The NYC taxi dataset is a treasure trove of information that can be used to uncover patterns and make predictions about taxi trips in New York City. In this blog post, I'll walk you through an analysis I conducted on this dataset, focusing on predicting trip duration using machine learning techniques.
DATASET OVERVIEW
The dataset used for this analysis is the yellow taxi trip data from January and February 2022. It includes detailed information about each taxi trip, such as pickup and dropoff locations, trip distances, and timestamps.
DATA PREPROCESSING
The first step in any data analysis project is to clean and preprocess the data. Here’s how I approached it. I loaded the dataset using pandas and explored its structure to understand the columns and data types. I calculated the trip duration in minutes by subtracting the pickup datetime from the dropoff datetime and converting the result to minutes. To remove outliers, I filtered trips with durations between 1 and 60 minutes. I computed the standard deviation and custom percentiles of the trip durations to gain insights into the distribution of trip times.
FEATURE ENGINEERING
To prepare the data for machine learning, I focused on two categorical features (pickup and dropoff locations) and one numerical feature (trip distance). I converted the pickup and dropoff location IDs to string type to facilitate the vectorization process. Using DictVectorizer from scikit-learn, I transformed the categorical features into a numerical format suitable for the regression model.
MODEL TRAINING
With the features prepared, I trained a linear regression model. The target variable for the model was the trip duration in minutes. I trained the linear regression model using the preprocessed data. I predicted the trip durations on the training data and calculated the root mean squared error (RMSE) to measure the average error in minutes between the predicted and actual trip durations.
MODEL VALIDATION
To validate the model, I used the February 2022 dataset. I loaded and preprocessed the validation data in the same way as the training data, ensuring consistency. I used the trained model to predict trip durations on the validation data. The validation RMSE was calculated to assess the model's performance on new, unseen data.
RESULTS
The model's performance was evaluated using RMSE, which measures the average error in minutes between the predicted and actual trip durations. For the validation dataset, the RMSE was 6.69 minutes. This indicates that, on average, the model's predictions are within approximately 6.69 minutes of the actual trip durations.
CONCLUSION
Predicting taxi trip durations in NYC using machine learning can provide valuable insights for various stakeholders, including taxi companies, drivers, and passengers. Although a RMSE of 6.69 minutes suggests there is room for improvement, this initial model provides a solid foundation. Future improvements could include incorporating more features, such as traffic conditions or weather data, and experimenting with more advanced algorithms. Feel free to dive into the dataset, experiment with different models, and let me know your findings or improvements. Happy analyzing!
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