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.
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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
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