Customer Segmentation Model using KMeans Clustering on the dataset taken from kaggle (included in the repo).
Companies and Organizations sometimes need to group customers based on the similarity among them. In this dataset, we had customers Annual Income and Spending Score, which we pushed in a dataframe X. Then to find the optimal number of clusters we used the Elbow Method. Using the WCSS, we found out 5 to be the appropriate number of clusters. Then, the KMeans Clustering Model was trained and then the model was saved which was later integrated into a basic streamlit app.
Pandas Scikit Learn Joblib Matpotlib
StreamLit App Dataset Jupyter Notebook