Uber Rides Analysis Project

🚖 Uber Cab Data Analysis & Dashboard Project

📌 Project Overview

This project focuses on analyzing an Uber Cab dataset using two different approaches:

1. Python (Data Cleaning + Analysis + Visualization)
2. Interactive Dashboard (Power BI / Tableau style)

The main objective is to uncover insights about ride patterns, revenue, cancellations, and payment methods.

---

📊 Dataset Description

The dataset contains details of Uber rides, including:

• Ride ID, Pickup & Drop Location
• Vehicle Type
• Payment Method
• Ride Status (Completed/Cancelled)
• Revenue, Distance, etc.

---

🛠 Tools & Technologies

• Python → Pandas, Matplotlib, Seaborn, Plotly
• Jupyter Notebook → Data Analysis & Visualization
• Power BI / Tableau → Dashboard for business insights

---

🐍 Python Analysis Workflow

The Python notebook covers:

1. Data Cleaning → Handling missing values & formatting
2. Exploratory Data Analysis (EDA) → Summary statistics & patterns
3. Visualizations →
   • Ride distribution by location
   • Monthly revenue & rides trend
   • Payment method comparison
   • Cancellations analysis
4. Insights → Key findings such as which payment methods generate more revenue, cancellation rates, and ride growth trends.

---

📈 Dashboard Insights

The dashboard highlights:

• KPIs: Total Rides, Completed Rides, Revenue, Cancellation %
• Charts:
  • Line chart → Monthly revenue & rides trend
  • Pie charts → Revenue & rides by payment method
  • Bar charts → Top drop locations
• Filters: Month, Pickup/Drop Location, Vehicle Type, Payment Method

📸 Dashboard Preview