Y4iI5MbpwLN9+3bcfffdePrpp11WzNy5c7F582bMmTMHRqMRBw4caFmWmpoKjUaDRx55BE8++SR69OiBzMxM5OTk4NChQ/j0009dVgd1nsVqx48HSlBe7Zj5ltG3G/olRUhclWcIgoAeMcGIiwrEiTM1OJpfBYPJjk37SpEcG4LBfSKh9ZNXDxcRkbdz+lPX398fcXFxrqwF27dvBwC8+uqrbZZt3LgR8fHxmDp1KkwmExYuXIgPP/wQycnJmD9/PjIyMlxaCzmvyWzFll9KUF3XCJVSQP94f/SK63wPoK9RKhRITY5AfGQA9v5ainK9FadL61BUbsCAnhFISQyXdW8XEZE3cTrw/OlPf8IPP/yAO++802XFtHfm1/Tp0y95WQuSjqnJik17i1BXb4afWomrBsfA2lgndVmS0vqp0C9ei9SewTiUV42qukYczK3E6dI6DE+NRmRY1x7ESETkCU4Hnuuuuw579uzBfffdh1tvvRUxMTEtM0fON2DAgE4VSL6j0fx72AnwU2H80HhoNQpUNEpdmXeICPXHNZk9cLq0DgdOVqCu3owf9hShV3wo0vtE8lIVRERu5HTgaT4BIQDs2LGjzXJnZ2mRb2qy2LB5XzHq6s3Q+qkwYVgCggM0Xn8WYE8TBAE940IRFxWEgycrcKqkFqeKa1FyzoghKVHoERPcoWmWRETUPk4HnldeecWVdZAPM1ts2LKvGHpDE/w1SkwY6gg7dHF+aiWGD4hBUvcQ7DlWjrp6M3YcPov80loM6x+NIO4/IiKXcjrw3HTTTa6sg3yUxWrHll+KUV3XCD+1I+yEBPLLur2iwgMwaWQijp2uxtHT1SirakDOjgKk9emGlB5h7O0hInIRz53Tn2THZhfx44ESVNU2QqNSYPzQeIQG+Uldls9RKhQY2KsbJo9MQlRYAGx2EftPVOCHPY7xUERE1HlO9/D84x//uOw6giDgf/7nf5x9CvJioihi77FylFc3QKUUMO6KBIQF+0tdlk8LCdRgwtB4nCquxf6T51CpN2HdzgKk9e6GvolhULC3h4jIaU4Hnl27drW5z263o6KiAjabDeHh4V3i+jhd1a+nq5FfUgsBwKi0WESEMuy4giAI6J2gQ/dugdh1tAzl1Q3Yf7ICReUGZA7szsOFREROcvnV0i0WC1auXIlly5ZhyZIlThdG3uvM2TocyqsEAFzRLwqxkZ6/CKjcBWrVGH9FPPJLavHLiQpU1jZi7c4CpPXqhpSkMKnLIyLyOS4fw6NWq3HXXXdh1KhR+Pe//+3qzZPEzlbW4+ejZQCAlMQw9OnBL193EQQBveJ1uP7KJMREBMBuF3EgtwI/7C5Epd4kdXlERD7FbRf06devH7777jt3bZ4kUF7dgLU7C2C3i4iPCkJ630ipS+oSArVqjBsSj/zSOuw/cQ5VtY1YuvpXiAA0Ks47ICJqD7d9Wu7YsYNjeGSkyWLDK8t2o9FsQ3iIP0YO7M5BtB4kCAJ6xYXi+iuT0D0iEDa7iMWrjuK7H/NRb+LJHYmILsfpHp758+df8H6DwYA9e/bg119/xYMPPuh0YeRd/vPNIZwqroW/Romr0mOhYs+CJAL81Rg7JA41hiZs+aUYpRX1WFtTgCv6RSGpewjP20NEdBEuDzyhoaFISEjA3LlzccsttzhdGHmP738+gw27C6EQgGszeyDQXy11SV2aIAjI6BuJP4/rjRc+3Iny6gb8fKQMJeeMGJYaDT+N245UExH5LKc/GY8fP+7KOshLnSyswQffHAIA3DW5P4K0atQYmiSuigAgNjIIN43thR2Hz+LwqUoUnTOiQm9C5oAYzpwjIvoDHpegi6o1NuHVj/fAarMjc0AMbh7fR+qS6A8UCgEDekbg2sxEhARq0Gi2Yev+Euz5tQwWq13q8oiIvEan+753796NLVu2oLS0FAAQGxuLcePGYfjw4Z0ujqRjt4t4a8UvqKgxoXtEIB6/fQgUCo4P8VbhIf64bkQiDuVW4kRhDfKKa1FW3YArB3VHRCgnDxAROR14zGYz/v73v+OHH36AKIoICQkBANTV1WHp0qW45ppr8Oabb0Kt5ngPX5T9Uz72HT8HjUqBf9wzDEFa/h69nUqpwJB+UYiNDMSuI2UwNliwYXchBvXqhv7J4ZxVR0RdmtOHtN577z1s2LAB9957L3766Sfs3r0bu3fvxvbt2/GXv/wF69evx3vvvefKWslDTpfW4qPVvwIA/nLDACTHhkpcEXVETEQgJl+ZhB7RwRBF4FBeJTbvLUJ9I6evE1HX5XTgyc7Oxk033YSnn34a3bp1a7k/IiICTz31FG688UasWrXKJUWS5zRZbHj9032w2uwYlhqN60clS10SOUGjVuLKtO7IHBADlVLAuRoT1u0oQGGZQerSiIgk4XTgqaioQFpa2kWXp6WloaKiwtnNk0SWrDqConIDwoL98NitGTyviw8TBAE940IxaUQSwkP8Ybbasf1QKXYd5YBmIup6nA48MTEx2L1790WX79mzBzExMc5uniSw68hZ5OwoAADMvn0IQoP8pC2IXCI4UINrhvdAanI4ACC/pBbf/1yA6rpGiSsjIvIcpwPPjTfeiLVr1+KFF15Afn4+bDYb7HY78vPz8a9//Qvr1q3DTTfd5MpayY1q6hrx7hcHAAA3ju2FjJQoaQsil1IoBAzuE4kJQxOg9VPB0GDBhl1n8OvpKoiiKHV5JHOCIECr1bLHuAsTBEHySUxOz9KaOXMmioqK8MUXX+DLL7+EQuHITna7HaIo4qabbsLMmTNdVii5jyiKmP/lQdTVm9EzNhQzru8vdUnkJtHhAZh8ZRL2HC1D0TkjDuZWoqyqASMGxiCAZ9AmOD4PXB1MtFotUlNTXbrNy3FHO6Qgl3ZotVoMGDAAZrNZshqcDjxKpRKvvvoq7rnnHmzbtg0lJSUAgLi4OIwZMwb9+vVzWZHkXpv3FWP3r2VQKRV44o4hUKuUUpdEbuSnVmLU4Fjkl9Ri3/FzKK9uwNqdBcgcEIP4qGCpyyOJCYKAHYdKUWt03RnVrVYravQ1CNOFQaVy/6VPQoP8cGVarNufxxPc8fuQQqBWhTEZCZLW0KFXXlNTE/77v/8bffr0QVZWFgCgX79+bcLNxx9/jM8//xz//Oc/Je/CokurqjXhw/87DAC4/doUJHYPkbgi8gRBENArXofIsADsOFSKGkMTfjxQir49dEjvGwmlgidh78pqjU0uvYSMxWJBRZURokLL7wQnuPr3IQWr1Sp1CR0bw7Ny5Up8++23GDdu3CXXGzduHL7++mt8+eWXnamN3Kz5UFa9yYLeCTrcPL631CWRh4UEanBNZiJSEsMAACcL9diwqxCGeum6nYmI3KFDgWft2rW49tprkZBw6W6pHj16YNKkSVizZk2niiP32rS3CHuPlUOlVGD2bRlQKvlXfVekVAgYkhKFMRlx0KiVqDE0Yd3PBSg4Wyd1aURELtOhb7iTJ0/iiiuuaNe6GRkZOHHihFNFkftV1Zqw8LdDWXdcl4IeMTyU1dXFRQZh8shERIVpYbWJ2Hn4LH4+chZWnrOHiGSgQ4HHYrG0+/irWq2WdDQ2XVzLoaxGK/r20OHP43goixwC/NUYPzQBA3tGQABwurQO3+86A72Pjx8gIupQ4ImKikJubm671s3NzUVUFM/l4o1+OlDacijrsVt5KItaUwgCBvXuhvG/nbOnrt6M9bvOIK9Iz3P2EJHP6tA33ZVXXonvvvsOVVVVl1yvqqoK3333Ha688spOFUeuZ2wwt8zKumViXx7KoouKDg/ApJGJiO0WCJtdxJ5j5dh+6CzMFpvUpRERdViHAs8DDzyApqYm3H333Th48OAF1zl48CDuueceNDU14f7773dJkeQ6S1f/Cr2xCQnRQfivCTyURZfmr1FhTEYcMvpGQiEAReUGrNt5BpV6k9SlERF1SIfOw5OQkIC3334bTzzxBG677TYkJCSgb9++CAwMRH19PXJzc1FYWAh/f3/87//+L3r06OGuuskJh09VYv2uMwCAh/8rnScYpHYRBAH9ksIRGabF9kNnUW+y4Ic9hRjcOxL9ksJkcRZYIpK/Dp/ycty4cVi1ahUWLlyILVu24IcffmhZFhUVhenTp+OBBx647NR18iyzxYb3vnT0yk0amYQBPSMkroh8TUSoFpNGJGLPr+UoLDfgQG4Fymscl6Xw17j/7LlERJ3h1KdUfHw85s6dCwAwGo2or69HYGAggoKCXFocuc6XG3NRUmFEWLAf7p7i2WvakHxo1EpcmdYd0SUB+OX4OZytrMe6nWdw5aDuiAoPkLo8IqKL6vT0nKCgIERHRzPseLGicgO+2nQSAPDQTWkI0vLU7uQ8QRDQO16HazMTERKoganJik17i3DkVCXsnMVFRF6K85FlThRFfPDNIVhtIoalRuPKtO5Sl0QyoQv2w3WZiUiODYEI4PCpKmzZVwxTk/TXzCEi+iOvOvB+5swZLF68GAcPHkRubi569uyJ1atXt1onKysLu3fvbvPYnJwc9OrVy1Ol+owfD5TgUF4lNCoFHropjQNMyaVUKgVGDOyO6PAA7D1W3nLl9ZGDuqN7RKDU5RERtfCqwJObm4utW7di8ODBsNvtFz3J2ZAhQ/DMM8+0ui8+Pt4TJfqUhkYLFq86AsBxzp1ojrEgN0mODUVEqBbbD5ZCb2zCln3FSE0Ox6Be3aQujYgIgJcFngkTJmDixIkAgDlz5uDIkSMXXC8kJATp6ekerMw3rVh/AtV1TejeLRA38fIR5GaOK6/3wP4T55BXXItfT1ejosaEYf0jpS6NiMi7xvAoFF5Vjk87c7YOq37MBwA8eOMgaNQ85w65n0qpwLDUGIxK6w61SoEKvQk/7ClGlYHjeohIWl7Vw9Neu3fvRnp6Omw2GwYPHozHHnsMw4YN69Q2RVFEQ0ODiyr8nclkavXTE0RRxPtfHYDdLmJY/0ikJgZ3um2CIECr1cJqtcJisbT7cc3rduQx7mSzOS6L0NF2dIY79oEU7eiI7hFaXD00DruOlqPGYMaRMyaYrBUY3LsbFIrfx5FZrY4gbjKZZHudLik+A5zl7Pv8cjz9OeBtrytnXwPu+n1Iwap2/B6amppc+jsRRbHdY1N9LvAMGzYM06ZNQ1JSEs6dO4fFixfj3nvvxSeffIKMjAynt2uxWHDs2DEXVtpaQUGB27b9R4cKGvBrQQ1USgFX9lG6pF1arRapqamo0degosrY4cfr9fpO1+AKEcGON4bBaEBFhd6jz+3KfSBlOzpiYIIG+eUiSqosOFViwLnqevRP0EKrcfTmCnbH6SxOnz7tE4GgMzz5GeCszr7PL8dTnwPe+rrq6GvA3b8PTxIiHL+T0tJSl/9ONBpNu9bzucDz6KOPtro9btw4TJ06Fe+//z4WLlzo9HbVajV693b9OBeTyYSCggIkJSVBq9W6fPt/1NBoxdurtgMAbh7XE1cO6+mS7TYn6DBdGERF+9thsVig1+uh0+mgVkt//p+QYMfFUoODghFp90w97tgHUrTDWeHhFugKK3Cy1AyDyY79+SYM7ReJuMhAhIX4AwCSk5O94i9xd/D0Z0BnOPs+vxxPfw542+vK2deAu34fUggOcMSN2NjYdgeU9sjLy2v3uj4XeP4oICAAY8eOxffff9+p7QiCgIAA981i0mq1bt1+s882HIHeaEb3boG49dr+Lr9elkqlcuoDS61We0XgUSod+8PZdnSGK/eBlO1wRrcQNXrERWLPrxWorG3EziPl6JOgw/grHLMrvT0IuIKnPgNcwV2vK099DqhUjq82b3tdOfsa8JX3+aWofvsu8vPzc+nvpSOnWuEoYRkpOFuH7J8cA5UfumkQLw5KXiXQX42rh/VA/6RwAEBukR7fbMlDaYVvd9UTkW/w+cDT0NCALVu2YNCgQVKXIqnmMyrb7SJGDuqOK/pFS10SURsKhYD0vpEYOyQefmolKvWNePytLdj6S7HUpRGRzHnVIS2TyYStW7cCAEpKSmA0GrFu3ToAwPDhw5Gfn49FixbhmmuuQVxcHM6dO4elS5eioqIC77zzjpSlS+7HAyU4ml8FjVqJ+6cNlLocokuK7RaISSMdV14vrazHG8v34VBeJR64cSCvvE5EbuFVnyxVVVV47LHHWt3XfPvjjz9GTEwMLBYL3nrrLej1emi1WmRkZGDu3LlIS0uTomSv0GSxYenqXwEA/zWhD6LCfGOcAHVtAf5q/Gl0T+iNZqz84QTW7zqD42eq8UzWUPSICZG6PCKSGa8KPPHx8Thx4sQl11m8eLGHqvEd/7clD5V6E7rptLhpHK8nRr5DoRBw56R+GNgzAm9+tg+FZQbMfnsb/vrnQbh6WA9e+42IXMbnx/B0dVW1Jny5KRcAcM+UVB4OIJ80uG8k3vn7OKT3jYTZYsM7Kw/grRW/8MrrROQyDDw+7uOcY2gy29AvMQxjMuKkLofIaWHB/pj7wEhkTe4PhQBs3leM2W9twenSWqlLIyIZYODxYScLa7BpbxEA4IEbB7H7n3yeQiHglol98T9/uwoRof4oqajH39/Zhu+2nYLdLv0J5IjIdzHw+ChRFLHoO8fV5MdfEY++PcIkrojIdQb0jMA7T4zDsNRoWKx2LPruCP714U5U1XrPZQKIyLcw8Pionw6U4lhBNfw0Ssy4PlXqcohcLjTID8//JRN/uzkNGrUSB3IrMOv1zdh+sFTq0ojIBzHw+KAmiw1L1xwFANw8vg+66bzr9OlEriIIAiZfmYx3nhiL3vGhMJosePXjPXj781/Q0OjbV48mIs9i4PFB/7c1DxU1JnQL9ec0dOoS4qOC8dojY3DLxL5QCMDGPUV49M0t+PV0ldSlEZGPYODxMVW1Jny10TEN/e6pAzgNnboMtUqBrMn98T9/uwpRYVqUVzfgH+/9hE/XHoPVZpe6PCLycgw8PuaTtcfQaLYhJTEMYzkNnbqgAT0j8O7fx2PC0ATYRWDlDyfx1LwfUVhWJ3VpROTFGHh8SF6RHhv3/DYNfdpATkOnLitQq8bs24fg6ayhCNKqkVekx2P/uxVf/HCSvT1EdEEMPD5CFEUs/O4wAGDcFfFISQyXuCIi6Y1Oj8P8p8ZjaP9oWG12fLL2GJ58dxtPVkhEbTDw+IifDpbi19PV0KiVuJvT0IlaRIRq8cJ9mZh9+xAEadU4VVyLJ97eihXfH4fFyt4eInJg4PEBTRYbPlrtmIb+X+N7cxo60R8IgoAJQxPw3tMTMGJgDKw2EZ+tP4En3t6KvGK91OVRFycIArRaLYchSIyBxwd8t/UUzjVPQx/fW+pyiLxWeIg/nr1nOJ666woEB2hQcLYOf397Kz78v8OoN/G8PV2Fv0YJUfSeS5FotVqkpqZCq+Ufq1LinGYvV13XiC83ngQA3M2roRNdliAIGJMRj7TekVj4f4ex7UAJsn/Mx08HSnDfnwZiTEYc/9KWOY1aCUEQsONQKWqNTVKXA6vVihp9DcJ0YVCp2v8ZHhsZhMF9It1YWdfCb08v90nOb9PQe4RhTEa81OUQ+QxdsB+eyhqKicN74INvDqG0sh5vLN+H9bvOYOaf05AQHSx1ieRmtcYm1BikDzwWiwUVVUaICi3UanW7HxcSqHFjVV0PD2l5sbwiPTbuLQQA3H/jQCgU/KuUqKMyUqIw/6nxuGtSP2hUChzKq8Sjb27Gxzm/orHJKnV5ROQhDDxeShRFLFp1BKIIjBsSj36chk7kNLVKiVuvScF7T0/4bQq7iC835uKv/28jNu0thN3uPeM9iMg9GHi81I5DZ3E0vwoaNa+GTuQqMRGBeOG+TPzz3uGICtOisrYRb63Yj9lvbcXB3AqpyyMiN+IYHi9kttiwZHXz1dB7IzKMI/uJXEUQBIwY2B1DUqKQ/WM+vth4EvmltXjugx0Y2j8aWZP7o2dcqNRlEpGLMfB4oe+2ncK56gZEhPrjz+M4DZ3IHTRqJW6e0AcTh/fA5xtOYO2OAuw9Vo69x8pxZVp33HFdPyTGhEhdJhG5CAOPl2kzDd2PvyIidwoN8sNDN6Vh6lU98dm64/jxYAl2HDqLnYfPYvTgONx2bQpndBHJAL9Nvcyna4/B1GRD3x46jOU0dCKPiYsMwlNZQ3HLxL74bP1x7Dh0FtsOlGDbgRIMT43Bn8f3RmpyOM/hQ+SjGHi8SF6xHj/scUxDf+DGQZyGTiSBxO4h+Mfdw5FfUovPN5zAz0fOYvevZdj9axlSEsPw53G9kTmwO5R8fxL5FAYeLyGKIhZ955iGPjaD09CJpNYzLhTP3jMcxecM+L+tp7BpbxFOnKnBK8v2oFuoP64dkYRrM3sgIpSTCoh8AQOPl9hx+Pdp6HdP4TR0Im8RHxWMWdPTceekfsj+MR/rdp5BZW0jPvv+OD7fcALD+kfj2hGJyOgbBbWKZ/og8lYMPF7AbLFhabZjGvqfx3EaOpE3Cgv2x4zrU3HbNSnYcfgs1u0swNH8Kuw6WoZdR8sQHKDGlWmxGJMRhwE9u/GQF5GXYeDxAqt+zEd5dQPCQ/xxM6+GTuTVNGolxg2Jx7gh8Sgsq8P3u85g2/4S6A1N+P7nM/j+5zMID/HDsNQYDOsfjT5xgVKX7JXsdhEWqw1mix3m5p8WG8xWO6w2O2x2EbY//rSLsNlE2Ox22O0A4DhDtvj7f0IE4KdW4Ltt+dAbm2C3i1AoBCgEwfFTASgEAUpF820BaqUCSqUCaqUCKpUCKqUAlVLx+z+VAhqVAmqVgoPWfRgDj8Rq6hrxxQ+chk7ki3rEhOCBaYPwlxsG4kheJbbuL8aOw2dRXfd7+FEpBSRGajCyWouMlO7oGRcKlVJ+h75EUYTZakdjkxWmJisazTY0mq1obLKhodGMOqMJ9oJiWKyOYGO1ufdyHiUV9S7fpgBArVZAo1JCo1ZC0/LfCqjVSmhUCviplfDXqODv99tPjdLldZBz+O0qsU/WHoOpyYo+CTqMG8Jp6ES+SKkQMLhvJAb3jcRfb07DwdxK7D1Wjj3HynGuugGnyppwal0uPl2XC41aiX6JYeifFI5e8aFI6h6K6PAAr52VaRdFNJltMP0WZM7/5wg3NjQ2WdFotuLylySztblHpVS0Dg4qJVQqASqFAkqlAKVCAaVC+P2/lb/1zggC4PgfWv7/t10YGaZF/6QI7Dtejrp6M+x2EXZRbP3zt3/W33qQLDY7bDYRFqu99W2bHVaro4dJBH7ribIDJku796FKKUCtBAKLLND6q+CvUSHAX4UAfzUCf/up9VN57WtALhh4JJRX9Ps09Ac5DZ1IFtQqJYb2j8bQ/tF4SBSRV1iJdT8eRVWDGieLamFosOBQXiUO5VW2PMZfo0Ri9xAkRAUjOiIA0eEBiApz/AwN8nP5YGirzQ5Dgxl19ef9MzahxtCE6rpGHC+ohqHB/FuosaEjfTEalQL+fo6eDX8/FbQaFdQqAZamBnSL0CFQq2kVbtzxuZcYE4yRg7pDb2hEjaHJJdu02hxBx3EY7kKH4n7/7+aereYQaLWJsNoAk7kRqL3w9gUAWn8VAv3VCApQIyRQg8YmK+KjgmG12V3Shq6OgUcioijiw/87/PvV0JM4DZ1IbgRBQFxkIK7sH4z+/fvD31+L4nMG/Hq6GsfPVKPgbB0KywxoNNtw4kwNTpypueB2AvxVCAnUICRQg0B/9W+HU34/pAIBrcaw2Gz2lkNKTc2Hlsw2NJptqDdZUN+B3gnAsfnmQzRaPxW0/o4g09xbofVTtoQcpaJtOLNYLKiosCAyIgBqtbpjO9