The main objective of this project is to test if the ad increases intention of the brand awareness on the clients of the company.

SmartAd is a mobile first advertiser agency. It designs intuitive touch-enabled advertising. It provides brands with an automated advertising experience via machine learning and creative excellence. Their company is based on the principle of voluntary participation which is proven to increase brand engagement and memorability 10 x more than static alternatives. SmartAd provides an additional service called Brand Impact Optimiser (BIO), a lightweight questionnaire, served with every campaign to determine the impact of the creative, the ad they design, on various upper funnel metrics, including memorability and brand sentiment. As a Machine learning engineer in SmartAd, one of your tasks is to design a reliable hypothesis testing algorithm for the BIO service and to determine whether a recent advertising campaign resulted in a significant lift in brand awareness

• Statistical Modelling
• Using core data science python libraries pandas, matplotlib, seaborn, scikit-learn
• ML algorithms Logistic regression, Decision Trees, XGBoost
• Model management (building ML catalog contains model feature labels and training model version)
• MLOps with DVC, CML, and MLFlow

• Reasoning with business context
• Data exploration
• Hypothesis testing
• Machine learning
• Hyperparameter tuning
• Model comparison & selection
• Experiment analysis

• git clone https://github.com/Group-6-10Academy/smart_ad_ABtesting.git
• cd smart_ad_ABtesting
• pip install -r requirements.txt