NYC Ridesharing Data Analysis Dashboard

A comprehensive interactive dashboard for analyzing NYC ridesharing data with advanced visualizations, hotspot mapping, and predictive analytics.

🔗 Try it out here → NYC Ride Sharing Analysis Website

🚀 Features

• Interactive Hotspot Mapping: Visualize pickup and drop-off locations with clustering and heatmaps
• Advanced Analytics: User behavior analysis, ride distribution patterns, and correlation insights
• Predictive Intelligence: Demand forecasting and business intelligence recommendations
• Real-time Filtering: Filter by weeks, location types, and other parameters
• Professional Visualizations: High-quality charts and interactive maps

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Quick Setup

# Clone the repository
git clone https://github.com/leahdsouza/nyc-ridesharing-data-analysis-dashboard.git
cd nyc-ridesharing-data-analysis-dashboard

# Install dependencies
make install
# or
pip install -r requirements.txt

# Setup development environment
make setup

🚀 Usage

Run the Interactive Dashboard

make run-dashboard
# or
cd src && streamlit run streamlit_hotspots.py

Run Data Analysis

make run-analysis
# or
cd src && python uber_analysis.py

Available Commands

make help          # Show all available commands
make install       # Install dependencies
make setup         # Setup development environment
make run-dashboard # Run the Streamlit dashboard
make run-analysis  # Run data analysis
make clean         # Clean generated files

📊 Dashboard Features

🗺️ Hotspot Mapping

• Interactive map with pickup (blue) and drop-off (red) locations
• Clustering for better performance with large datasets
• Heatmap overlay showing ride density
• Layer controls for toggling different data views

📈 Analytics Dashboard

• Weekly Trends: Animated charts showing ride patterns over time
• Distribution Analysis: Pie charts and histograms of ride types
• User Intelligence: User behavior analysis and engagement metrics
• Predictive Insights: Demand forecasting and business recommendations

🎛️ Interactive Controls

• Week selection for time-based filtering
• Location type filtering (pickups vs drop-offs)
• Clustering threshold adjustment
• Multiple analysis views

📋 Data Requirements

The dashboard expects the following CSV files in the data/ directory:

• user_summary.csv: User-level metrics and statistics
• ride_summary.csv: Location data for mapping and visualization

🔧 Development

Project Structure

• src/: Contains all Python source code
• data/: Contains input data files (CSV format)
• assets/: Contains generated visualizations and outputs
• docs/: Future documentation directory

Dependencies

Key dependencies include:

• streamlit: Web application framework
• plotly: Interactive visualizations
• folium: Interactive maps
• pandas: Data manipulation
• numpy: Numerical computing
• matplotlib & seaborn: Static visualizations

📈 Key Metrics

The dashboard analyzes and visualizes:

• 15,120 total rides across the dataset
• 30,240 location points (origins and destinations)
• 100% heavy users (users with >1 ride per week)
• 99 weeks of comprehensive data coverage

🎯 Business Intelligence

The dashboard provides strategic insights including:

• User engagement patterns and retention analysis
• Geographic hotspots for marketing and operations
• Demand forecasting and growth predictions
• Competitive advantage analysis
• Revenue potential calculations