FLNI/n6cjXpig6eowM9Y0or6iCf0CwI/g0WdHQaEVDowX1jVaYGi2wi/jtPisq9CYAwMHcSny79RQAx2sgNNAPuhA/hAX7ISzYH0EBakdPF7ULA49EfjxQgmMF1fDTcBo6UVehUAjoEROCHjEhmDQyCYAjnJRW1qOgtA4llUacq25A+W//KvQm2O1iyxdhWVWDy2oRBCBIq2kJUiGBGuiC/RAe4o/SCiNE0dHjoPVTwU+j5BerEwRBgEatREigBk0NKkRGBl0w8Imi2BJGjSYLjA1m1DWYYbbYUWdsQn2jteU1cLbq97FJSoUAXbAjAIWHahGp0yI4QM2B1RfBwCOBRrO1ZRr69Al90E3HaehEXZVSqUBCdPAFr9dlt4uob7T8dsjJjNr6JjQ0WtBkscNisaHJYoPFaocoOgKMAAC/zUA6f9Csv8YRWvw1SgT4Ow6XBAVoLjp1fu2O0y47FESXJwiCo+fMT9Xq+yAxJhhXpsXi2y15KCo3oNZoRs1vh+n0hibY7CKqahtRVdsIFDuOlfmpleim0yIyTIuYiADogvwYgH7DwCOBbzfnobK2EVFhWtzIq6ET0UUoFAKCAzQIDtAgLlLqakgKzWEoMiwAkWEBLffbRRHGBjNq6ppQbWhElb4RVXWNaLLYUFJhREmFEQDgp1EiJiIA3SMCERMRCG0XngncdVsukXM1Dfhqcx4A4N4bBsBPzSmLRETUMQpBQEigH0IC/ZDYPQQAYLPbUV3XhMoaE87VOA6LNpltOHPWgDNnDQCASJ0W8dFBSIgKRqDWN8dTOYuBx8OWrf4VZosNA3pGYFRarNTlEBGRTCgVCkTqHGN5+ieHw2a3o1LfiLKqepytrEeNoQkVehMq9CbsP1GBsGA/JHUPQWL3kC7R8+NVLTxz5gwWL16MgwcPIjc3Fz179sTq1avbrPfll19i0aJFKC0tRXJyMmbPno3x48dLUHHHHM2vwrYDJRAExzR0HlclIiJ3USoUiA53nN5gcJ9I1JssKD5nRPE5AypqTKgxNKHGUIEDuRXoHhGI5NgQxEUGQSnDE2MCXhZ4cnNzsXXrVgwePBh2ux2i2PbsD2vWrMHzzz+PmTNnYsSIEcjJycGsWbOwfPlypKene77odrLbRSz87jAA4NrMRPSMC5W4IiIi6koCtWqkJIYhJTEMjWYrisoNOF1ah6raRpRW1qO0sh4atRK94kLRJ0Enu0NeXhV4JkyYgIkTJwIA5syZgyNHjrRZ591338WUKVPw+OOPAwBGjBiBkydP4r333sPChQs9WW6HbN1filPFtQjwV+GuSf2lLoeIiLowf40KfRLC0CchDHX1ZpwurUVBaR0amqw4VlCN4wXViIsKQt8eYYgK08riiIRX9VspLnDCqvMVFRWhoKAAkydPbnX/9ddfj507d8JsNruzPKc1WuxYscExUPn2a1OgC/aTuCIiIiKHkEANBveJxA1jemJ0eiyiwgIgAig+Z8SmvUVYv6sQxeeMFzzq4ku8qofncvLz8wEAycnJre7v1asXLBYLioqK0KtXLylKu6RtR+pQW29GXGQgpozqKXU5REREbSgEAfFRwYiPCkatsQknC2twurQO1XWN+PFACXRBfhjQMwLx0UE+eSJKnwo8tbWOEyuFhIS0ur/5dvNyZ4iiiIYG153FtNnpkmr8fMJxPoSsSX1gMTfC4p0dUZckCAK0Wi2sVisslvaflr553Y48xp1sNsfFCzvajs5wxz6Qoh3OulT7rVbHaRlMJpPP//V4sS5/s9kMrVYLs9ns9YcFBEGAv7+/y19Xnv4c8Lb3h7Ptl7IdAX4KpPeJQL9EHXKLanGqpBZ6YxO2HypFSIAaA3uFo3tEQLtf01a14/3d1NTk0ve6KIrtrsGnAo87WSwWHDt2zOXbXbG1EnY70Lu7H/xtlTh2rPLyD/JCWq0WqampqNHXoKLK2OHH6/V61xflhIhgxxvDYDSgokLv0ed25T6Qsh3OulD7BXsQAOD06dMwmUwersh11Go1BgwYAKWy7Xm1tFotdDqd54vqhLq6OlRUOP8H5MV46nPAW98fHW2/t7QjJgSICAhESbUZJVVm1DVYsONwOUIDlOgZ44eQgMufT06IcLzXS0tLXf5e12g07VrPpwJPaKhjZpPBYEBk5O+nHa2rq2u13BlqtRq9e7v2rMf5JXU4UVIMhQD85YYB6JUQ4dLte1Jzgg7ThUFUtP9SGBaLBXq9HjqdzisuGhgS7OgNDA4KRqTdM/W4Yx9I0Q5nXar9YSH+AByHqX25h0cQBCiVSmzbX4RaQ2OrZVarFQajAcFBwVCpvPsjNy4qGEP6xSAkJARWtO9LpD08/Tngbe8PZ9vvbe2I7Q4MtthworAWucW1qG2wYX9+A+KjAjGoZ/glZ3UFBzhe+7Gxse0OKO2Rl5fX7nW9+933Bz17Osa/5Ofnt/x38221Wo2EhASnty0IAgICAi6/YgeE6ewIC/bDkJ5+6JUQ4fLtS0GlUjn1gaVWq70i8DT/Be5sOzrDlftAynY460Ltbw4AWq08ridXb7LCYLK1us9isaKiyghRoYVa7d2HtExNjtrd9bry1OeAt74/Otp+b2yHWq3GkH7+SEkKx+G8SpwurUPxuXqcrWzAgJ4R6JcUBuUFJiCpVI62+Pn5ufT93pHDxF41S+tyEhISkJSUhHXr1rW6PycnByNHjnRpanSFuMggfPD0GIweEHL5lYmIiHxEoL8aIwZ2x6SRiYgKC4DNLuJQXiXW7jiDsvOu6O5NvKqHx2QyYevWrQCAkpISGI3GlnAzfPhwhIeH45FHHsGTTz6JHj16IDMzEzk5OTh06BA+/fRTKUsnIiLqcsKC/TFhaDzOlBmw/8Q5GBrM2LyvGIkxwbiiXxT8NN4TM7ynEgBVVVV47LHHWt3XfPvjjz9GZmYmpk6dCpPJhIULF+LDDz9EcnIy5s+fj4yMDClKJiIi6tIEQUBS9xDEdgvE4VOVyC3U40yZAWXVDRjWPxoJ0cFSlwjAywJPfHw8Tpw4cdn1pk+fjunTp3ugIiIiImoPjVqJK/pFI6l7KHYdOYvaejN+OliKxO7BGJsu/cWyfWoMDxEREXm3iFB/XDcyEanJ4RAAnDlrwJebTiG/xPWnOugIBh4iIiJyKaVCgcF9InFNZg+EBGpgarLip4MlktbkVYe0iIiISD4iQrWYNCIRhvom3HpNCuxW6S41wB4eIiIichulUoGE6CD4qS9/RmZ3YuAhIiIi2WPgISIiItlj