now got seed funding of $10 Million. They are expanding their wings in Marketing Analytics. They collected data from their Marketing Intelligence team and now wants you (their newly appointed data analyst) to segment type of ads based on the features provided. Use Clustering procedure to segment ads into homogeneous groups. The following three features are commonly used in digital marketing: CPM = (Total Campaign Spend / Number of Impressions) * 1,000 CPC = Total Cost (spend) / Number of Clicks CTR = Total Measured Clicks / Total Measured Ad Impressions x 100 Perform the following in given order: Read the data and perform basic analysis such as printing a few rows (head and tail), info, data summary, null values duplicate values, etc. Treat missing values in CPC, CTR and CPM using the formula given. You may refer to the Bank_KMeans Case Study to understand the coding behind treating the missing values using a specific formula. You have to basically build an user defined function and then call the function for imputing. Check if there are any outliers. Do you think treating outliers is necessary for K-Means clustering? Based on your judgement decide whether to treat outliers and if yes, which method to employ. (As an analyst your judgement may be different from another analyst). Perform z-score scaling and discuss how it affects the speed of the algorithm. Perform clustering and do the following: Perform Hierarchical by constructing a Dendrogram using WARD and Euclidean distance. Make Elbow plot (up to n=10) and identify optimum number of clusters for k-means algorithm. Print silhouette scores for up to 10 clusters and identify optimum number of clusters. Profile the ads based on optimum number of clusters using silhouette score and your domain understanding [Hint: Group the data by clusters and take sum or mean to identify trends in clicks, spend, revenue, CPM, CTR, & CPC based on Device Type. Make bar plots.] Conclude the project by providing summary of your learnings.