Will you click on the advertisement on Facebook?

Building a predictive model to see who will click on an advertisement on Facebook.

The repository has multiple directories, with each serving a different purpose:

• input/: contains the dataset, split into train and test files.
• model/: consists of baseline logistic regression models. Stratified Kfold validation was applied while training the model, with number of folds=5, hence you see 5 models.
• notebooks/: Consists of one jupyter notebook. It was used for EDA purpose and also experiment with some functions used for feature engineering.
• src/: this directory consists of the source code for the project.
  • config.py: consists of variables which are used all across the code.
  • create_folds.py: used for implementing stratified kfold cross validation.
  • feature_engg.py: used for cleaning the dataset and applying feature engineering techniques.
  • test_functionalities: using pytest module, i define some data sanity checks on the training data.
  • train.py: this file contains the code for implementing the model. The train and the inference stage.

To obtain clean data and split it into train and test set, use the following command:

python train.py --clean dataset

To train the model use the following command:

python train.py --train skfold

For inference stage, use:

python train.py --test inference

For more information use:

python train.py --help