This project aims to analyze and predict the number of journeys made on Transport for London's (TfL) network between 2010 and 2022. The project explores various machine learning models to identify the best-performing model for accurate predictions. The dataset is from Datacamp (https://www.datacamp.com/datalab/datasets). The workflow includes data preprocessing, feature engineering, exploratory data analysis, model evaluation, and hyperparameter tuning. Visualization techniques are employed to gain insights into the data and model performance.
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This project analyzes and predicts the number of journeys on Transport for London's (TfL) network. It involves a comprehensive workflow that includes data preprocessing, feature engineering, exploratory data analysis (EDA), model evaluation, and hyperparameter tuning to identify the best-performing model for accurate journey predictions.
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tekolujoe/TfL-Journey-Analysis-and-Prediction
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This project analyzes and predicts the number of journeys on Transport for London's (TfL) network. It involves a comprehensive workflow that includes data preprocessing, feature engineering, exploratory data analysis (EDA), model evaluation, and hyperparameter tuning to identify the best-performing model for accurate journey predictions.
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