AutoPricePrediction-ML-Project

Used Automobile Price Prediction

Problem Statement

The problem at hand is to predict the price of a used car based on various features such as year of manufacture, km driven, fuel type, etc.

Dataset

The dataset used is a collection of used car prices and their corresponding features.

Data Type

The data includes 10 features and 1 target column (price). The features include

• year of manufacture
• km driven
• fuel type
• seller type
• transmission
• owner count
• seats
• torque
• engine
• mileage

Algorithms Used

In this project, the following algorithms were used:

• Linear Regression
• Random Forest Regression
• XGBoost
• Lasso Regression

Data Preprocessing

The dataset was preprocessed using pandas profiling to get an in-depth understanding of the data and identify any missing or duplicate values, along with the distributions of the features.

Training Model

The algorithms were trained on the dataset using a train-test split, with 80% of the data used for training and 20% for testing. The best model was selected based on performance metrics such as root mean squared error (RMSE) and R-squared score. Hyperparameter tuning was also performed using RandomizedSearchCV to get the best hyperparameters for the Random Forest Regression model.

Outputs

The results of the models are shown below:

The Linear Regression

• RMSE of 143619.53
• R2 score of 0.66

The Random Forest

• RMSE of 33524.23
• R2 score of 0.98

The XGBoost

• RMSE of 89372.12
• R2 score of 0.87

The Lasso Regression

• RMSE of 143619.53
• R2 score of 0.66

Random Forest Regression (after hyperparameter tuning)

• Best hyperparameters: {'n_estimators': 500, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': None}
• RMSE: 77736.08259917068
• R2 score: 0.8984801752097382

Conclusion

Based on the results, the Random Forest Regression model performed better than the Linear Regression model, with a lower RMSE and higher R-squared score. The model can be used to predict the prices of used cars based on their features, with a high degree of accuracy.