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    Medium
    https://medium.com › @mumbaiyachori

    Ad Click Prediction | Machine learning system design

    • We will be using a funnel-based approach for predicting ad click prediction. Our first step involves candidate generation, wherein we select candidates for our prediction model. Further, we will use a simple pr… See more

    Metric Design & Requirements

    Offline Metrics: During the training, we will focus on ML metrics rather than the CTR/ revenue metrics, and we can focus on the offline metrics later. 1. Normalized Cross Entropy: It i… See more

    Medium
    Data Size

    Each observation includes features like user embedding, ads, click_or not. With a 1% CTR, we have 1 Billion ads. So starting with a month of data, we have around 100*10¹² * … See more

    Medium
    Train/test Data Labelling

    We will use previous session data for generating our training data. We will perform a time-based split. For example, we can use 25 days of data for training and five days fo… See more

    Medium
    Imbalanced Dataset

    As most of our dataset will be no clicks, we will have an imbalance in our data set. This can be achieved by either oversampling or undersampling. We would prefer to do undersamp… See more

    Medium
    How can ml modeling improve ad campaign performance?
    GitHub - farrellwahyudi/Predicting-Ad-Clicks-Classification-by-Using-Machine-Learning: In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression.
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    Table of Contents
    Metric Design & Requirements
    Data Size
    Train/test Data Labelling
    Imbalanced Dataset
    How can ml modeling improve ad campaign performance?
     

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