Power BI dashboard analysing rail operations: ticket sales, delays, revenue, and customer behaviour.
Exploratory data analysis project on NYC Taxi trip data using Python to identify demand patterns, operational trends, and opportunities to optimize taxi services.
Through exploratory data analysis and statistical evaluation, this project investigates how customer attributes specifically Gender, Age, and User Type influence ride duration. The analysis aims to identify patterns in trip behavior and assess whether demographic and subscription characteristics are associated with differences in usage.
Develop a predictive model to accurately forecast hourly traffic volumes at different road junctions based on historical traffic data
30-year U.S. airline market analysis (1993–2024) examining pricing trends, competition effects, demand concentration, and route-level business intelligence in Python.
Deep exploratory analysis of NYC TLC trip data to understand demand patterns, zone-level variability, seasonality, and revenue distribution. Conducted structured EDA on spatial heterogeneity, temporal trends, skew, feature correlations, and lag effects. Built Prophet and LightGBM models.
An end-to-end data analysis project focused on OLA ride-sharing data including trip patterns, demand analysis, driver performance metrics using SQL and Power BI.
Interactive Power BI dashboard analyzing Mumbai Local Railway passenger traffic, peak-hour congestion, and station utilization using DAX, Power Query, and Excel.
fmCSA carrier data extraction tool
Difference-in-Differences analysis of bus-lane policies and ridership trends in Israel.
End-to-End Data Analytics Dashboard using Excel and Python for Delhi Metro Rail Corporation.
End-to-end analysis of NYC Yellow Taxi trip data using PySpark, covering data cleaning, EDA, feature engineering, demand patterns, and fare prediction modeling.
Real-world Markov Process Simulation using Divvy Bike Data (GBFS), demonstrating SAS-to-Python migration for operational analytics and forecasting.
Python EDA on Uber ride request data to analyze trip patterns, peak demand hours, cancellation trends, and supply-demand gaps using Pandas and Seaborn.
Data analysis for Ride-sharing market and demand analysis for Chicago, integrating trip, competitor, and weather data to generate data-driven operational and strategic insights.
Synthetic transportation analytics control tower built with SQL, Power BI, DAX, dimensional modelling, and tested KPI reconciliation.
Python-based analytical framework for large-scale vehicular telematics data processing, feature engineering, and road safety analytics. Developed during a research internship at BITS Pilani Hyderabad Campus under the TRIAL Lab for telematics-driven crash-risk assessment and intelligent transportation research.
End-to-end SQL analytics project analyzing ride-hailing data with insights on revenue, driver performance, demand patterns, and profitability.
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