4CEiIiLZY+AhIiIi2WPgISIiItlj4CEiIiLZY+AhIiIi2WPgISIiItlj4PEAtVoNQRCkLoOIiKjL4qUl3EwQBAwYMABKpbRnmCQiIurKGHg8QKlUYtv+ItSbrFKX4rTYyCAM7hMpdRlEREROYeDxkFpDIwwmm9RlOC0kUCN1CURERE7jGB4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPZ8LPN988w1SUlLa/HvjjTekLo2IiIi8lErqApy1aNEiBAcHt9yOjo6WsBoiIiLyZj4beAYMGIDw8HCpyyAiIiIf4HOHtIiIiIg6ymd7eKZOnYqamhrExsbilltuwf333w+lUun09kRRRENDgwsrdDCbzdBqtbBarbBYrC7fvqfYbDYA+K0dlnY/rnndjjzGnZxtR2e4Yx9I0Q5nXar9VqvjPWsymSCKokfrciVBEM57n7dup7e9By7FXa8rT+8Db3t/ONt+b2tHZ1jVjvd3U1OTS9/roihCEIR2retzgScyMhKPPPIIBg8eDEEQsGnTJrz99tsoLy/HCy+84PR2LRYLjh075sJKHbRaLXQ6HQxGAyqqjC7fvqdEBDteUAajARUV+g4/Xq/v+GPcobPt6AxX7gMp2+GsC7VfsAcBAE6fPg2TyeThilxHq9UiNTUVNfqai77PveU9cCnufl15ah946/ujo+331nY4Q4hwvNdLS0td/l7XaDTtWs/nAs/o0aMxevTolttXXXUV/Pz8sGzZMsycORNRUVFObVetVqN3796uKrOF2WwGAAQHBUNUaF2+fU8JCQ4B4GhHpF3d7sdZLBbo9XrodDqo1e1/nLs4247OcMc+kKIdzrpU+8NC/AEAycnJPt/DAwBhurA273Nvew9cirteV57eB972/nC2/d7Wjs4IDnDEjdjY2HYHlPbIy8tr97o+F3guZPLkyViyZAmOHTvmdOARBAEBAQEuruz3D0KVSgW1un3dbt6o+XChox0df+Op1Wqv+LDvbDs6w5X7QMp2OOtC7VepHB9BWq3v/jFwvkv9PrzlPXAp7n5deWofeOv7o6Pt99Z2OEOlcrTFz8/Ppe/39h7OAjhomYiIiLoAWQSenJwcKJVKpKamSl0KEREReSGfO6R13333ITMzEykpKQCAjRs34osvvsCMGTMQGRkpcXVERETkjXwu8CQnJ+Prr79GWVkZ7HY7kpKS8OyzzyIrK0vq0oiIiMhL+Vzgee6556QugYiIiHyMLMbwEBEREV0KAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREckeAw8RERHJHgMPERERyR4DDxEREcmeTwaeU6dO4d5770V6ejpGjRqF1157DWazWeqyiIiIyEuppC6go2pra3H33XcjKSkJ8+bNQ3l5OV599VU0NjbihRdekLo8IiIi8kI+F3g+//xz1NfXY/78+dDpdAAAm82GuXPn4qGHHkJ0dLS0BRIREZHX8blDWtu2bcPIkSNbwg4ATJ48GXa7Hdu3b5euMCIiIvJagiiKotRFdMTIkSNx880348knn2x1/+jRozFt2rQ297fHL7/8AlEUoVarXVVmC1EUoVAo0Nhkhd23dnUrKqUCGrUSjWYr7PaOtcNut0Oh8I5s3Zl2dIar94FU7XDWxdqvUAjw16jgYx9DFyQIwkV/H970HrgUd76uPLkPvPH94Uz7vbEdzlIIAvz9VLDb7RAEwWXbtVgsEAQBQ4YMuey6PndIq66uDiEhIW3uDw0NRW1trVPbbN75rvwl/HHb/n4+t6svyF/DdngTubTDHe89Kcjl98F2eBe5tAOAy0OvIAjt/vyQz17shIyMDKlLICIiIjfy/j7WPwgJCYHBYGhzf21tLUJDQyWoiIiIiLydzwWenj17Ij8/v9V9BoMBFRUV6Nmzp0RVERERkTfzucAzZswY7NixA3V1dS33rVu3DgqFAqNGjZKwMiIiIvJWPjdLq7a2FlOmTEFycjIeeuihlhMP3nDDDTzxIBEREV2QzwUewHFpiX//+9/Yv38/AgMDMW3aNMyePRsajUbq0oiIiMgL+WTgISIiIuoInxvDQ0RERNRRDDxEREQkeww8REREJHsMPERERCR7DDxEREQkeww8REREJHsMPG5y6tQp3HvvvUhPT8eoUaPw2muvwWw2S12Wx5w5cwYvvPACpk2bhtTUVEydOlXqkjxq7dq1+Otf/4oxY8YgPT0d06ZNw1dffYWuchaIrVu34q677sKIESMwcOBAXH311XjllVcueB28rqC+vh5jxoxBSkoKDh8+LHU5HvHNN98gJSWlzb833nhD6tI86ttvv8WNN96IQYMGITMzE/fffz8aGxulLssjsrKyLvgaSElJwZo1azxeD6+W7ga1tbW4++67kZSUhHnz5rWcDbqxsbHLnA06NzcXW7duxeDBg2G327vMF32zjz76CHFxcZgzZw7CwsKwY8cOPP/88ygrK8OsWbOkLs/t9Ho90tLSkJWVBZ1Oh9zcXMybNw+5ublYsmSJ1OV53Pvvvw+bzSZ1GZJYtGgRgoODW25HR0dLWI1nLViwAAsXLsTMmTORnp6Ompoa7Ny5s8u8Fv71r3/BaDS2um/ZsmVYv349Ro4c6fmCRHK5Dz74QExPTxdrampa7vv888/F/v37i2VlZdIV5kE2m63lv5955hlxypQpElbjeVVVVW3ue+6558QhQ4a02jddycqVK8W+fft2mfdAs7y8PDE9PV1csWKF2LdvX/HQoUNSl+QRX3/9tdi3b98Lvhe6glOnTompqanili1bpC7Fq0yYMEF84IEHJHluHtJyg23btmHkyJHQ6XQt902ePBl2ux3bt2+XrjAPUii69ksrPDy8zX39+/eH0WhEQ0ODBBVJr/n9YLFYpC3Ew15++WXcdtttSE5OlroU8qBvvvkG8fHxGDt2rNSleI1ffvkFxcXFuOGGGyR5/q79reQm+fn56NmzZ6v7QkJCEBkZifz8fImqIqnt27cP0dHRCAoKkroUj7HZbGhqasLRo0fx3nvvYcKECYiPj5e6LI9Zt24dTp48iYcffljqUiQzdepU9O/fH1dffTX+85//dJnDOQcPHkTfvn3x/vvvY+TIkRg4cCBuu+02HDx4UOrSJLN69WoEBATg6quvluT5OYbHDerq6hASEtLm/tDQUNTW1kpQEUlt7969yMnJwTPPPCN1KR41fvx4lJeXAwBGjx6NN998U+KKPMdkMuHVV1/F7Nmzu1TIbRYZGYlHHnkEgwcPhiAI2LRpE95++22Ul5d3ibGMFRUVOHLkCE6ePIl//etf0Gq1+OCDD/CXv/wF69evR0REhNQlepTVasXatWsxYcIEBAQESFIDAw+Rm5WVlWH27NnIzMzEjBkzpC7Hoz788EOYTCbk5eVhwYIFmDlzJpYuXQqlUil1aW63YMECRERE4Oabb5a6FEmMHj0ao0ePbrl91VVXwc/PD8uWLcPMmTMRFRUlYXXuJ4oiGhoa8M4776Bfv34AgMGDB2PChAn49NNP8dhjj0lcoWdt374d1dXVks7Y5SEtNwgJCbng9Nva2lqEhoZKUBFJpa6uDg888AB0Oh3mzZvX5cY29evXDxkZGZg+fTref/997Nq1Cxs2bJC6LLcrKSnBkiVL8Oijj8JgMKCurq5l7FZDQwPq6+slrlAakydPhs1mw7Fjx6Quxe1CQkKg0+lawg7gGMeWmpqKvLw8CSuTxurVq6HT6XDVVVdJVgN7eNygZ8+ebcbqGAwGVFRUtBnbQ/LV2NiIhx56CAaDAStXrmw1NbcrSklJgVqtRmFhodSluF1xcTEsFgsefPDBNstmzJiBwYMH44svvpCgMvKU3r17X/S13tTU5OFqpNXY2IgffvgBf/rTn6BWqyWrg4HHDcaMGYMPPvig1ViedevWQaFQYNSoURJXR55gtVrx+OOPIz8/H8uXL+9S5x65mIMHD8JisXSJQcv9+/fHxx9/3Oq+Y8eO4ZVXXsHcuXMxaNAgiSqTVk5ODpRKJVJTU6Uuxe3Gjx+Pb775BseOHUP//v0BADU1NTh69CjuueceaYvzsE2bNqGhoUGy2VnNGHjc4LbbbsMnn3yChx9+GA899BDKy8vx2muv4bbbbusyX3wmkwlbt24F4OjeNxqNWLduHQBg+PDhF5y2LSdz587F5s2bMWfOHBiNRhw4cKBlWWpqKjQajXTFecCsWbMwcOBApKSkwN/fH8ePH8fixYuRkpKCiRMnSl2e24WEhCAzM/OCywYMGIABAwZ4uCLPu++++5CZmYmUlBQAwMaNG/HFF19gxowZiIyMlLg695s4cSIGDRqERx99FLNnz4afnx8+/PBDaDQa3HHHHVKX51HZ2dmIjY3FFVdcIWkdgih2sVPgesipU6fw73//G/v370dgYCCmTZuG2bNny/6LrllxcfFFpx5+/PHHF/0ykIsJEyagpKTkgss2btwo+16ODz/8EDk5OSgsLIQoioiLi8M111yD++67r0vOWAKAXbt2YcaMGfjqq6+6RA/Pyy+/jB9//BFlZWWw2+1ISkrC9OnTkZWVBUEQpC7PI6qrq/HKK69g8+bNsFgsGDp0KP7xj3+gd+/eUpfmMbW1tRg1ahTuvvtuPPXUU5LWwsBDREREste1powQERFRl8TAQ0RERLLHwENERESyx8BDREREssfAQ0RERLLHwENERESyx8BDREREssfAQ0RERLLHwENERESyx2tpEZGkioqKsHTpUmzfvh1lZWUAgLi4OGRmZuLWW29Fv379AADz5s3D/PnzWx6nUqkQHR2NCRMm4NFHH225UO8fvfbaa1i8eDEmT56Mt99+u83yP14GRaVSISgoCMnJyRg+fDhuu+02xMbGtnpM82UiLuZ///d/MWXKlHbvAyJyPwYeIpLM5s2bMXv2bCiVStxwww3o168fFAoF8vPzsX79eqxYsQIbN25EXFxcy2NefPFFBAQEwGQyYefOnfjkk09w9OhRrFixos32RVHEmjVrEBcXh82bN8NoNF70Wl5Tp07FmDFjIIoiamtrcfjwYSxbtgwff/wx/vu///uCASYrK+uC18VKT093fqcQkVsw8BCRJAoLC/HEE08gNjYWH330EaKiolotf/LJJ/HZZ59BoWh95P26665DeHg4AOC2227D7NmzkZOTg0OHDiEtLa3Vurt27UJZWRmWLVuG+++/Hxs2bMBNN910wXpSU1Mxbdq0VveVlJTgL3/5C5555hn06tWrpbep2dChQzFp0iSn2k9EnsUxPEQkiUWLFqGhoQGvvPJKm7ADOA4tzZgxA927d7/kdoYOHQrAEaD+KDs7G71798aIESMwcuRIZGdnd6jGuLg4vPrqq7BYLFi4cGGHHktE3oWBh4gksXnzZiQmJmLw4MGd2k5xcTEAtBnDYzabsX79+pZDUVOmTMHPP/+MioqKDm0/IyMDPXr0wI4dO9osq6+vR3V1dZt/oig62Roichce0iIijzMajTh37hwmTpzYZlldXR2sVmvL7YCAAPj7+7fcrq2tBQCYTCb8/PPP+OyzzxAeHo5hw4a12s7mzZtRV1fXEngmTpyIF154AWvWrME999zToXr79OmDjRs3thkD9Oyzz15w/Z9++gmRkZEdeg4ici8GHiLyOKPRCMARZv4oKysLx48fb7n99NNP47777mu5/ccxM3379sUrr7wCrVbb6v7s7GwMHDgQiYmJAICgoCCMGzcO2dnZHQ48zXXW19e3CjwPP/xwyyG184WGhnZo+0Tkfgw8RORxgYGBAICGhoY2y1566SXU19ejsrISTz31VJvl8+bNQ1BQEKqrq/HJJ5+guLi4VQ8Q4Ogl2rp1K+666y6cOXOm5f4hQ4bg+++/x+nTp5GcnNzuepvrbK67Wd++fXHllVe2eztEJB0GHiLyuODgYERGRiI3N7fNsuYxPc1jc/5o6NChLbO0xo8fjxtuuAFPPvkkvvnmm5YZXevWrYPZbMaSJUuwZMmSNtvIzs7Go48+2u56c3NzERERcdEp7UTk/Rh4iEgS48aNw5dffnnB6eTtFRgYiFmzZuEf//gH1q5d2zJeJzs7G3379sXDDz/c5jErV67E6tWr2x149u/fj8LCQvzpT39yqkYi8g6cpUVEkrj//vuh1Wrx7LPPorKyss3y9s50uuGGGxATE9Mybfzs2bPYs2cPJk2adMF/f/7zn3HmzBkcPHjwstsuKSnBnDlzoFarW40jIiLfwx4eIpJEUlIS3njjDfz973/HpEmTWs60LIoiiouLsXr1aigUCsTExFxyO2q1GjNmzMBrr72Gbdu24fjx4xBFsdXlIs43duxYqFQqZGdnt5oS/+uvv+K7776DKIqoq6vD4cOHsX79egiCgNdee63NSQcBYO/evWhqampzf0pKygXXJyLpCCJPGEFEEiosLMSSJUuwY8cOlJWVQRAExMbGYvjw4bj99tvbXEtr586dLWN4mhmNRowbNw79+/eHXq+H0WjE5s2bL/qcWVlZOHXqFLZt24aysrILXksrMTERI0aMcOpaWrNmzcIjjzzizO4gIjdh4CEiIiLZ4xgeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSPQYeIiIikj0GHiIiIpI9Bh4iIiKSvf8PM/FcjexuEQEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "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": [
