Deep-Learning-Projects

NYC Taxi Fare Prediction

Project Overview

This project aims to predict taxi fares in New York City using deep learning techniques. It's based on a Kaggle competition dataset containing over 55 million taxi rides.

Dataset

The dataset includes the following features:

• pickup_datetime: Timestamp of the ride start
• pickup_longitude, pickup_latitude: Coordinates of the pickup location
• dropoff_longitude, dropoff_latitude: Coordinates of the dropoff location
• passenger_count: Number of passengers

Target variable: fare_amount (USD)

Methodology

1. Data Preprocessing:

   • Handling missing values and outliers
   • Feature engineering (e.g., extracting time-based features, calculating distances)
   • Normalization of numerical features

2. Exploratory Data Analysis:

   • Visualization of fare distributions, popular routes, time-based patterns

3. Model Development:

   • Built a deep neural network using TensorFlow
   • Architecture: [Brief description of your model architecture]
   • Hyperparameter tuning using Random Forest Regressor

4. Evaluation:

   • Metrics used: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
   • Cross-validation strategy:

Results

• Achieved a Mean Absolute Error of $X.XX on the test set
• [Any other notable results or insights]

Technologies Used

• Python
• TensorFlow/PyTorch
• Pandas, NumPy
• Matplotlib, Seaborn for visualization
• Scikit-learn for preprocessing and evaluation

Setup and Installation

git clone https://github.com/yourusername/nyc-taxi-fare-prediction.git
cd nyc-taxi-fare-prediction
pip install -r requirements.txt
Loy, J. (2019). Neural network projects with Python : the ultimate guide to using Python to explore the true power of neural networks through six projects. http://cds.cern.ch/record/2671438