Python Pandas Matplotlib Seaborn

Performed in-depth analysis of the Titanic dataset to uncover survival trends based on gender, class, and age. Created visualizations and derived insights.

🌐 View on Kaggle

Power BI

A visually rich dashboard analyzing hotel revenue, occupancy, performance metrics, and operational KPIs for multiple luxury properties. Includes insights on ADR, RevPAR, cancellations, and realization rates.

📊 View on GitHub  |  💾 Download PBIX File

ADR, RevPAR, DBRN, DSRN, DURN, Cancellation & Realization Rates

• 📊 1.68B Revenue analyzed across multiple properties
• 🏨 57.79% Average Occupancy Rate
• 💰 12,696 Average Daily Rate (ADR)
• ⭐ 70.14% Realization Rate
• 📈 Interactive filters for city, room type, and date ranges
• 🏢 6+ Luxury Hotel Properties analyzed

Tableau Git GitHub

Designed and developed an interactive HR Analytics Dashboard in Tableau to visualize key employee metrics such as turnover, department performance, and satisfaction levels. The dashboard enables data-driven HR decision-making by providing clear insights into workforce trends.

• Identified departments with the highest attrition rates.
• Analyzed correlations between job satisfaction and employee performance.
• Created dynamic filters for gender, department, and job role comparisons.

📁 Download Tableau File (.twbx)
🌐 View on Tableau Public

Python Pandas Scikit-learn Matplotlib

This project applies Linear Regression to predict housing prices based on features such as area, number of bedrooms, and age. It demonstrates both Univariate and Multivariate Regression techniques using Python libraries like scikit-learn, pandas, and matplotlib.

• ✅ Built regression models for accurate house price prediction.
• 📊 Evaluated performance using MAE, MSE, RMSE, and R² metrics.
• 📈 Visualized regression lines and predicted vs actual values.
• 🧹 Performed data cleaning, outlier detection, and train-test splitting.

🌐 View Code on Google Colab