        "# Bar graph of Weekly study hours vs GPA\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.scatterplot(data=df, x='Weekly study hours', y='Cumulative grade point average in the last semester (/4.00)')\n",
        "plt.title('Weekly Study Hours vs GPA')\n",
        "plt.xlabel('Weekly Study Hours')\n",
        "plt.ylabel('GPA')\n",
        "\n",
        "# Set x-axis ticks to display integer values only\n",
        "plt.xticks(range(df['Weekly study hours'].astype(int).min(), df['Weekly study hours'].astype(int).max() + 1))\n",
        "\n",
        "# Set y-axis ticks to display integer values only\n",
        "plt.yticks(range(int(df['Cumulative grade point average in the last semester (/4.00)'].min()), int(df['Cumulative grade point average in the last semester (/4.00)'].max()) + 1))\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "vc7b-mWhnz_x",
        "outputId": "2dc66f81-9547-487d-c2ab-b76542d40565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar plot of average GPA vs attendance to classes\n",
        "df['Attendance to classes'] = df['Attendance to classes'].replace({1: 'Yes', 2: 'No'})\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(data=df, x='Attendance to classes', y='Cumulative grade point average in the last semester (/4.00)')\n",
        "plt.title('Average GPA by Attendance to Classes')\n",
        "plt.xlabel('Attendance to Classes')\n",
        "plt.ylabel('Average GPA')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "0N3Lywsen2fw",
        "outputId": "6f4760f5-c43d-4dc1-ce88-00596ead6314"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of Preparation to midterm exams 1 vs GPA\n",
        "# Define the mapping of numerical values to categorical labels\n",
        "preparation_labels = {\n",
        "    1: 'Alone',\n",
        "    2: 'With Friends',\n",
        "    3: 'Not Applicable'\n",
        "}\n",
        "\n",
        "# Map numerical values to categorical labels in the DataFrame\n",
        "df['Preparation to midterm exams 1'] = df['Preparation to midterm exams 1'].map(preparation_labels)\n",
        "\n",
        "# Scatter plot of Preparation to midterm exams 1 vs GPA with categorical labels\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data=df, x='Preparation to midterm exams 1', y='Cumulative grade point average in the last semester (/4.00)')\n",
        "plt.title('Preparation to Midterm Exams 1 vs GPA')\n",
        "plt.xlabel('Preparation to Midterm Exams 1')\n",
        "plt.ylabel('GPA')\n",
        "\n",
        "# Set y-axis ticks to display integer values only\n",
        "plt.yticks(range(int(df['Cumulative grade point average in the last semester (/4.00)'].min()), int(df['Cumulative grade point average in the last semester (/4.00)'].max()) + 1))\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "wo_iGsBTn49a",
        "outputId": "4654aad3-f95f-4ee7-fcea-1425a6067f81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of Preparation to midterm exams 2 vs GPA\n",
