Flipkart Product Review Analysis & Dashboard

Demo Links

Laptops: https://electronics-review-analysis-laptops-dashboard.streamlit.app
Mobiles: https://electronics-review-analysis-mobiles-dashboard.streamlit.app
Tabs: https://electronics-review-analysis-tabs-dashboard.streamlit.app

An end-to-end data science project for scraping, analyzing, and visualizing customer reviews for Mobiles, Laptops, and Tablets from Flipkart.

Project Structure

Electronics-Review-Analysis/
│
├── Data Analysis/
│ ├── Laptops_Reviews_Analysis.ipynb.ipynb
│ ├── Mobiles_Reviews_Analysis.ipynb
│ └── Tabs_Reviews_Analysis.ipynb
│
├── Datasets/
│ │ ├── flipkart_reviews_laptops.csv
│ │ ├── flipkart_reviews_mobiles.csv
│ │ └── flipkart_reviews_tabs.csv
│ │ ├── laptops_cleaned_reviews.csv
│ │ ├── Mobiles_cleaned_info.csv
│ │ └── Tabs_cleaned_reviews.csv
│ │ ├── Taptops.csv
│ │ ├── Mobiles.csv
│ │ └── Tabs.csv
│
├── Presentation and Document/
│ ├── Electronic_Review_Analysis.pdf
│ └── Review_Analysis_presentation.pptx
│
├── Web Scrapping/
│ ├── Laptops_scrapping.ipynb
│ ├── Mobiles_scrapping.ipynb
│ └── Tabs_scrapping.ipynb
│
├── Dashboard/
│ ├── Laptops.py
│ ├── Mobiles.py
│ └── Tabs.py
│
├── requirements.txt
└── README.md

Data Collection & Preprocessing

Source

Reviews are scraped using requests and BeautifulSoup from Flipkart.
Reviews are categorized into:
📱 Mobiles – 61 products
💻 Laptops – 74 products
📟 Tablets – 53 products

⚙️ Initial Preprocessing

Assigned unique Product IDs
Converted rating and review columns to numeric
Extracted product-specific details:
Mobiles: Model, Colour, Storage
Laptops/Tablets: Brand, Model, Specifications

Text Cleaning

Lowercased text
Removed punctuation, stopwords, numbers, emojis, and filler text like "READ MORE"
Stripped extra whitespace

Handling Missing Data

Filled missing ratings with mean values
Used Product ID mapping to fill missing model and rating info
Dropped irrelevant columns and reordered
Replaced remaining nulls with "Unknown"

Output

Cleaned CSVs saved for Mobiles, Laptops, and Tablets

Exploratory Data Analysis (EDA)

Mobile EDA Histograms for rating, reviews, and price
Price segmentation: Budget / Mid-Range / Premium
Top 10 most-reviewed smartphones
Sentiment vs Rating correlation
LDA Topic Modeling

Laptop EDA
Price per GB RAM/Storage
Engagement Score analysis
Brand-wise comparison using pie, bar, and radar charts
KMeans Clustering
Keyword & Topic Modeling Visuals

Tablet EDA
Feature-based review keyword extraction (e.g., battery, display, camera)
Popularity Score metric
Parallel coordinates & 3D scatter plots
Product clustering using KMeans

Interactive Dashboard (Streamlit)

Three dashboards are available with filtering options:

📱 Mobiles: Filter by Storage, Color, Price, Rating, Sentiment
💻 Laptops: Filter by Brand, Processor, RAM, Price, Reviews
📟 Tablets: Filter by Screen Size, Battery Life, Brand, Rating

Tech Stack

Backend: pandas, numpy, matplotlib, seaborn, nltk, scikit-learn
Frontend: Streamlit

User Experience

Real-time filtering
Responsive UI
Interactive plots
Actionable insights for:
Analysts 📊
Marketers 📢
Consumers 🛍️

Learning Outcomes

Practical experience in web scraping
Deep understanding of data preprocessing for textual data
Executed EDA, sentiment analysis, clustering, and topic modeling
Built interactive dashboards using Streamlit
Improved documentation and storytelling with data

Future Scope

Expand to other platforms (Amazon, Snapdeal)
Integrate image-based sentiment analysis
Add real-time review tracking

Sreenshots

Requirements

Install dependencies: pip install -r requirements.txt

Run Dashboard

cd dashboard/
streamlit run Mobiles.py
streamlit run Laptops.py
streamlit run Tabs.py

Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you’d like to change.

License

This project is licensed under the MIT License – see the LICENSE file for details.

👨‍💻 Author

Developed by [J V Purushotham]
Contact: jvpurushotham31@gmail.com