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New York City Taxi Fare Prediction

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The goal of this project is to predict with reasonable accuracy, the taxi fares of new york city based on the dataset provided by this kaggle competition: https://www.kaggle.com/competitions/new-york-city-taxi-fare-prediction/overview

The dataset consists of following csv files:

  • train.csv: Input features and target fare_amount values for the training set (about 55M rows)
  • test.csv: Input features for the test set (about 10K rows). Your goal is to predict fare_amount for each row.
  • sample_submission.csv: a sample submission file in the correct format (columns key and fare_amount). This file 'predicts' fare_amount to be $11.35 for all rows, which is the mean fare_amount from the training set.

Following are the features of the training and test dataset:

  • pickup_datetime - timestamp value indicating when the taxi ride started.
  • pickup_longitude - float for longitude coordinate of where the taxi ride started.
  • pickup_latitude - float for latitude coordinate of where the taxi ride started.
  • dropoff_longitude - float for longitude coordinate of where the taxi ride ended.
  • dropoff_latitude - float for latitude coordinate of where the taxi ride ended.
  • passenger_count - integer indicating the number of passengers in the taxi ride.

The target column fare_amount is a float dollar amount of the cost of the taxi ride. This value is only in the training set; this is what we are predicting in the test set

Import Libraries

Lets start by importing all the necessary python libraries and packages required for this project:

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from sklearn.cluster import KMeans
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler

%matplotlib inline