Every Data Science project follows a structured pipeline:
1. Data Collection
Sources include:
- CSV files
- Databases
- APIs
- Sensors
2. Data Cleaning
- Remove missing values
- Fix incorrect entries
- Normalize formats
3. Data Exploration (EDA)
- Understand distribution
- Identify trends
- Detect outliers
4. Modeling
- Apply algorithms
- Train models
5. Evaluation
- Check accuracy
- Improve model
6. Deployment
- Use model in real-world systems