🏋️‍♀️ HealthKart Influencer Campaign Dashboard | Data Analytics Project

This Streamlit-based interactive dashboard helps track and analyze the performance of influencer marketing campaigns at HealthKart across platforms like Instagram, YouTube, and Twitter.
It offers ROI estimation, performance insights, payout tracking, and chatbot-based queries—all powered by simulated campaign data.

🔗 Live Dashboard: Click here to explore the deployed dashboard
📄 Project Documentation: Click here to view the full project documentation (PDF)

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✨ Features

• 📊 Interactive multi-page Streamlit dashboard
• 👥 Influencer filtering by platform, category, and product
• 📈 Campaign overview with KPIs like Revenue, ROAS, Orders
• 💰 Payout tracker with post/order-based calculation
• 🔮 ROI forecasting using regression
• 🤖 Inbuilt natural-language chatbot
• 🧪 Realistic data simulation using Faker

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🧰 Technologies Used

Category	Technology / Library	Purpose
💻 Frontend	Streamlit	Interactive UI for dashboard
📊 Visualization	Plotly Express	Graphs, charts, and plots
🧮 Modeling	Scikit-learn	Simple regression for ROI forecasting
📦 Data Handling	Pandas, NumPy	Data cleaning, merging, and transformation
🤖 NLP/Chatbot	Regex, basic logic	Intent-based rule matching for chatbot
🧪 Data Creation	Faker, random	Simulate realistic influencer campaign data
🐍 Language	Python 3.x	Core programming language

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🔍 How It Works

This dashboard uses simulated datasets representing influencers, posts, tracking data, and payouts.
Users can navigate through five main pages:

1. Campaign Overview: Platform-wide KPIs, product revenue, and ROAS distribution.
2. Influencer Insights: Breakdown of performance by influencer with pie charts.
3. Payout Tracker: View how payouts are distributed based on posts/orders.
4. Forecast ROI: Predict ROI based on follower count and engagement.
5. Chatbot: Ask natural-language questions like "Top influencers by ROAS".

Filtering options are available in the sidebar to slice data by platform, category, and product.

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📷 Preview

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📁 Folder Structure

HealthKartProject/
├── README.md                  # Project overview and usage guide
├── Project_documentation.pdf            # Full project documentation/report
├── data/                      # Simulated datasets
│   ├── data_simulation.py
│   ├── influencers.csv
│   ├── posts.csv
│   ├── payouts.csv
│   └── tracking_data.csv
├── app.py                 # Main dashboard script
├── chatbot.py             # Chatbot logic and data interface
├── assets/                # Visuals and assets
│   ├── preview_1.png      # Preview of Page 1   
│   ├── preview_2.png      # Preview of Page 2   
│   ├── preview_3.png      # Preview of Page 3   
│   ├── preview_4.png      # Preview of Page 4   
│   ├── preview_5.png      # Preview of Page 5   
│   └── healthkart_logo.png   # Logo of the brand
└── requirements.txt           # Python dependencies

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💡 Use Cases

• 💼 For marketing teams to measure influencer ROI
• 📊 For data analysts to derive campaign insights
• 🧑‍💻 For students learning data visualization & Streamlit
• 🧪 For simulation & experimentation without real campaign data

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⚙️ Setup Instructions

1. Clone this Repository

   git clone https://github.com/Anushka-Sharma-008/HealthKartProject.git
   cd healthkart-dashboard

2. Install dependencies

   pip install -r requirements.txt

3. Simulate data

   python data_simulation.py

4. Run the Dashboard

   streamlit run app.py

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🧾 Assumptions Made

• Influencer payout basis is either per post or per order
• Campaigns, products, and platforms are predefined (simulated)
• ROAS = Revenue / Total Payout (with NaN and ∞ handled)
• Forecast model is simple linear regression based only on followers
• One influencer can have multiple posts and campaign entries
• Data is synthesized using Faker and does not represent actual HealthKart campaigns

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📊 Insights Summary

• 💰 Total Revenue: Derived from tracking data across campaigns
• 🧑‍🤝‍🧑 Top Influencers: Identified by highest ROAS and total orders
• 💸 Cost Efficiency: Campaign ROAS varies widely by platform
• 📦 Product Insights: Certain products drive significantly higher revenue
• 🛠️ Chatbot Utility: Offers quick query handling for common business questions

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🙋‍♀️ Author

Anushka Sharma
🌐 LinkedIn • 🐱 GitHub 🎓 Learning Data Science, Analytics & Machine Learning

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⭐ Show Your Support

If you found this project helpful or inspiring:

• ⭐ Star this repository
• 🛠️ Fork it to build upon or adapt it for your own use
• 💬 Share feedback or suggestions via Issues/Discussions