kaggle-new-york-city-taxi-fare-prediction

My solution of the New York City Taxi Fare Prediction competition of Kaggle.

To know more about the competition please follow the link: https://www.kaggle.com/c/new-york-city-taxi-fare-prediction

Data:

The training data contains in 'train.csv' file and the testing data contains in the 'train.csv' file and the testing data contains in the 'test.csv' file. I am also adding a file 'data_description.txt' which contains the explanations of the fields available in the other data files.

To download the data please follow the links:

1. train.csv (https://www.kaggle.com/c/new-york-city-taxi-fare-prediction/download/train.csv)
2. test.csv (https://www.kaggle.com/c/new-york-city-taxi-fare-prediction/download/test.csv)

Install:

To run this notebook:

1. Clone the repository.
2. Install virtualenv.
3. Navigate to the directory where you unzipped or cloned the repo and create a virtual environment with virtualenv env.
4. Activate the environment with source env/bin/activate
5. Install the required dependencies with pip install -r requirements.txt.
6. Execute ipython notebook from the command line or terminal.
7. When you're done deactivate the virtual environment with deactivate.

Dependencies:

• NumPy
• IPython
• Pandas
• SciKit-Learn
• Seaborn
• Matplotlib
• Keras

I have been using kaggle kernels for this project. Here's my notebook : https://www.kaggle.com/rishabh254/nyc-ola/notebook

Code:

main.ipynb : contains the whole code
eda.ipynb : contains data exploration
cleaning.ipynb : contains code to remove outliers
feature_engineering.ipynb : contains code to extract new features
model1.ipynb : model for rides with variable fare
model2.ipynb : model for rides with almost constant fare

Goal:

The goal for the competition is to predict the fare amount (inclusive of tolls) for a taxi ride in New York City given the pickup and dropoff locations.