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📜 Project Title

🎯 AIM

📊 DATASET LINK

https://www.google.com

📓 KAGGLE NOTEBOOK

https://www.google.com

??? Abstract "Kaggle Notebook"

<iframe 
    src="https://www.kaggle.com/embed/avdhesh15/cvd-risk-prediction-system?kernelSessionId=218959248" 
    height="600" 
    style="margin: 0 auto; width: 100%; max-width: 950px;" 
    frameborder="0" 
    scrolling="auto" 
    title="cvd-risk-prediction-system">
</iframe>

⚙️ TECH STACK

Category Technologies
Languages Python, JavaScript
Libraries/Frameworks TensorFlow, Keras, Flask
Databases MongoDB, PostgreSQL
Tools Docker, Git, Jupyter, VS Code
Deployment AWS, Heroku

📝 DESCRIPTION

!!! info "What is the requirement of the project?" - Write the answer here in simple bullet points.

??? info "How is it beneficial and used?" - Write the answer here in simple bullet points.

??? info "How did you start approaching this project? (Initial thoughts and planning)" - Write the answer here in simple bullet points.

??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)." - Write the answer here in simple bullet points.

🔍 PROJECT EXPLANATION

🧩 DATASET OVERVIEW & FEATURE DETAILS

??? example "📂 dataset.csv"

- There are X features in the dataset.csv

| Feature Name | Description |   Datatype   |
|--------------|-------------|:------------:|
| feature 1    | explain 1   | int64/object |

??? example "🛠 Developed Features from dataset.csv"

| Feature Name | Description | Reason   |   Datatype   |
|--------------|-------------|----------|:------------:|
| feature 1    | explain 1   | reason 1 | int64/object |

🛤 PROJECT WORKFLOW

!!! success "Project workflow"

``` mermaid
  graph LR
    A[Start] --> B{Error?};
    B -->|Yes| C[Hmm...];
    C --> D[Debug];
    D --> B;
    B ---->|No| E[Yay!];
```

=== "Step 1" - Explanation

=== "Step 2" - Explanation

=== "Step 3" - Explanation

=== "Step 4" - Explanation

=== "Step 5" - Explanation

=== "Step 6" - Explanation

🖥 CODE EXPLANATION

=== "Section 1" - Explanation

⚖️ PROJECT TRADE-OFFS AND SOLUTIONS

=== "Trade Off 1" - Describe the trade-off encountered (e.g., accuracy vs. computational efficiency). - Explain how you addressed this trade-off (e.g., by optimizing hyperparameters, using a more efficient algorithm, etc.).

=== "Trade Off 2" - Describe another trade-off (e.g., model complexity vs. interpretability). - Explain the solution (e.g., by selecting a model that balances both aspects effectively).

🖼 SCREENSHOTS

!!! tip "Visualizations and EDA of different features"

=== "Image Topic"
    ![img](https://assets.ltkcontent.com/images/103034/line-graph-example_27c5571306.jpg)

??? example "Model performance graphs"

=== "Image Topic"
    ![img](https://assets.ltkcontent.com/images/103029/bar-graph-example_27c5571306.jpg)

📉 MODELS USED AND THEIR EVALUATION METRICS

Model Accuracy MSE R2 Score
Model Name 95% 0.022 0.90
Model Name 93% 0.033 0.88

✅ CONCLUSION

🔑 KEY LEARNINGS

!!! tip "Insights gained from the data" - Write from here in bullet points

??? tip "Improvements in understanding machine learning concepts" - Write from here in bullet points

🌍 USE CASES

=== "Headline 1" - Explain your application

=== "Headline 2" - Explain your application

🔗 USEFUL LINKS

=== "Deployed Model" - https://www.google.com

=== "GitHub Repository" - https://www.google.com

=== "Binary Model File" - https://www.google.com