        "# Define the mapping of numerical values to categorical labels\n",
        "preparation_labels_2 = {\n",
        "    1: 'Closest Date to Exam',\n",
        "    2: 'Regularly During Semester',\n",
        "    3: 'Never'\n",
        "}\n",
        "\n",
        "# Map numerical values to categorical labels in the DataFrame\n",
        "df['Preparation to midterm exams 2'] = df['Preparation to midterm exams 2'].map(preparation_labels_2)\n",
        "\n",
        "# Scatter plot of Preparation to midterm exams 2 vs GPA with categorical labels\n",
        "plt.figure(figsize=(10, 3))\n",
        "sns.scatterplot(data=df, x='Preparation to midterm exams 2', y='Cumulative grade point average in the last semester (/4.00)')\n",
        "plt.title('Preparation to Midterm Exams 2 vs GPA')\n",
        "plt.xlabel('Preparation to Midterm Exams 2')\n",
        "plt.ylabel('GPA')\n",
        "\n",
        "# Set y-axis ticks to display integer values only\n",
        "plt.yticks(range(int(df['Cumulative grade point average in the last semester (/4.00)'].min()), int(df['Cumulative grade point average in the last semester (/4.00)'].max()) + 1))\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "mNYyLwEkn7iA",
        "outputId": "6b5b43a0-ebda-4eeb-80fa-be469a0c1afc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the DataFrame by 'Attendance to seminars/conferences related to the department' and calculate the mean GPA for each group\n",
        "gpa_by_attendance = df.groupby('Attendance to the seminars/conferences related to the department')['Cumulative grade point average in the last semester (/4.00)'].mean().reset_index().replace({1: 'Yes', 2: 'No'})\n",
        "\n",
        "# Plotting the clustered bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=gpa_by_attendance, x='Attendance to the seminars/conferences related to the department', y='Cumulative grade point average in the last semester (/4.00)')\n",
        "plt.title('Mean GPA by Attendance to Seminars/Conferences')\n",
        "plt.xlabel('Attendance to Seminars/Conferences')\n",
        "plt.ylabel('Mean GPA')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "4am7nNf_n91G",
        "outputId": "33fb357c-cada-4aaa-d02e-99757844b99d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new column \"Total Class Engagement\" by summing up the values from the three columns\n",
        "df['Total Class Engagement'] = df['Taking notes in classes'] + df['Listening in classes'] + df['Discussion improves my interest and success in the course']\n",
        "\n",
        "# Scatter plot of Total Class Engagement vs GPA\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data=df, x='Total Class Engagement', y='Cumulative grade point average in the last semester (/4.00)')\n",
        "plt.title('Total Class Engagement vs GPA')\n",
        "plt.xlabel('Total Class Engagement')\n",
        "plt.ylabel('GPA')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "zRQuHIW4oAMS",
        "outputId": "81815ebb-fbdf-4dbd-e3e2-6b3f9843d80b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "pJHXPfyO9ge1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9T3LsDC0i1cG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}