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📜 Project Title

🎯 AIM

https://www.google.com

📓 KAGGLE NOTEBOOK

https://www.google.com

Kaggle Notebook

⚙️ 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

What is the requirement of the project?

  • Write the answer here in simple bullet points.
How is it beneficial and used?
  • Write the answer here in simple bullet points.
How did you start approaching this project? (Initial thoughts and planning)
  • Write the answer here in simple bullet points.
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

📂 dataset.csv
  • There are X features in the dataset.csv
Feature Name Description Datatype
feature 1 explain 1 int64/object
🛠 Developed Features from dataset.csv
Feature Name Description Reason Datatype
feature 1 explain 1 reason 1 int64/object

🛤 PROJECT WORKFLOW

Project workflow

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

🖥 CODE EXPLANATION

  • Explanation

⚖️ PROJECT TRADE-OFFS AND SOLUTIONS

  • 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.).
  • 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

Visualizations and EDA of different features

img

Model performance graphs

img

📉 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

Insights gained from the data

  • Write from here in bullet points
Improvements in understanding machine learning concepts
  • Write from here in bullet points

🌍 USE CASES

  • Explain your application
  • Explain your application