Data Science Projects with Source Code

Data Science Projects by Domain

Our projects span multiple industries and data types to give you practical experience.

Exploratory Data Analysis

EDA projects with thorough data cleaning, statistical summaries, correlation analysis, and publication-quality visualizations. Each notebook walks through the analysis step by step with markdown explanations alongside the code.

Predictive Analytics

Regression and classification projects using scikit-learn, XGBoost, and LightGBM. Projects include feature engineering, model selection, hyperparameter tuning, and performance evaluation with proper train/test splits and cross-validation.

Data Visualization Dashboards

Interactive dashboards built with Streamlit, Plotly Dash, or Flask that let users explore data through filters, charts, and maps. These are presentation-ready demos that work great in academic evaluations.

Running Data Science Projects

Most projects run in Jupyter Notebook or JupyterLab. Install dependencies with pip install -r requirements.txt, open the notebook, and run cells sequentially. Dashboard projects start with streamlit run app.py.

Datasets Included

Every project ships with the dataset used — either as CSV files or with a download script for larger datasets. Data sources are documented so you can cite them in academic reports.