📊 Blinkit Analysis in Python
Welcome to the Blinkit Data Analysis Project – a Python-powered deep-dive into the operations and trends of Blinkit, India’s leading quick-commerce platform. In this project, we clean, process, and visualize Blinkit's dataset to uncover business insights and user behavior patterns that shape the future of hyperlocal delivery.
🚀 Project Overview
🔍 Objective: To analyze Blinkit's performance metrics using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. The goal is to explore customer preferences, product demand trends, delivery efficiency, and sales performance.
🧰 Tech Stack
Language: Python
Data Handling: Pandas, NumPy
Visualization: Matplotlib, Seaborn
Notebook: Jupyter Notebook / Google Colab
Output: Interactive HTML Report
📂 Features
📦 Product Category-wise Insights
📈 Time Series & Seasonal Trends
🚚 Delivery Time Analysis
🧍 User Behavior Patterns
💰 Sales & Revenue Trends
🧠 Learnings
Hands-on practice in data cleaning, aggregation, and EDA
Improved storytelling with data visualization
Insights into quick-commerce business models
Experience publishing interactive analysis reports
🔮 Future Enhancements
Add ML-based demand prediction
Integrate live data scraping
Create dashboards with Plotly or Tableau
👩💻 About the Author
Bishakha Kapoor 🎓 Commerce Student | 📊 Aspiring Data Analyst | 🎯 Curious Learner
Coming from a non-tech background, I transitioned into data analytics by combining my business acumen with technical skills. This project is a reflection of my passion for turning raw data into actionable insights. I enjoy analyzing real-world problems, visualizing patterns, and crafting stories that numbers tell.
🔗 Let's Connect
📍 Location: Bihar, India
🧠 Learning: SQL, Python, Power BI, Excel