A collection of end-to-end data science and analytics projects covering EDA, machine learning, forecasting, and interactive dashboards. All notebooks run directly in Google Colab — no local setup required.
Analyzes global COVID-19 trends with a deep focus on India. Compares trajectories across the US, Italy, and China. Uses Facebook Prophet for time-series forecasting of future case counts.
Tools: pandas · plotly · seaborn · fbprophet
Extracts business intelligence from raw transactional data. Covers sales trend analysis, customer behavior, product performance, RFM segmentation, and cohort retention analysis.
Tools: pandas · numpy · plotly · seaborn · scikit-learn
Segments customers into distinct groups using unsupervised ML. Determines optimal cluster count via the Elbow Method and Silhouette Score. Reduces dimensionality with PCA for 2D cluster visualization.
Tools: scikit-learn · pandas · matplotlib · seaborn
Builds a supervised regression model to predict future sales from historical data. Includes feature engineering (lag features, rolling averages, time-based features) and evaluates performance using MAE, RMSE, and R².
Tools: scikit-learn · pandas · numpy · matplotlib · seaborn
Analyzes academic, demographic, and socio-economic factors affecting student grades. Delivers findings through an interactive Plotly dashboard with filters for dynamic exploration.
Tools: pandas · plotly · seaborn · scikit-learn · scipy
| Category | Tools |
|---|---|
| Data Manipulation | pandas, numpy |
| Visualization | matplotlib, seaborn, plotly |
| Machine Learning | scikit-learn |
| Forecasting | fbprophet |
| Environment | Google Colab |
Each project is self-contained in its own .ipynb notebook. Click any Open in Colab badge above to run it directly in your browser.
To run locally:
git clone https://github.com/RajBhadani/Projects-Data_Science_and_Analytics.git
cd Projects-Data_Science_and_Analytics
jupyter notebookDistributed under the Apache 2.0 License.