Student Performance Prediction Model

Welcome to the Student Performance Prediction Model repository! This project is a machine learning model that predicts student scores based on the number of hours studied.

Table of Contents

• Introduction
• Dataset
• Model Overview
• Installation
• Usage
• Results

Introduction

This project is focused on building a linear regression model to predict student performance based on the hours studied. The model was developed using Python and Flask and is intended to serve as a practical demonstration of my skills in data science and machine learning.

Dataset

The dataset used in this project includes the following columns:

Hours Studied	Scores
2.5	21
5.1	47
3.2	27
8.5	75
3.5	30
1.5	20
9.2	88
5.5	60
8.3	81
2.7	25
7.7	85
5.9	62

Note: This dataset is sourced from Kaggle's Student Study Hours Dataset.

Model Overview

The model was built using the following steps:

1. Data Collection: The dataset was sourced from Kaggle's Student Study Hours Dataset.
2. Data Preprocessing: The data was cleaned and prepared for modeling.
3. Modeling: A linear regression model was trained to predict student scores.
4. Deployment: The model was deployed as a web application using Flask.

Key Features

• Simple and Intuitive: Easy-to-understand linear regression model.
• Web Interface: Users can input study hours through a web interface and receive predicted scores.
• Educational: Aimed at beginners looking to understand basic machine learning concepts and web development using Flask.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/kevin-babu-dotcom/StudentScoreModel.git
   

2. Navigate to the repository

   cd StudentScoreModel
   

3. Install the required dependencies:

    pip install -r requirements.txt
   

4. Create a virtual environment (optional but recommended):

     python -m venv venv
     venv\Scripts\activate`
   

5. Install the required dependencies:

    pip install -r requirements.txt
   

Usage

To use the model, follow these steps:

1. Run the Flask Application: Make sure you are in the project directory, then run:

    python app.py
   

2. Access the Web Application: Open your web browser and go to http://127.0.0.1:5000/.

3. Input Study Hours: On the web page, enter the number of study hours in the input field and submit the form to get the predicted score.

Results

Here is a plot showing the relationship between hours studied and scores The script generates a scatter plot showing the correlation between the hours studied and the grades. The regression line is also plotted, showing the linear relationship established by the model:

     plt.figure(figsize=(8, 6))
     plt.scatter(X, y, color='blue', label='Actual Data')
     plt.plot(X_test, model.predict(X_test), color='red', label='Regression Line')
     plt.title('Hours Studied vs. Scores')
     plt.xlabel('Hours Studied')
     plt.ylabel('Scores')
     plt.legend()
     plt.show()