This repository contains scripts and Jupyter notebooks for analyzing student performance data. The analysis includes exploring data trends, visualizing insights, and applying machine learning models for predictive tasks.
- Data Analysis: Utilizes Python libraries such as Pandas and NumPy for data manipulation and exploratory data analysis (EDA).
- Visualization: Matplotlib and Seaborn are used for creating visual representations of data trends.
- Machine Learning: Scikit-learn is employed for building predictive models and clustering analysis.
- Jupyter Notebooks: Includes interactive notebooks (
analysis.ipynb,machine_learning.ipynb) for detailed analysis workflows. - Scripts: Python scripts (
visualization.py,scripts/) for automating data visualization and analysis tasks.
- Python 3.x
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Data Preparation:
- Place your student performance dataset (
students.csv) into thedata/directory.
- Exploratory Data Analysis:
- Open and run
analysis.ipynbusing Jupyter Notebook to explore the dataset, perform statistical analysis, and generate initial insights.
- Visualization:
- Execute
visualization.pyto generate visualizations such as histograms, scatter plots, and bar charts to visualize key metrics and trends in the data.
- Machine Learning Models:
- Explore
machine_learning.ipynbfor applying machine learning algorithms like regression, classification, or clustering to predict student performance or identify patterns.
- Scripts:
- Customize scripts in the
scripts/directory for specific data preprocessing, feature engineering, or other analysis tasks tailored to your dataset.
- Contributing:
- Feel free to fork this repository, make improvements, and submit pull requests. Contributions to add new features or enhance existing functionalities are welcome.
- Inspiration for this project came from the need to better understand student performance factors and contribute to educational research.