Student Result Analysis & Prediction

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Project Overview

This project analyzes student performance data and builds a machine learning model to predict scores in Math, Reading, and Writing.

It focuses on identifying how factors like gender, parental education, study hours, and lifestyle impact academic performance.

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Dataset

Dataset used: Expanded_data_with_more_features.csv Total records: 30,641 students

Features include:

• Gender
• Ethnic Group
• Parent Education
• Lunch Type
• Test Preparation
• Weekly Study Hours
• Scores (Math, Reading, Writing)

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Data Preprocessing

• Removed unnecessary column (Unnamed: 0)
• Handled missing values
• Converted categorical data where required.

Checked dataset structure using:

• .info()
• .describe()
• .isnull().sum()

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Exploratory Data Analysis (EDA)

Performed detailed visualization using:

• Seaborn
• Matplotlib

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Visualizations:

• Gender distribution
• Heatmaps of score averages
• Bar plots (Gender vs Scores)
• Correlation heatmap

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🎯 Future Improvements

• Feature engineering
• Deploy using Streamlit