This assignment involves analyzing a dataset containing various details about used cars, with the primary focus on the target variable: the price of the cars, measured in lakhs.
The dataset includes features such as make and model, location, year of manufacture, mileage, odometer reading, fuel type, transmission type, number of owners, engine displacement, horsepower, number of seats, and the new price of the car.
Key tasks include investigating and handling missing values through imputation or removal, cleaning the data by stripping units from numerical attributes, and converting categorical variables (fuel type and transmission) into numerical formats using one-hot encoding. Additionally, you will create a new feature representing the current age of the car by subtracting the manufacture year from the current year.
Finally, various data manipulation operations such as selecting, filtering, renaming, mutating, arranging, and summarizing with group by will be performed to gain insights into the dataset. This assignment will enhance your skills in data preprocessing and feature engineering while providing practical experience in data analysis.