DeepVenture Hub

DeepVenture Hub is an AI-powered idea marketplace and business simulator that empowers entrepreneurs and graduates by providing an interactive platform for idea evaluation, business simulation, personalized microlearning, mentorship matching, and real-time industry analytics.

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Features

🚀 AI-Powered Idea Evaluation

🔍 Submit your business idea and receive an AI-generated evaluation score.

📊 Business Simulation

🎮 Run simulations based on your idea to assess potential success and market viability.

🤝 Mentor Matching

🎯 Get matched with industry experts based on the keywords in your idea description.

📚 Personalized Microlearning Modules

📖 Explore curated microlearning content across topics such as business fundamentals, digital marketing, and financial management.

📈 Real-Time Analytics Dashboard

📊 View up-to-date market trends, funding rounds, and sector-specific insights via a dedicated dashboard.

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Technologies Used

• Python 3.x
• Tensorflow
• Scikit-learn
• Gradio - For interactive idea evaluation UI.
• Streamlit - For building the real-time analytics dashboard.
• Standard Python Libraries (e.g., random) for mock implementations.

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2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Dataset 📂 Dataset Used: Yelp Review Full Dataset 🔗 Dataset Download: (https://huggingface.co/datasets/Yelp/yelp_review_full)

📑 Dataset Columns:

• idea_description – Text of the idea description.
• score – A numeric target score for training the evaluation model. Project Structure

deepventure_hub/
├── requirements.txt             
├── README.md                    
├── deepventure_backend.py        
│    - scikit‑learn functions for idea evaluation
│    - TensorFlow functions for simulation
│    - Microlearning, mentorship, and analytics functions
├── gradio_app.py                
└── streamlit_app.py
└── streamlit_dashboard.py
└── microlearning_app.py
└── output/screenshots
       

Usage 1️⃣ Training and Evaluation

• 💡 Idea Evaluation

• 📌 The function evaluate_idea(description) in deepventure_backend.py:

• Loads (or trains) a scikit-learn model using the stackoverflow_survey_2024.csv dataset.

• Transforms the idea description using a TF-IDF vectorizer.

• Predicts a score between 0 and 100.

📈 Business Simulation

• 📌 The function run_simulation(title, description, score):

• Loads (or trains) a TensorFlow model that takes a normalized idea score as input.

• Outputs a predicted success rate for the idea.
  2️⃣ Launch Gradio Interface

• 📌 To start the interactive idea evaluation interface, run:

python gradio_app.py

🚀 A local web interface will open where you can:

• ✅ Enter your idea title and description.
• ✅ Receive an AI evaluation score.
• ✅ Run a business simulation.
• ✅ Get matched with a mentor.
• ✅ Explore microlearning modules.

3️⃣ Launch Streamlit Dashboard

• 📌 To view the real-time analytics dashboard, run:

streamlit run streamlit_app.py

📊 This dashboard displays:

• ✔️ Market trends
• ✔️ Funding rounds
• ✔️ Sector insights

4️⃣ Launch Streamlit Microlearning Module 📌 To explore the microlearning module and boost your skills, run:

streamlit run microlearning_streamlit.py

📚 This module displays:

• ✔️ Secure Login Interface

• ✔️ Personalized Learning Modules

• ✔️ Interactive Quizzes

• ✔️ Progress Tracking

• ✔️ Certificates

License

📜 This project is licensed under the MIT License.

Contact

📧 For questions or further information and inquiries, please contact: vikhrams@saveetha.ac.in