A Clear View into Namma Yatri's Trips

Project Description: Namma Yatri is a comprehensive transportation project that utilizes SQL, Excel, and Power BI to analyze and visualize data related to trips, drivers, and customer interactions. The objective is to gain actionable insights to optimize service efficiency, customer experience, and overall business performance.

Data Insights: The project incorporates data from various sources, including trip details, driver information, and customer interactions, creating a holistic view of the transportation ecosystem.

Key Components:

• Excel: Stores and manages structured data related to trips, drivers, and customer interactions.
• SQL: Used for data cleaning, preprocessing, and performing specific analyses that complement SQL queries.
• Power BI: Employs interactive visualizations and dashboards to present data insights in a user-friendly manner, facilitating better decision-making.

Key Questions and Insights:

• Most Used Payment Method: Analyzing payment method data to determine the most frequently used payment method among customers.
• Highest Payment Instrument: Identifying the instrument through which the highest payment transactions were made.
• Two Locations with Most Trips: Examining trip data to pinpoint the two locations that have experienced the highest number of trips.
• Top 5 Earning Drivers: Aggregating driver earnings data to identify and showcase the top 5 earning drivers.
• Duration with More Trips: Analyzing the temporal distribution of trips to identify the duration (e.g., time of day or day of the week) with the highest number of trips.
• Driver-Customer Pair with More Orders: Exploring data to find the specific driver-customer pairs that have the highest number of completed orders.
• Search to Estimate Rate: Calculating the rate at which customer searches lead to trip estimates.
• Estimate to Search for Quote Rates: Evaluating how often customers who receive estimates proceed to search for quotes.
• Conversion Rate: Assessing the overall conversion rate from customer interactions to completed trips.
• Area with Highest Trips in Duration: Identifying the geographical area that experiences the highest number of trips during specific durations.
• Area with Highest Fares, Cancellations, Trips: Analyzing data to determine the geographical area that has the highest fares, cancellations, and overall trip frequency.
• Duration with Highest Trips and Fares: Pinpointing the specific duration with the highest number of trips and the highest overall fares.