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Is your feature request related to a problem? Please describe. This feature request addresses the need for a predictive model that estimates the selling price of used cars based on various features. Currently, individuals and car dealers often face challenges in determining accurate selling prices due to multiple influencing factors such as fuel type, seller type, transmission, and other car specifications. This makes it difficult for sellers to estimate a fair market value, potentially leading to undervalued sales or overpriced listings that deter buyers.
Describe the solution you'd like I would like to build a machine learning solution that utilizes regression models such as Linear Regression and Lasso Regression to predict the selling price of cars based on features like fuel type, seller type, and transmission. The solution should provide a user-friendly interface where users can input car specifications and receive a predicted selling price based on the trained model.
Describe alternatives you've considered Manual Pricing Tools: Some online car valuation tools allow users to estimate the price of their car manually, but these do not leverage the power of machine learning and may not be as accurate. Other Regression Models: Consideration of additional regression models such as Decision Trees or Random Forest could offer more flexibility, especially with non-linear relationships between features and target variables.
Approach to be followed (optional) Data Collection: Use the dataset containing car details and selling prices to train the model. Data Preprocessing: Check for null values and handle missing data. Perform label encoding for categorical variables (e.g., Fuel_Type, Seller_Type, and Transmission). Model Training: Split the data into training and test sets. Train a Linear Regression model and evaluate performance using the R-squared score and actual vs predicted scatter plots. Train a Lasso Regression model to handle potential overfitting and assess the results using similar metrics. Evaluation: Visualize the results of both models using scatter plots and R-squared scores to assess model accuracy. Deployment: Provide an interactive interface (e.g., web or desktop) where users can input their car's details and get a predicted selling price.