koozy0 / learn-scikit-learn

Demonstrate how to approach a machine learning problem with the tools of scikit-learn.

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 classification-model-workshop notebook. You can also refer to the solutions if you'd like.

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

The 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 python

   • Create a virtualenv folder in repo to store the dependencies in the next bullet

   • Install dependencies

     • jupyter
     • pandas
     • numpy
     • matplotlib
     • sklearn

3. source .venv/bin/activate

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)