StudPred.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SharanyaSD/Student-Performance-Dashboard/blob/main/StudPred.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LLBVu1tQGIcV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"StudentsPerformance.csv\")"
      ],
      "metadata": {
        "id": "s4xS3-lYGtNY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset\n",
        "print(df.head())\n",
        "\n",
        "# Summary statistics of the dataset\n",
        "print(df.describe())\n",
        "\n",
        "# Count the number of missing values in each column\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# Count the unique values in categorical columns\n",
        "print(df['gender'].value_counts())\n",
        "print(df['race/ethnicity'].value_counts())\n",
        "print(df['parental level of education'].value_counts())\n",
        "print(df['lunch'].value_counts())\n",
        "print(df['test preparation course'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7orey3yG_eA",
        "outputId": "ad5ed6d7-8f18-4ab6-b97a-9e9c940cbea4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   gender race/ethnicity parental level of education         lunch  \\\n",
            "0  female        group B           bachelor's degree      standard   \n",
            "1  female        group C                some college      standard   \n",
            "2  female        group B             master's degree      standard   \n",
            "3    male        group A          associate's degree  free/reduced   \n",
            "4    male        group C                some college      standard   \n",
            "\n",
            "  test preparation course  math score  reading score  writing score  \n",
            "0                    none          72             72             74  \n",
            "1               completed          69             90             88  \n",
            "2                    none          90             95             93  \n",
            "3                    none          47             57             44  \n",
            "4                    none          76             78             75  \n",
            "       math score  reading score  writing score\n",
            "count  1000.00000    1000.000000    1000.000000\n",
            "mean     66.08900      69.169000      68.054000\n",
            "std      15.16308      14.600192      15.195657\n",
            "min       0.00000      17.000000      10.000000\n",
            "25%      57.00000      59.000000      57.750000\n",
            "50%      66.00000      70.000000      69.000000\n",
            "75%      77.00000      79.000000      79.000000\n",
            "max     100.00000     100.000000     100.000000\n",
            "gender                         0\n",
            "race/ethnicity                 0\n",
            "parental level of education    0\n",
            "lunch                          0\n",
            "test preparation course        0\n",
            "math score                     0\n",
            "reading score                  0\n",
            "writing score                  0\n",
            "dtype: int64\n",
            "female    518\n",
            "male      482\n",
            "Name: gender, dtype: int64\n",
            "group C    319\n",
            "group D    262\n",
            "group B    190\n",
            "group E    140\n",
            "group A     89\n",
            "Name: race/ethnicity, dtype: int64\n",
            "some college          226\n",
            "associate's degree    222\n",
            "high school           196\n",
            "some high school      179\n",
            "bachelor's degree     118\n",
            "master's degree        59\n",
            "Name: parental level of education, dtype: int64\n",
            "standard        645\n",
            "free/reduced    355\n",
            "Name: lunch, dtype: int64\n",
            "none         642\n",
            "completed    358\n",
            "Name: test preparation course, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset\n",
        "print(df.head())\n",
        "\n",
        "# Summary statistics of the dataset\n",
        "print(df.describe())\n",
        "\n",
        "# Count the number of missing values in each column\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# Count the unique values in categorical columns\n",
        "print(df['gender'].value_counts())\n",
        "print(df['race/ethnicity'].value_counts())\n",
        "print(df['parental level of education'].value_counts())\n",
        "print(df['lunch'].value_counts())\n",
        "print(df['test preparation course'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IK62zoLqHHVb",
        "outputId": "10fe9efe-c833-4f6f-8352-bc89603cc71f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   gender race/ethnicity parental level of education         lunch  \\\n",
            "0  female        group B           bachelor's degree      standard   \n",
            "1  female        group C                some college      standard   \n",
            "2  female        group B             master's degree      standard   \n",
            "3    male        group A          associate's degree  free/reduced   \n",
            "4    male        group C                some college      standard   \n",
            "\n",
            "  test preparation course  math score  reading score  writing score  \n",
            "0                    none          72             72             74  \n",
            "1               completed          69             90             88  \n",
            "2                    none          90             95             93  \n",
