Car Price Prediction in the USA by using Liner Regression

This paper studies a Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing factors/variables in the U.S automobile industry. The prediction of a car price has become a high-interest research area of great importance, as it requires significant initiative and knowledge of the field expert. I have applied to a highly comprehensive analysis with all data cleaning, exploration, visualization, feature selection and model building. The data used for the prediction was collected from the web portal fred.stlouisfed.org using a web scraper that was written in Python/Jupyter programming language. According to problem-solving, I have split it into 5 parts (Data understanding and exploration, Data cleaning, Data preparation: Feature Engineering and Scaling, Feature Selection using RFE and Model Building and Linear Regression Assumptions Validation and Outlier Removal).