STUDENT PERFORMANCE ANALYSIS
Project Overview :
- This project analyzes student performance using Python.
- Dataset contains study hours, attendance, and marks of students.
- Visualizations include bar charts, scatter plots, and heatmaps.
- Helps identify key factors that impact student results & scoring trends.
Objectives:
- Understand the dataset with summary statistics and missing value checks
- Analyze relationships between study hours, attendance & performance
- Generate key visualizations for better insights
- Identify patterns that impact student outcomes
Technologies Used :
- Python
- Pandas
- Matplotlib
- Seaborn
- NumPy
- VS Code
Steps Performed in Analysis :
1️⃣ Data LoadingRead the CSV file using pandas
- Display first few rows
- Check dataset info and shape
2️⃣ Data Cleaning
- Identify missing values
- Handle null entries if required
- Ensure numeric columns are correctly typed
3️⃣ Exploratory Data Analysis (EDA)
- Summary statistics
- Mean study hours, mean attendance
- Correlation matrix
- Outlier detection
4️⃣ Data Visualizations
- Generated visual outputs (available in results folder):
- Study Hours vs Score
- Attendance vs Score
- Score Distribution
- Correlation Heatmap
How to Run the Project :
-
Download or clone the repository
-
Make sure Python is installed
Install required libraries using:
- pip install pandas numpy matplotlib seaborn
Run the analysis file:
- python analysis.py
-
All generated graphs will be saved inside the results folder
Dataset :
The dataset contains fields such as:
- Student Name
- Study Hours
- Attendance
- Marks
Results :
All outputs and graphs are available in the results folder.
-
Distribution graphs
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Correlation heatmap
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Scatter plots
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Performance comparison charts
Conclusion :
This project helps in understanding student academic behavior through data. It is useful for students, teachers, and anyone interested in data analysis using Python. The insights generated can support better decision-making and performance improvement strategies.