            "3                    none          47             57             44  \n",
            "4                    none          76             78             75  \n",
            "       math score  reading score  writing score\n",
            "count  1000.00000    1000.000000    1000.000000\n",
            "mean     66.08900      69.169000      68.054000\n",
            "std      15.16308      14.600192      15.195657\n",
            "min       0.00000      17.000000      10.000000\n",
            "25%      57.00000      59.000000      57.750000\n",
            "50%      66.00000      70.000000      69.000000\n",
            "75%      77.00000      79.000000      79.000000\n",
            "max     100.00000     100.000000     100.000000\n",
            "gender                         0\n",
            "race/ethnicity                 0\n",
            "parental level of education    0\n",
            "lunch                          0\n",
            "test preparation course        0\n",
            "math score                     0\n",
            "reading score                  0\n",
            "writing score                  0\n",
            "dtype: int64\n",
            "female    518\n",
            "male      482\n",
            "Name: gender, dtype: int64\n",
            "group C    319\n",
            "group D    262\n",
            "group B    190\n",
            "group E    140\n",
            "group A     89\n",
            "Name: race/ethnicity, dtype: int64\n",
            "some college          226\n",
            "associate's degree    222\n",
            "high school           196\n",
            "some high school      179\n",
            "bachelor's degree     118\n",
            "master's degree        59\n",
            "Name: parental level of education, dtype: int64\n",
            "standard        645\n",
            "free/reduced    355\n",
            "Name: lunch, dtype: int64\n",
            "none         642\n",
            "completed    358\n",
            "Name: test preparation course, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Plot the distribution of scores\n",
        "sns.histplot(df['reading score'], kde=True, label='Reading Score')\n",
        "sns.histplot(df['math score'], kde=True, label='Math Score')\n",
        "sns.histplot(df['writing score'], kde=True, label='Writing Score')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Visualize the relationship between scores and categorical variables\n",
        "sns.boxplot(x='gender', y='math score', data=df)\n",
        "plt.show()\n",
        "sns.boxplot(x='race/ethnicity', y='reading score', data=df)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pRp-ceBJHL6G",
        "outputId": "21b40b70-d9a8-4b34-9252-ade50f2a235b"
      },
      "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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\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": [
        "!pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iat-bfkoIjaf",
        "outputId": "5cc50586-6058-4346-eac6-c3552f05f7f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (3.30.0)\n",
            "Requirement already satisfied: aiofiles in /usr/local/lib/python3.10/dist-packages (from gradio) (23.1.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from gradio) (3.8.4)\n",
            "Requirement already satisfied: altair>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (4.2.2)\n",
            "Requirement already satisfied: fastapi in /usr/local/lib/python3.10/dist-packages (from gradio) (0.95.1)\n",
            "Requirement already satisfied: ffmpy in /usr/local/lib/python3.10/dist-packages (from gradio) (0.3.0)\n",
            "Requirement already satisfied: gradio-client>=0.2.4 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.2.4)\n",
            "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from gradio) (0.24.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.14.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.1.2)\n",
            "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.2.0)\n",
            "Requirement already satisfied: markupsafe in /usr/local/lib/python3.10/dist-packages (from gradio) (2.1.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from gradio) (3.7.1)\n",
            "Requirement already satisfied: mdit-py-plugins<=0.3.3 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.3.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from gradio) (1.22.4)\n",
            "Requirement already satisfied: orjson in /usr/local/lib/python3.10/dist-packages (from gradio) (3.8.12)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from gradio) (1.5.3)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from gradio) (8.4.0)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.10/dist-packages (from gradio) (1.10.7)\n",
            "Requirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (from gradio) (0.25.1)\n",
            "Requirement already satisfied: pygments>=2.12.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.14.0)\n",
            "Requirement already satisfied: python-multipart in /usr/local/lib/python3.10/dist-packages (from gradio) (0.0.6)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from gradio) (6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from gradio) (2.27.1)\n",
            "Requirement already satisfied: semantic-version in /usr/local/lib/python3.10/dist-packages (from gradio) (2.10.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from gradio) (4.5.0)\n",
            "Requirement already satisfied: uvicorn>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.22.0)\n",
