CarDekho Used Car Price Prediction-Kaggle Notebook

Process Flow of Project

1. Understanding Problem Statement
2. Getting System Ready
3. Data Collection
4. Understanding the Data-Data Eyeballing & Data Description
5. Data Cleaning & Preprocessing I
6. Exploratory Data Analysis (EDA)
7. Univariate Analysis
8. Bivariate Analysis
9. Multivariate Analysis
10. Data Cleaning & Preprocessing II
11. Insights from Data Visualization
12. Feature Engineerig
13. Model Buidling & Evaluation
14. Selection of Best Model & Hyperparameter Tuninng
15. Generating Pickle file

Problem Statement:

The used car market in India is a dynamic and ever-changing landscape. Prices can fluctuate wildly based on a variety of factors including the make and model of the car, its mileage, its condition and the current market conditions. As a result, it can be difficult for sellers to accurately price their cars.

Approach:

We propose to develop a machine learning model that can predict the price of a used car based on its features. The model will be trained on a dataset of used cars that have been sold on Cardekho.com in India. The model will then be able to be used to predict the price of any used car, given its features.

Objective

To build suitable Machine Learning Model for Used Car Price Prediction.

Benefits:

The benefits of this solution include:

1. Sellers will be able to more accurately price their cars which will help them to sell their cars faster and for a higher price.
2. Buyers will be able to find cars that are priced more competitively.
3. The overall used car market in India will become more efficient.

We believe that this project has the potential to make a significant impact on the used car market in India. We are excited to work on this project and to see the positive impact that it can have.