@datascibykashi: A professional Data Science project is more than a Jupyter Notebook. If you want your project to look organized, reproducible, and production-ready, use a structure like this 👇 🗂️ Recommended Structure data-science-project/ │ ├── data/ │ ├── raw/ │ ├── processed/ │ └── external/ │ ├── notebooks/ │ ├── 01_data_collection.ipynb │ ├── 02_data_cleaning.ipynb │ ├── 03_eda.ipynb │ └── 04_modeling.ipynb │ ├── src/ │ ├── data/ │ │ └── make_dataset.py │ ├── features/ │ │ └── build_features.py │ ├── models/ │ │ ├── train.py │ │ └── predict.py │ └── visualization/ │ └── plots.py │ ├── models/ │ └── trained_model.pkl │ ├── tests/ │ └── test_model.py │ ├── reports/ │ └── figures/ │ ├── app/ │ └── app.py │ ├── requirements.txt ├── README.md ├── .gitignore └── config.yaml 🔍 What Does Each Folder Do? 📁 data/ Stores raw, processed, and external datasets. 📓 notebooks/ Used for exploration, experimentation, EDA, and initial modeling. ⚙️ src/ Contains reusable Python code for data processing, feature engineering, modeling, and visualization. 🤖 models/ Stores trained model files and artifacts. 🧪 tests/ Contains tests to make sure your code and ML pipeline work correctly. 📊 reports/ Stores generated charts, figures, and analysis reports. 🌐 app/ Contains the application or API used to serve your model. 📄 README.md Explains the project, setup, methodology, results, and how to run it. 📦 requirements.txt Lists the Python dependencies required to reproduce the project. 🔄 PROFESSIONAL DATA SCIENCE WORKFLOW Problem Definition ⬇️ Data Collection ⬇️ Data Cleaning ⬇️ EDA ⬇️ Feature Engineering ⬇️ Model Training ⬇️ Evaluation ⬇️ Experiment Tracking ⬇️ Model Deployment ⬇️ Monitoring & Maintenance 💡 Remember ❌ final_project.ipynb with everything inside ✅ Organized project with reusable code, documentation, testing, and deployment A good Data Scientist doesn’t just build a model. They build a reproducible system around the model. 🚀 📌 Save this structure for your next Data Science project. #DataScience #Python #MachineLearning #creatorsearchinsights #datascience

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Friday 11 September 2026 08:57:27 GMT