            "Requirement already satisfied: websockets>=10.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (11.0.3)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair>=4.2.0->gradio) (0.4)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair>=4.2.0->gradio) (4.3.3)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair>=4.2.0->gradio) (0.12.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio-client>=0.2.4->gradio) (2023.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gradio-client>=0.2.4->gradio) (23.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.13.0->gradio) (3.12.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.13.0->gradio) (4.65.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (0.1.2)\n",
            "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (2.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->gradio) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->gradio) (2022.7.1)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (8.1.3)\n",
            "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (0.14.0)\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "pip install gradio\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exaN36DS6fdI",
        "outputId": "278a1be6-94dd-498b-cd1a-c9ca1d2ea297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4694 sha256=9653a5cbf5c60a885079dc8ae72bdc5bda5a212c615b4d1c2097b53fc14cff86\n",
            "  Stored in directory: /root/.cache/pip/wheels/0c/c2/0e/3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, multidict, h11, frozenlist, async-timeout, aiofiles, yarl, uvicorn, starlette, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, aiosignal, httpx, fastapi, aiohttp, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.30.0 gradio-client-0.2.4 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 huggingface-hub-0.14.1 linkify-it-py-2.0.2 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.8.12 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3 yarl-1.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import gradio as gr\n",
        "\n",
        "# Assuming the dataset file is named \"studentperformance.csv\"\n",
        "df = pd.read_csv(\"StudentsPerformance.csv\")\n",
        "\n",
        "# Select the features and target variable\n",
        "features = ['gender', 'race/ethnicity', 'parental level of education', 'lunch', 'test preparation course']\n",
        "target = 'writing score'\n",
        "X = df[features]\n",
        "y = df[target]\n",
        "\n",
        "# Perform one-hot encoding on categorical variables\n",
        "X_encoded = pd.get_dummies(X)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train a Random Forest regressor model\n",
        "model = RandomForestRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error:\", mse)\n",
        "\n",
        "# Define a function for making predictions\n",
        "def predict_writing_score(gender, race_ethnicity, parental_education, lunch, test_prep):\n",
        "    data = pd.DataFrame([[gender, race_ethnicity, parental_education, lunch, test_prep]], columns=features)\n",
        "    # Perform necessary data preprocessing (e.g., one-hot encoding, label encoding) on the input data\n",
        "    data_encoded = pd.get_dummies(data)\n",
        "    \n",
        "    # Get the list of feature names from the one-hot encoded data\n",
        "    encoded_features = data_encoded.columns.tolist()\n",
        "    \n",
        "    # Find the missing features that were used during training\n",
        "    missing_features = set(X_encoded.columns.tolist()) - set(encoded_features)\n",
        "    for feature in missing_features:\n",
        "        # Add the missing feature with a value of 0\n",
        "        data_encoded[feature] = 0\n",
        "    \n",
        "    # Reorder the columns to match the order used during training\n",
        "    data_encoded = data_encoded[X_encoded.columns]\n",
        "    \n",
        "    prediction = model.predict(data_encoded)[0]\n",
        "    return prediction\n",
        "\n",
        "\n",
        "# Define the input and output interfaces for Gradio\n",
        "# Define the input and output interfaces for Gradio\n",
        "# Define the input and output interfaces for Gradio\n",
        "inputs = [\n",
        "    gr.inputs.Dropdown(choices=['female', 'male'], label=\"Gender\"),\n",
        "    gr.inputs.Dropdown(choices=['group B', 'group C', 'group D', 'group E'], label=\"Race/Ethnicity\"),\n",
        "    gr.inputs.Dropdown(choices=['high school', 'some college', 'associate\\'s degree',\n",
        "                                'bachelor\\'s degree', 'master\\'s degree'], label=\"Parental Education\"),\n",
        "    gr.inputs.Dropdown(choices=['standard', 'free/reduced'], label=\"Lunch\"),\n",
        "    gr.inputs.Dropdown(choices=['none', 'completed'], label=\"Test Preparation\")\n",
        "]\n",
        "output = gr.outputs.Textbox(label=\"Predicted Writing Score\")\n",
        "\n",
        "# Create the Gradio interface\n",
        "iface = gr.Interface(fn=predict_writing_score, inputs=inputs, outputs=output, title=\"Writing Score Predictor\")\n",
        "iface.launch(share=True)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "Su_6coepJHi1",
        "outputId": "154769ee-6886-440e-d7c8-ca28ec63a826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 216.8178948164126\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:219: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://f1afcaac66a3d21cb4.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/html": [
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}