Exploratory Data Analysis of Facebook Ad Data with Python

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In this project facebook ad data is analysed through means of an exploratory data analysis. Metrics commonly use in ad analysis are implemented and investigated. It is assumed business performance is driven by absolute return on advertising spend and as such the ROAS metric is targeted. This preliminary analysis suggests further campaigns should focus on the 30-34 age group, particularly males. The advertising spend is least effectively targeted on the 45-49 age group. However, the number of clicks associated with these conclusions is in some cases low and it is therefore suggested that further work aim to show the statistical significance of targeting these groups.

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Using Docker and Kubernetes to produce a scalable fraud detection API

In this report a simple logistic regression model is used to classify credit card transactions as fraudulent or not. A Recall of 0.8 and Precision of 0.7 is obtained for a false positive rate of 0.0005. However, for a model to be useful from a business perspective an understanding of how to deploy the model in the real world is important. Docker and Kubernetes are investigated for this purpose.

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