Price-Prediction-for-Used-Cars-Datascience-Project

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.

Problem Statement:

The problem statement for this project is to predict the price of a used car based on a set of features, such as the car's manfacturer, years used, mileage, engine,power,kilometers driven and no. of seats.

Solution approach:

A machine learning model can be used to predict used car prices by considering a variety of factors.The model can be trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.

Observations :

The following observations were made during the course of this project:

• The manfacturer of the car is the most important features for predicting the price of a used car.
• The no. of years the car used has a negative on the price.
• The mileage of the car also has impact on the price.
• The power of the car has impact on the price.
• The engine of the car has impact on the price.
• The no. of seats the car has impact on the price.
• The Kilometers the car driven has small impact on the price

Findings:

The following findings were made from the results of this project:

• The Cat Boost algorithm is a highly accurate model for predicting the price of a used car.
• The price of a used car can be predicted with a high degree of accuracy based on a set of features, such as the car's manfacturer, years used, mileage, engine,power,kilometers driven and no. of seats.
• The manfacturer of the car is the most important features for predicting the price of a used car.
• The no. of years the car used has a negative on the price.
• The mileage of the car also has impact on the price.
• The power of the car has impact on the price.
• The engine of the car has impact on the price.
• The no. of seats the car has impact on the price.
• The Kilometers the car driven has small impact on the price

Insights:

The insights from this project can be used by car dealerships, car buyers, and other businesses that are involved in the used car market. These insights can help these businesses to make more informed decisions about the pricing of used cars. For example, car dealerships can use these insights to set more competitive prices for their used cars. Car buyers can use these insights to get a better deal on a used car. And other businesses that are involved in the used car market can use these insights to improve their operations.# Price-Prediction-for-Used-Cars-Datascience-Project

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.