Student Performance Indicator ML Model

This repository contains a machine learning model designed to predict student performance based on various features like gender, parental education, and lunch type. The model is built using Pandas, NumPy, and Scikit-learn, achieving 85% accuracy.

Table of Contents

1. Project Overview
2. Features
3. Technologies Used
4. Data
5. How It Works
6. Model Details
7. Installation and Setup
8. Usage
9. Acknowledgments

Project Overview

This project uses a machine learning pipeline to predict student performance based on multiple features. The pipeline is built using Scikit-learn, and the model achieves an accuracy of 85%. Exploratory Data Analysis (EDA) was conducted to uncover patterns and trends from the dataset.

Features

• 85% accuracy achieved in predicting student performance.
• Exploratory Data Analysis (EDA) performed on 1000+ student records to uncover insights.
• Key features analyzed include gender, parental education, test preparation course completion, and lunch type.
• Data visualization with Seaborn and Matplotlib to highlight significant findings.

Technologies Used

• Python: Programming language for data analysis and model building.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• Scikit-learn: For building and evaluating the machine learning model.
• Seaborn and Matplotlib: For data visualization.

Data

The dataset consists of over 1000 student records with information such as:

• Gender
• Parental education level
• Test preparation course completion status
• Type of lunch provided
• Student scores in various subjects

How It Works

Data Preprocessing:

• Cleaned and formatted the dataset using Pandas.
• Handled missing values and categorical data using Label Encoding and One-Hot Encoding.

Exploratory Data Analysis (EDA):

• Visualized patterns using Seaborn and Matplotlib.
• Identified key insights, such as a 10% increase in scores for students who completed test preparation courses.

Model Building:

• The model was built using Scikit-learn, utilizing algorithms like Logistic Regression or Random Forest (based on your choice).
• Evaluated the model's performance using cross-validation.

Model Details

• Accuracy: 85% prediction accuracy on student performance.
• Algorithms Used: Logistic Regression or Random Forest, based on the analysis.
• Metrics: Accuracy, precision, recall, and F1-score were evaluated during the testing phase.

Installation and Setup

Prerequisites:

Make sure you have the following installed:

• Python (>= 3.8)
• pip (Python package manager)

Steps to Run the Project:

1. Clone this repository:

   git clone https://github.com/Adeebeeeee/Student-Performance-Indicator-ML-Model.git
   cd Student-Performance-Indicator-ML-Model

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter notebook or Python script to execute the analysis and train the model.

Usage

• Run the provided Jupyter notebook to load the dataset, perform EDA, train the model, and make predictions.
• Use the model to predict student performance based on the available features.

Acknowledgments

• Dataset: The student performance dataset used in this project is available on Kaggle or a similar open source platform.
• Libraries: Thanks to Scikit-learn, Pandas, Matplotlib, and Seaborn for providing essential tools for machine learning and data visualization.

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License

This project is licensed under the MIT License.