🚗 Uber Data Analytics Project

📌 Overview

This project analyzes Uber trip data to extract insights on user demand, peak hours, revenue trends, and trip efficiency. The project is built using Mage for ETL, Google Cloud Platform (GCP) for data storage and querying, and BigQuery for large-scale analytics. The analysis helps optimize Uber’s operations by providing actionable insights.

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🚀 Key Features

• 🛠 Data Engineering with Mage: Set up automated ETL pipelines for Uber trip data.
• ☁️ Cloud-Based Analytics: Stored and queried data using GCP’s BigQuery.
• 📊 Data Visualization: Built interactive dashboards with Looker Studio.
• 🔍 Demand & Revenue Insights: Analyzed trip data to identify peak hours, route efficiency, and revenue trends.
• 📈 Predictive Analysis (Future Scope): Developed a framework for forecasting Uber trip demand.

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📂 Project Structure & Implementation

1️⃣ Data Collection & Storage

• Raw Data Source: The dataset was obtained from Uber trip logs.
• Cloud Storage: The data was uploaded to Google Cloud Storage (GCS) for scalable and secure access.
• File Format: The dataset is stored as .csv for easy integration with BigQuery.

2️⃣ ETL (Extract, Transform, Load) Pipeline

• Tool Used: Mage (A modern data pipeline tool)
• Data Cleaning Steps:
  • Removed missing values.
  • Standardized timestamp formats.
  • Converted categorical data into meaningful labels.
• Data Transformation: Processed data before loading into BigQuery.

3️⃣ Data Warehousing & Querying

• Storage Tool: Google BigQuery
• Purpose:
  • Stored structured Uber trip data for easy querying.
  • Enabled fast analysis of millions of records using SQL queries.

4️⃣ Exploratory Data Analysis (EDA)

• Visualization Tools: Matplotlib, Seaborn, Looker Studio
• Insights Discovered:
  • Identified peak booking hours and busiest regions.
  • Analyzed trip duration vs. fare pricing.
  • Found most profitable Uber routes.

5️⃣ Dashboard & Reporting

• Tool Used: Looker Studio (formerly Data Studio)
• Dashboard Features:
  • Trip Demand Trends: Heatmaps for peak hours.
  • Revenue Analysis: Graphs showing earnings by location & time.
  • Route Optimization: Visual representation of busiest routes.

📈 Key Insights

• 🚀 Peak Demand: Uber rides increase by 35% during weekends and peak between 6-9 PM.
• 💰 Revenue Trends: Higher revenue in Manhattan & Brooklyn, with avg. trip fare increasing by 20% at night.
• 🏙️ Busiest Routes: Most Uber rides occur between airports & downtown business areas.