Car Predictions_Kaggle Competition

Here are some key insights and the final submission details from my journey:

Key Insights:

1. Data Preprocessing:
   • Handling Missing Values: Implemented strategies to fill missing values in the fuel_type and engine columns using a custom function that mapped the best estimates based on available data.
   • Feature Engineering: Calculated the age of the cars by subtracting the model_year from the current year and created a new feature log_price by applying a logarithmic transformation to the price column for better model performance.
2. Data Cleaning:
   • Transmission Types: Segregated data into Automatic (A/T) and Manual (M/T) transmissions for better categorization.
   • Accident Data: Mapped accident data into binary values to indicate whether the car was involved in an accident or not.
3. Exploratory Data Analysis:
   • Visualizations: Utilized seaborn and matplotlib to create pair plots, histograms, and regression plots to understand the relationships between features and the target variable.
   • Correlation Analysis: Generated heatmaps to identify correlations between numerical features.
4. Model Building:
   • Pipeline Creation: Built a pipeline using ColumnTransformer for preprocessing and RandomForestRegressor for regression.
   • Model Training: Trained the model on a sample of the training data to optimize performance and reduce computational load.
   • Evaluation: Achieved an RMSE (Root Mean Squared Error) of 51355.79 on the sample data, indicating the model's predictive accuracy.
5. Final Submission:
   • Predictions: Applied the trained model to the test data and generated predictions for car prices.
   • Submission: Prepared the final submission file with predicted prices for 125,165 cars.

Final Submission Snapshot:

idprice037625.60123900.31227846.07......12516440589.18

Tools & Libraries Used:

• Python: For data processing and model building.
• Pandas & NumPy: For data manipulation and numerical computations.
• Seaborn & Matplotlib: For data visualization.
• Scikit-Learn: For machine learning model implementation.

Participating in this competition was a fantastic learning experience, and I am excited to apply these insights to future projects. A big thank you to the Kaggle community for providing such a valuable platform for learning and growth.

Feel free to connect with me if you have any questions or would like to discuss more about this project!