Analysis-of-Factors-Affecting-Vehicle-Selling-Price

Overview

This R script performs analysis and prediction on a dataset containing details about cars and their attributes. The goal is to build a predictive model for car prices using multiple linear regression.

Dataset

The Dataset contains the following columns:

• Price: Selling price of the car (dependent variable).
• Kilometer: Distance traveled by the car.
• Year: Year of manufacturing.
• Engine_numeric: Engine capacity in numeric format.
• Max_Power_numeric: Maximum power of the car's engine in numeric format.
• Fuel.Type: Type of fuel used by the car.
• Transmission: Transmission type of the car.
• Owner: Number of previous owners.
• Seller.Type: Type of seller.
• Seating.Capacity: Seating capacity of the car.

Workflow

1. Data Preparation and Exploration:

   • Loading the dataset and exploring its structure.
   • Visualizing data distributions and relationships.

2. Data Preprocessing:

   • Handling missing values.
   • Encoding categorical variables.
   • Removing duplicate rows.
   • Handling outliers using Winsorization.

3. Model Building:

   • Splitting the data into training and testing sets.
   • Building multiple linear regression models with different combinations of predictor variables.

4. Model Evaluation:

   • Assessing the assumptions of linear regression models.
   • Evaluating model performance using metrics such as MAE, RMSE, and R-squared.

5. Additional Analysis:

   • Calculating Pearson correlation coefficients between numeric variables.
   • Visualizing correlations using a correlation plot.
   • Making predictions on the test dataset and comparing them with actual prices.

Files

• car_price_prediction_script.R: The main R script containing all the code for data analysis, preprocessing, modeling, and evaluation.
• car_details_v4.csv: The dataset used for analysis.
• Report: FInal report of the project.

Usage

1. Ensure you have R and the required libraries installed (listed in the script).
2. Download the dataset file car_details_v4.csv and place it in the appropriate directory.
3. Run the R script car_price_prediction_script.R in an R environment or RStudio.

Dependencies

• R (version X.X.X)
• Required R libraries: ggplot2, dplyr, DescTools, caret, lmtest

Author

Ganesh Vannam