AreYouABot

A classifier to detect bots that produces fake clicks and leads on the website.

Pre-installation steps:

• Make sure you have python 3+ installed on your computer.
• Make sure you have pip3 package installed on your computer.

Install requirements:

In your terminal/command-line go to the project folder and execute the command below:

pip3 install -r InstallMe.txt 

Running of the project:

Please run the following command in your terminal and read carefully to proceed thereafter.

python main.py

Internal working of the project:

a. This project takes the files stored in data/raw_dataset/ to transform them to create a train, val and test set.

b. There are 4 features that are constructed:

1. No_of_events: It adds a feature to dataframe mentioning the number of events each user did on it's arrival on the website.
2. Ratio_of_categories_interacted: It adds the ratio of no. of distinct category user interacted upon total number of distinct category interacted by all the user.
3. Ratio_of_skewness_of_events_to_detect_bot_behaviour: It adds the ratios of click_ad/total_events, send_email/total_events and send_sms/total_events. These features will give the information about the typical bot behaviour.
4. Ratio_of_amount_events_done_more_by_a_typical_bot: It adds the ratio of (total events done by a user - average events done by real user)/(average events done by real user) This feature is designed to encapsulate the typical bot behaviour, as for a bot this value will tend to be more than 1 and for a real user it will lie between 0 and 1 in normal cases.

c. Thereafter, a simple DecisionTreeClassifier is trained with default parameters and the trained model is saved at trained_model/model.pkl.

d. The evaluation metrics are printed when main.py is running for validation and test set.

e. The confusion matrix plots are saved in folder plots/

f. The output of the test set as required is saved in folder output/test_set_predictions.csv.

Results on Validation and Test set:

Validation-set

Test-set