1. Learn to upload a dataset to postgresql and query from this source.
2. Work on files structure for projects
3. Work on markdown skills :-)
4. Practice classification models available in sckit-learn.

https://archive.ics.uci.edu/ml/datasets/Bank+Marketing

1. The dataset used in the analysis is queried from a postgresql database. The data are not big enough to require this, however it was good practice. Alternatively, source data can be found in the /Datafolder

2. Directions on the appropriate order to run the files are present in the /Directions folder text file.

• Logistic Regression
• K Nearest Neighbors
• Random Forrest Classifier

1. The data are contained in csv format /Data/bank-full.csv

• I loaded it into a postgre database for practice, feel free to just use the data from the file.

2. Run /scripts/connect.py this will run the data load.
3. Open the Jupyter Notebook file /Notebooks/Bank-Marketing-Project-EDA to view the analysis performed on the data before the classification models were run.
4. Run /scripts/data-transformation.py to clean the data into usable format.
5. Run the various classification models in /Models/classification models.py