Analyzing bank marketing data with scikit-learn

This repo contains 2 notebooks which demonstrate how one can approach a classification machine learning problem using scikit-learn. The models are trained on data on direct marketing campaigns (phone calls) of a Portuguese banking institution (Data source: UCI Machine Learning Repository). The classification goal is to predict if the client will subscribe a term deposit (variable y).

For the hands-on workshop, please use the notebook titled 2-classification-model-workshop-starter-code.ipynb in the notebooks directory. You can also refer to the 3-classification-model-workshop-solutions.ipynb if you'd like.

To see the complete process of approaching a machine learning problem, please see the full-analysis notebook. It illustrates the process of training, evaluating, tuning/optimizing and interpreting a logistic regression model

The 4-comparing-models notebook demonstrates how you can follow a similar pattern for training a model using Logistic Regression Classifier, Naive Bayes Classifier, KNearest Neighbour Classifier, Decision Tree Classifier, Support Vector Machine Classifier, Random Forest Classifier. It also demonstrates how you can optimize a Random Forest Classifier using GridSearchCV

Get started

1. Clone the repo and cd into the directory: git clone github.com/davified/learn-scikit-learn && cd learn-scikit-learn

2. Run ./bin/setup.sh (read it before running!). Under the hood, this will:

   • Install anaconda (for python3)
   • Create a virtual environment
   • Install dependencies listed in environment.yml

3. To activate the virtual environment, run source activate ml-101

4. Start the notebook: jupyter notebook (if you're not familiar with jupyter notebook, it's just an IDE. You can refer to the cheatsheet)

5. If prompted, select ml-101 as your jupyter kernel

6. At the end of the session, stop the Jupyter server by hitting Ctrl-C in your terminal. To exit the virtual environment, run source deactivate