Forage Data@ANZ Program Task 2 - Predictive Analytics

In this video, I walk through my workbook for task 2 of the Data@ANZ Program on Forage, formerly InsideSherpa. Task 2 is called Predictive Analytics where we are required to explore correlations between customer attributes, build a regression and a decision-tree prediction model based on our findings. This video is a continuation of my previous video where I analysed the dataset that was provided to us in this program. The dataset contains historical transactions made by 100 ANZ customers over a 3-month period. In order to build a prediction model, in this video, I explain in detail how to create a target variable, customers' annual salary as well as predictor variables that can help us model these salaries. Furthermore, I also briefly go through some preprocessing steps which include train test split and make column transformer that consists of one-hot encoder and standard scaler. Finally, we evaluate the accuracy of model predictions using RMSE. If you find the video helpful, please drop a like, subscribe to my channel and share my content around - that would help me out a ton. Thank you for watching and I'll see you in the next one! Timestamp 0:00 - Introduction 2:10 - Import libraries and data 3:35 - Create target variable 7:10 - Create predictor variables 11:56 - Data preprocessing 15:15 - Model annual salary 16:42 - Conclusion Forage website https://www.theforage.com Link to my work on GitHub https://github.com/chongjason914/fora... Follow me Facebook -   / chongjason914   Instagram -   / chongjason914   Twitter -   / chongjason914   Medium -   / chongjason   LinkedIn -   / chongjason914