This repository contains the code for a machine learning model that is trained on the term deposit marketing dataset provided by Apziva. The goal of the project was to build a robust machine learning system that leverages information coming from call center data and improves the success rate for calls made to customers for any product that third-parties may offer.

Prerequisites

The following packages are required to run the code:

1. pandas
2. numpy
3. matplotlib and seaborn
4. scikit-learn

You can install these packages by running the following command: pip install -r requirements.txt

Train and predict

I found that the XGB classifier algorithm performs remarkably well in predicting the target label. The code for the model can be found in the train.py file. To train the model, run the following command: python [file]

The model will be trained on a pre-processed version dataset_final.csv of the raw dataset term-deposit-marketing-2020.csv dataset, and the accuracy score will be printed.

The code for using the trained model to make predictions on new data is contained in the predict.py file.

Evaluation

The model uses accuracy_score and f1_score as evaluation metrics.

Conclusion

In this project, I leveraged the power of machine learning, and employed a few pre-processing steps (label encoding, data balancing, feature-selection, and standardization), and an effective grid search cross-validation technique to identify the best parameters that allowed the XGB classifier algorithm to reach f-1 and accuracy scores of 94% -- more than 13% higher than the target value requested by the company.