This project predicts the prices of motorbikes in Europe based on various features like brand, engine capacity, type, and more. It uses machine learning techniques for regression analysis to provide accurate predictions.
ML Project.ipynb: Jupyter notebook containing the code for data preprocessing, training, and testing the model.europe-motorbikes-zenrows.csv: Dataset containing motorbike details and prices..ipynb_checkpoints/: Auto-saved checkpoints for the notebook.
- Data Preprocessing: Handles missing values, encodes categorical data, and scales numeric features.
- Model Training: Utilizes regression models like Linear Regression, Random Forest, and XGBoost.
- Model Evaluation: Assesses the performance using metrics like RMSE and R².
- Price Prediction: Provides an interface to input bike details and predict prices.
- Python
- Jupyter Notebook
- Pandas
- Scikit-learn
- Matplotlib/Seaborn
- ZenRows API (for dataset extraction)