Google Maps–Inspired AI Traffic Route Guidance | Deep Learning + Heuristic Search | Real-World SCATS Data (Boroondara 2006)
Data-driven market analysis of Swiggy restaurants to uncover top cuisines, delivery efficiency trends, and high-opportunity areas for new restaurant entry.
Built a machine learning pipeline to detect fraudulent credit card transactions using real-world data (1M+ rows). Explored fraud patterns, validated key hypotheses, and optimized models like KNN, Random Forest, and SVM with 98%+ recall. Strong focus on data insights & impact.
An end-to-end analysis of Google Pay India Play Store reviews to uncover reliability issues, user sentiment patterns, and support performance.
Statistical analysis of NYC TAP funding across colleges using Python, EDA, hypothesis testing, and real 260K-row dataset.
End-to-end market and restaurant analysis using Zomato data (123K+ records), focusing on city-wise competition, pricing, ratings, cuisines, and delivery trends using Python.
Exploratory Data Analysis of a ride-hailing dataset uncovering cancellation patterns, demand trends, operational delays (TAT), revenue drivers, and customer experience insights using Python, Pandas, Seaborn, and Matplotlib.
Develop a predictive model to accurately forecast hourly traffic volumes at different road junctions based on historical traffic data
Add a description, image, and links to the real-world-dataset topic page so that developers can more easily learn about it.
To associate your repository with the real-world-dataset topic, visit your repo's landing page and select "manage topics."