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https://github.com/rahulsm20/car-data

A data analytics project that involves analyzing a car dataset that includes information on various car brands, years, prices, mileage, and fuel types, in order to gain insights into the car market.
https://github.com/rahulsm20/car-data

data-analysis data-analytics matplotlib numpy pandas python

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A data analytics project that involves analyzing a car dataset that includes information on various car brands, years, prices, mileage, and fuel types, in order to gain insights into the car market.

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README 

        
# Car Data Analysis Project

[Tableau Dashboard](https://public.tableau.com/views/Quikrdataanalysis/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link7)

## Objectives

The main objective of this project is to analyze the given dataset to understand patterns and trends in the data, to draw conclusions relating to how factors like region, age, gender and pre-existing conditions affect a customer's insurance charges and to develop a model that can predict the charges for a given individual based on their age, sex, region, smoker status, and number of children.



## About the data

The dataset used for this project consists of the following columns:



- Name of car 

- Company name : Name of the company that manufactures the car

- Year : Year the car was made

- Price : Price the vehicle is listed for

- Kms driven : Kilometres the car has been drived for

- Fuel_type : Type of fuel the car uses



## Table of Contents



- [Overview](#overview)

- [Methodology](#methodology)

- [Conclusion](#conclusion)

  

## Methodology



We analyzed the dataset by performing the following steps:



1. Data exploration: We explored the dataset to understand its size, structure, and format. We also checked for missing values and outliers.



2. Identify trends and patterns: We identified trends and patterns within the data, such as the distribution of prices and the correlation between the year of the car and its price.



3. Segment the data: We segmented the data by car brand and fuel type to analyze the trends and patterns for each group.



4. Visualize the data: We created various charts and graphs to visualize the relationships between different variables, such as scatterplots to show the correlation between the year and price and bar charts to compare the average price of different car brands.



5. Draw conclusions: We drew conclusions from the insights and used them to inform decision-making.



## Conclusions

1. Car prices vary greatly by company.   

Luxury brands tend to be much higher in cost than cars made by mass producers.  

Most expensive car brand:  Jaguar | Average price: ₹2,495,000.00   

Least expensive car brand:  Fiat  | Average price: ₹109,875.00

2. Newer cars are likely to be higher in value.   

This indicates the depreciation aspect of motor vehicles.  

Cars made in 2019 have the highest average price of ₹739,527.78, which is +45.72% higher than the sample average.

3. Average price also varies by fuel type of the car.  

Average diesel car costs ₹488,780.84, that is +33.68% more than the average petrol car and +58.06% more than the average LPG car.

4. Distribution of listed cars by fuel type is very close, where petrol cars account for 52.6%, diesel cars account for 47.1% and LPG cars account for a measly 0.2%.

5. Most cars listed are by large manufacturers making cars of moderate cost.  

As a result, Maruti is the manufacturer with the most units listed at 27.2% of all listed cars.   

Hyundai comes in second at 17.1% and Mahindra at third with 11.7%.