Automatidata - Taxi Tip Prediction

I developed a machine learning model aimed at predicting whether taxi passengers would give a generous tip, defined as 20% or more. This project showcased my expertise in various aspects of data science, including data preprocessing, feature engineering, and model evaluation using Python. By leveraging these skills, I was able to construct a predictive model that could provide valuable insights into customer tipping behavior in the taxi industry.

• Utilized a dataset of taxi trip records to build a predictive model for tip generosity.

• Employed various machine learning algorithms to achieve the best predictive performance.

1. Python

2. Pandas

3. SQL

4. Jupyter Notebook

1. Conducted data cleaning and preprocessing to prepare the dataset for modeling.

2. Engineered features such as mean trip duration and distance to improve model performance.

3. Employed Random Forest and XGBoost classifiers for modeling and GridSearchCV for hyperparameter tuning.

1. Achieved an F1 score of 0.7136 with the Random Forest model and 0.6955 with the XGBoost model.

2. Identified key features influencing tipping behavior, including VendorID and predicted fare.

1. Propose further feature engineering, such as creating features based on fare rounding behavior.

2. Suggest collecting additional data, such as past tipping behavior, to enhance model performance.

1. Successfully built a predictive model for taxi tip generosity with acceptable performance metrics.

2. Demonstrated proficiency in machine learning techniques and data analysis.

Python, Pandas, NumPy, Scikit-learn, XGBoost, Jupyter Notebook.