{"id":5839,"date":"2020-11-14T11:33:48","date_gmt":"2020-11-14T11:33:48","guid":{"rendered":"http:\/\/onlineclassesguru.com\/?p=5839"},"modified":"2020-11-14T11:33:48","modified_gmt":"2020-11-14T11:33:48","slug":"predictive-modeling","status":"publish","type":"post","link":"https:\/\/onlineclassesguru.com\/index.php\/2020\/11\/14\/predictive-modeling\/","title":{"rendered":"Predictive Modeling"},"content":{"rendered":"<style type=\"text\/css\"><\/style><p>Telecom Customer Churn Prediction Assessment<br \/>\nCustomer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. The data also indicates which were the customers who canceled their service. Based on this past data, we need to build a model which can predict whether a customer will cancel their service in the future or not.<br \/>\nYou are expected to do the following :<\/p>\n<p>EDA (16 Marks)<\/p>\n<p>How does the data looks like, Univariate and bivariate analysis. Plots and charts which illustrate the relationships between variables (4 Marks)<br \/>\nLook out for outliers and missing values (4 Marks)<br \/>\nCheck for multicollinearity &amp; treat it (4 Marks)<br \/>\nSummarize the insights you get from EDA\u00a0(4 Marks)<\/p>\n<p>Build Models and compare them to get to the best one (39 Marks)<\/p>\n<p>Logistic Regression (8 Marks)<br \/>\nKNN (8 Marks)<br \/>\nNaive Bayes (8 Marks) (is it applicable here? comment and if it is not applicable, how can you build an NB model in this case?)<br \/>\nModel Comparison using Model Performance metrics &amp; Interpretation (15 Marks)<\/p>\n<p>Actionable Insights (5 marks)<\/p>\n<p>Interpretation &amp; Recommendations from the best model<\/p>\n<p>Please note the following:<\/p>\n<p>Your submission should have two files \u2013 1) report in PDF format with a word limit of 3000 words, 2) R Code file. Appendices are not counted in the word limit.<br \/>\nYou must give the sources of data presented. Do not refer to blogs; Wikipedia etc.<\/p>\n<p><center><a href=\"http:\/\/onlineclassesguru.com\/orders\/ordernow\"><img decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcTyj99p60XCLyLk1htB7-1neRt8-2QdnenNlQ&usqp=CAU\"target=\"_http:\/\/onlineclassesguru.com\/orders\/ordernow\"\/><\/center><p>","protected":false},"excerpt":{"rendered":"<p>Telecom Customer Churn Prediction Assessment Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. The data also&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5839","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Predictive Modeling - onlineclassesguru<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/onlineclassesguru.com\/index.php\/2020\/11\/14\/predictive-modeling\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predictive Modeling - onlineclassesguru\" \/>\n<meta property=\"og:description\" content=\"Telecom Customer Churn Prediction Assessment Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. 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In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. 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