Problem Description:
The aim is to build a model that predicts the selling price of a car based on features such as age, fuel type, seller type, transmission, etc. This problem is critical for car dealerships and individual sellers to estimate a fair selling price based on various parameters, ensuring profitability and market competitiveness.

Model Description:
The models being used are:

Linear Regression:

A straightforward model that establishes a linear relationship between the features and the target variable (selling price). This model is appropriate as a baseline to observe how well the features explain the variability in car prices.
Lasso Regression:

A type of linear regression that includes L1 regularization to penalize large coefficients, which can lead to simpler, more interpretable models by shrinking less important feature coefficients to zero. This model is ideal for addressing issues of multicollinearity and overfitting, which may arise with the baseline linear regression.
Estimated Time for Completion:
Data Collection & Preprocessing: 1 hour
Model Training & Evaluation: 2-3 hours
Visualization & Analysis: 1-2 hours
Testing & Optimization: 2 hours
Expected Outcome:
The models should be able to predict the car's selling price with reasonable accuracy based on the given dataset. The Lasso regression model is expected to handle overfitting better by reducing the number of significant features and improving generalization.

Linear Regression: Expected to provide a basic prediction with an initial R² score.
Lasso Regression: Expected to enhance performance by simplifying the model, possibly improving the prediction on the test set.
Additional Context:
Dataset contains information about various cars including their names, features, and selling price.
Preprocessing steps include replacing categorical features (Fuel Type, Seller Type, Transmission) with numerical values to make them suitable for regression algorithms.