The project focuses on extracting insights related to bank customers. The business objective is to divide the customers into segments that could be reached out to with different merketing campaigns. The data represents the transactions frequency, amount...
The data is available in: https://www.kaggle.com/arjunbhasin2013/ccdata
The steps are part of a guided project on Coursera.
- Understand how to leverage the power of machine learning to transform marketing departments and perform customer segmentation
- Apply Python libraries to import and visualize dataset images.
- Understand the theory and intuition behind k-means clustering machine learning algorithm
- Learn how to obtain the optimal number of clusters using the elbow method
- Apply Scikit-Learn library to find the optimal number of clusters using elbow method
- Apply k-means in Scikit-Learn to perform customer segmentation
- Understand the theory and intuition behind Principal Component Analysis (PCA) algorithm
- Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization
- Compile and fit unsupervised machine learning models such as PCA and K-Means to training data
Task 1: Understand the problem statement and business case
Task 2: Import libraries and datasets
Task 3: Visualize and explore datasets
Task 4: Understand the theory and intuition behind k-means clustering machine learning algorithm
Task 5: Learn how to obtain the optimal number of clusters using the elbow method
Task 6: Use Scikit-Learn library to find the optimal number of clusters using elbow method
Task 7: Apply k-means using Scikit-Learn to perform customer segmentation
Task 8: Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization
Exploratory Data Analysis: Various steps were performed to clean the data, an example of the steps is the heatmap of the correlations between the different features:
Results: The clustering obtained 5 clusters (could be optimized further). The below clusters represent different types of customers and their habits:
