Project-1. Consumer Complaints Resolution Consumer complaint resolution is important to any business. In this case we have been given detailed consumer complaints along with whether consumer disputed with the conclusion. If we are able to predict this, consumer who is more likely to dispute a conclusion can be given more attention as to how the complaints are handled as well as how persuasively the final conclusions are conveyed to them. Your target here is to build prediction model for column "Consumer disputed". Data Files Training Data = Consumer_Complaints_train.csv Test Data = Consumer_Complaints_test.csv Formal Problem Statement Your target here is to build prediction model so that you are able to predict which consumer is more likely to dispute the resolution of a complaint i.e. you need to make predictions for the "Consumer disputed" column. All the column names are self-explanatory. You need to build your model on train data. Test data does not have the response column "Consumer disputed", you need to predict those values and submit it in a csv format. Here you work on creating the machine learning models and choosing the one which gives the best performance. To get a passing grade in this project you need to have the AUC score of at least 0.52 for your test data predictions. Note: Output of test data is provided in sample_submission.csv

Task1: Build the model for trained data. Task2: To update the notebook to improve the performance