- Pandas == 1.5.3
- Matplotlib == 3.7.1
- Yellowbrick == 1.5
- Scikit-learn == 1.2.2
- Imbalanced-learn == 0.10.1
- XGBoost == 1.7.5
- LightGBM == 3.3.5
The motivation behind this project is to use unsupervised learning techniques to uncover the relationship between the demographics of the company's existing customers and the general population of Germany. This will enable the identification of which parts of the population are more likely to be customers of the mail-order company and which are less so. The next step involves building a prediction model using demographic information from individuals that were part of a mail-order marketing campaign to decide whether or not they would successfully convert into customers. By the end of the project, we aim to provide insights that will help the mail-order company better target its campaigns and increase its customer base.
- Project Notebook
- Project Report
- src conatins all the modules used in the Project Notebook
- Results Contains all the Prediction on Test Data
I am grateful to Udacity & Bertelsmann-Arvato for this amazing data and Problem statement. Special thanks to Joshua Starmer's StatQuest for providing an explanantion of PCA & KMeans algorithms. Also of note was this succinct article on the Elbow Method by Cássia Sampaio.