bigbasket-

🛒📦 BigBasket Product Data Analysis 🧠📊

Welcome to my exploratory data analysis (EDA) project on the BigBasket 🧺 product dataset!
This project dives into India’s leading online grocery platform to uncover hidden insights 🕵️‍♂️ using data visualization 📈 and Python 🐍.

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📁 Dataset Overview 🔍

The dataset contains details about 27,555 products sold on BigBasket. Here's a quick snapshot:

🔑 Column Name	📋 Description
🏷️ product	Product name
🧰 category	Broad product category
🔖 sub_category	Specific product category
🏭 brand	Brand name
💵 sale_price	Final selling price (after discount)
💰 market_price	Original price before discount
🧾 type	Item type
⭐ rating	Customer rating (scale: 1 to 5)
📝 description	Short description of the product

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🧼 Data Cleaning & Preprocessing 🧹

✅ Checked for missing values
✅ Converted ratings to integers
✅ Created new features:

• 🔻 discount = market_price - sale_price
• 📉 discount_percent = (discount / market_price) * 100

✅ Applied Label Encoding 🎫 to categorical columns for correlation analysis

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📊 Exploratory Data Analysis (EDA) 🔬

🔟 Top 10 Categories by Product Count

📊 Bar chart of top categories by number of products

💸 Average Sale Price by Category

💡 Identify which categories are generally more expensive

⭐ Rating Distribution

🧍 Understand customer satisfaction

🎯 Average Discount by Category

🤑 See where the best deals are offered!

🧠 Correlation Heatmap

🧩 Analyze relationships between price, discount, and rating

🏆 Top & 🐢 Least Sold Products

📈 Products with the highest and lowest sale prices

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📈 Visualizations Used 🖼️

📊 Bar Charts – Category count, Avg price, Avg discount
🥧 Pie Chart – Category distribution
📦 Box Plot – Price spread across categories
🔁 Scatter Plot – Rating vs price, sale vs market price
🔎 Joint Plot – Deep dive on sale vs market
🔥 Heatmaps – Price & rating correlation
👥 Pairplot – Relationships among features
📉 Distplots – Sales price distribution

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🧠 Key Insights & Findings 💡

• 🥇 Beauty & Hygiene is the most popular category
• 🛍️ Discounts go up to 50%+ on many products
• ⭐ Ratings are mostly between 3.5 – 4.5
• 💎 Highest priced products were in Men’s Grooming and Cookware
• 🥬 Least expensive ones were in Hair Care and Herbs & Seasonings

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🛠️ Tools & Technologies 🧰

• Python 🐍
• Pandas & NumPy 📐
• Matplotlib & Seaborn 📊
• Google Colab ☁️
• Scikit-learn 🔬 (Label Encoding)

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🔚 Conclusion 📌

This EDA provided valuable insights about BigBasket’s product offerings, pricing, discounts, and customer satisfaction. This data can be used for:

• 🎯 Product Recommendation Systems
• 🛒 Customer Segmentation
• 💸 Dynamic Pricing Models

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🙋‍♂️ Author

Adarsh Kumar
📫 adar843462@gmail.com
🔗 www.linkedin.com/in/adarsh-kumar-29a761353

💻 Aspiring Data Scientist | Python • SQL • Power BI • Excel

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