AndreaHobby / BlackTIDES-BlackInDataWeek23

Presentation

BlackTIDES-BlackInDataWeek23 Data Science Project Template

Table of Contents

1. Background
2. Goals
3. Data Collection and Data Cleaning
4. Feature Engineering and Selection
5. Modeling
6. Results
7. Final Thoughts
8. Next Steps
9. References
10. Repo Structure

Background

• Describe the background of the problem you are trying to solve and why it is important.

Goal(s)

• List 2-3 goals

Data Collection and Data Cleaning

• Describe where the data came from
• Describe data shape
• Describe cleaning process
• Discuss if there is missing data and how you approached dealing with it
• Describe data science pipeline (include image if you have one)

Feature Selection/Feature Engineering

• Discuss feature engineering process
• Discuss feature selection process

Modeling

• Discuss model selection process
• Discuss hyperparameter tuning

Results

Discuss Model Evaluation Including:

• Accuracy and other metrics
• Confusion matrix
• Cross-validation
• Bias and variance trade-off
• Overfitting and underfitting
• Feature importance
• Interpretability

Final Thoughts

• Discuss any obstacles in developing the model and how you overcame them
• Pros/Cons of each decision you made throughout the project and what you have learned overall from the project

Next Steps

Here are some options:

• Increase the amount of data
• Incorporate more features
• Experiment with different algorithms
• Validate the model on a different dataset
• Develop a user interface

References

Repo Structure(optional)

├── /data (data)
├── /img (contains all images for repo)
├── 1-DataCleanManip.py
├── 2-DataModeling.py
├── README.md
└── dataset.csv