Project Overview

This project involves a detailed analysis of the automotive market using data from the 'car dekho' database. The focus is to understand the inventory and trends of vehicles available in the market from 2020 to 2023, with a deeper look into the fuel preferences over the years.

Objectives

- To assess the total number of cars available in the 'car dekho' database.
- To project the number of cars that will be available in the year 2023.
- To analyze the availability of cars for each year from 2020 to 2022.
- To understand the distribution of cars by fuel type (Petrol, Diesel, CNG) over the years.

Methodology

1. Data Retrieval: Executed SQL queries to extract data from the 'car dekho' database for various years.
2. Aggregate Analysis: Performed count aggregations to determine the total number of cars available, as well as the number available for each year and fuel type.
3. Trend Identification: Grouped data by year and fuel type to identify trends and patterns in car availability.
4. Comparative Analysis: Analyzed the distribution of cars with more than 100 units available in a year as well as those with fewer than 50.

Challenges and Solutions

- Adapted queries for different subsets of data to ensure accurate counts for each category.
- Utilized SQL grouping and having clauses to filter and categorize data effectively.

Results

1. Successfully retrieved the total number of cars available in the 'car dekho' database.
2. Identified the projected number of cars for the year 2023 and the distribution from 2020 to 2022.
3. Analyzed the data to reveal the popularity and availability trends of different fuel types over the years.
4. Isolated the data for years with significant inventory changes, highlighting years with more than 100 cars and fewer than 50 cars available.

Conclusion

The project demonstrated robust SQL querying techniques to derive meaningful insights from the 'car dekho' automotive database. The findings provided a clear picture of the car inventory trends, fuel preferences over the years, and highlighted potential growth or decline in car availability. This project underscores the importance of data-driven decision-making in the automotive industry and showcases the analytical capabilities of SQL for market trend analysis.