Customer clustering using silhouette K-means and silhouette analysis on Python. Also using logistic regression on Python to predict top 30 customers.
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Updated
Dec 14, 2022 - Jupyter Notebook
Customer clustering using silhouette K-means and silhouette analysis on Python. Also using logistic regression on Python to predict top 30 customers.
Analysis to optimize services & resident satisfaction in senior living facilities by segmenting population based on characteristics & behaviors.
Utilized Python-based unsupervised machine learning algorithms, including K-Means and DBSCAN, to effectively segment the mall customer market.
This project explores customer segmentation and market analysis in the context of online retail using an online retail dataset. By applying advanced analytics, we aim to uncover insights that can drive strategic decisions and enhance business performance.
The project uses KMeans clustering on the Global Superstore dataset to categorize customers based on their buying habits, aiming to help retailers make better business decisions by tailoring their marketing strategies and improving their inventory management.
Unsupervised Learning - Using K Means algorithm to Cluster the customers.
Unsupervised machine learning
Creating predictive models to classify Trump's vote share and clustering counties based on demographics and economic variables. Report findings in PDF with detailed methodologies, model assessments, and R code for the project.
Using the Elbow Method and Silhouette Analysis to find the optimal K in K-Means Clustering.
Learning Styles Segmentation using K-Prototypes
An analysis and approach to customer segmentation
Implements K-means clustering for customer segmentation based on age, annual income, and spending score. The analysis aims to uncover distinct customer segments for targeted marketing and personalized customer experiences.
Data Mining - EDA, Feature Selection, Standardize, Remove Global Outliers, Normalize, Feature Extraction (with PCA), Clustering, Classification (baseline models and hyperparameter tuning with GridSearchCV).
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