/Car-price-prediction

The primary objective of is project is to create a data science solution for predicting used car prices accurately by analyzing a diverse dataset including car model, no. of owners, age, mileage, fuel type, kilometers driven, features and location. The aim is to build a ML model that offers users to find current valuations for used cars

Primary LanguageJupyter Notebook

Car-price-prediction

CarDekho Used Car Price Prediction

Project Overview

This project aims to create a data science solution for predicting used car prices accurately by analyzing a diverse dataset obtained from CarDekho. The dataset includes various factors such as car model, number of owners, age, mileage, fuel type, kilometers driven, features, and location. The ultimate goal is to build a machine learning model that offers users the ability to find current valuations for used cars.

Technologies Used

Python Pandas NumPy Matplotlib Seaborn Scikit-learn

Data Understanding

The dataset contains multiple Excel files, each representing a city. Each Excel file provides an overview of each car, including details, specifications, and available features.

Data Sources

Data collected from CarDekho. Dataset link: Dataset Feature description link: Features

Approach

Import Data: Load data from all Excel files. Data Inspection: Examine the structure of each dataset component (New Car Detail, New Car Overview, etc.).

Data Preprocessing:

Handle Missing Values: Impute or remove missing values appropriately. Feature Engineering: Extract relevant information from features like age, mileage, etc. Encode Categorical Variables: Use suitable techniques. Normalize/Scale Numerical Features: Bring numerical features to a comparable range. Exploratory Data Analysis (EDA): Create visualizations to understand the distribution of target variables (used car prices) and relationships between features.

Model Selection: Choose regression models suitable for predicting continuous values. Model Evaluation: Use suitable metrics to evaluate model performance. Fine-tune Hyperparameters: Optimize model hyperparameters to improve performance. Feature Importance: Analyze feature importance to understand which features contribute most to the predictions.