problem Statement An advertiser would like to estimate the performance of his campaign in the future

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Raw metrics: Impressions - Number of time the ad was shown Clicks - Number of time the ad clicked shown Cost - Amount spent to show ad Conversions - Number of transactions received (higher the better) Revenue - Total value of transactions received (higher the better) Efficiency metrics: Needs to be calculated and are based on raw metrics CTR - Clicks / Impression (higher the better - used to evaluate if the users find the ad relevant) CPC - Cost / Click (lower the better - used to evaluate if the cost for getting a click) CPA - Cost / Conversion (lower the better - used to evaluate if the cost for getting a conversion) ROI - Revenue/Cost (higher the better - used to evaluate the effectiveness of the advertising budget spent) Problem Statement An advertiser would like to estimate the performance of his campaign in the future. For the given data, please try to address the below questions: Perform exploratory data analysis and specify your findings with respect to any trends or outliers based on raw or efficiency metrics Identify correlation in performance of the ads by time metrics if any (Eg day of the week, day etc) Given the past performance data, predict the performance (Impressions, Clicks, Conversions, Revenue) of an ad between March 1st and March 15th. Use the data in predict.csv for your predictions and submit your response as shown in the file sample_submssion.csv) Note: Being able to summarise your findings visually is important for us. Please use appropriate charts/graphs. We would like to know your thought process to arrive at your analysis and predictions. Your submission will be evaluated using RMSE and R2 metrics. The jupyter notebook that you share with us, should fetch the data directly from the S3 Bucket to the notebook and should be fully executable without any errors.

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