New York Taxi Fare Prediction Model by Kristóf NémethNew York Taxi Fare Prediction Model by Kristóf Németh

New York Taxi Fare Prediction Model

Kristóf Németh

Data Analyst

Data Scientist

Jupyter

Python

Data

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Posted May 7, 2025

Developed a model to predict New York taxi fares using data analysis and linear regression.

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new-york-taxi-fare

New York taxi fare prediction based on location and time.

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         new-york-taxi-fare and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── new-york-taxi-fare   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes new-york-taxi-fare a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations

Notebook Overview

The project includes a Jupyter notebook, new-york-taxi-fare.ipynb, which performs the following tasks:

Data Loading and Cleaning:

Loads a subset of the dataset for analysis.

Cleans the data by removing invalid or outlier records (e.g., negative fares, unrealistic coordinates).

Exploratory Data Analysis (EDA):

Analyzes the distribution of fares and other features.

Visualizes pickup and dropoff locations on maps.

Examines correlations between fare amount and features like distance, time, and passenger count.

Feature Engineering:

Calculates the Haversine distance between pickup and dropoff points.

Extracts temporal features such as pickup hour and year.

Modeling:

Implements a baseline linear regression model using features like distance, pickup hour, and year.

Evaluates the model using metrics such as RMSE and MAPE.

Findings:

Strong correlation between fare amount and distance.

Inflation effects observed over the years.

Limited correlation between fare and passenger count.

Key Findings

Distance: The most significant predictor of fare amount.

Inflation: Fares have increased over the years, likely due to inflation.

Time: Pickup hour shows some correlation with fare amount.

Passenger Count: No significant impact on fare amount.

Future Work

Improve data cleaning by addressing false data points (e.g., trips in water).

Engineer additional features for better model performance.

Experiment with advanced machine learning models for prediction.

Enhance visualizations for deeper insights.