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Solved 9 biz tasks by 18 graphs and 10 statistical methods include dummy data partitioning (RMSE & R2), stepwise model selection, multicollinearity (correlation, VIF), MLR, GLM for logistic regression.

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KAR-NG/Marketing_Analytics

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Marketing Analytics (MSc. In Business Analytics Final Project)

EDA + Machine Learning (Regression) + Group comparison

Solved 9 biz tasks by 18 graphs and 10 statistical methods include dummy data partitioning (RMSE & R2), stepwise model selection, multicollinearity (correlation, VIF), MLR, GLM for logistic regression.


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Reference

https://www.kaggle.com/jackdaoud/marketing-data

Australia Bureau of Statistics 2014, Age Standard, viewed 26 July 2021, https://www.abs.gov.au/statistics/standards/age-standard/latest-release

Minitab Blog Editor 2013, Enough Is Enough! Handling Multicollinearity in Regression Analysis, viewed 26 July 2021, https://blog.minitab.com/en/understanding-statistics/handling-multicollinearity-in-regression-analysis

The Pennsylvania State University 2018, 10.7 - Detecting Multicollinearity Using Variance Inflation Factors, viewed 27 July 2021, https://online.stat.psu.edu/stat462/node/180/

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Solved 9 biz tasks by 18 graphs and 10 statistical methods include dummy data partitioning (RMSE & R2), stepwise model selection, multicollinearity (correlation, VIF), MLR, GLM for logistic regression.

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