This code performs customer segmentation using RFM (Recency, Frequency, Monetary) analysis. It creates an RFM dataframe, determines optimal clusters with k-means, and orders clusters from worst to best. Customers are segmented into high, mid, and low value groups and visualized in a 3D plot for better marketing strategies.

👨 K-Means Clustering Customer Segmentation is an interactive Streamlit app that uses machine learning to group customers by income and spending habits. It helps businesses target marketing, personalize offers, and gain insights with easy retraining, visualizations, and modular code.

I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predictive analysis). Used deep learning to enhance my model.

Introduction In ecommerce companies like online retails, customer segmentation is necessary in order to understand customers behaviors. It leverages aqcuired customer data like the one we have in our case, transactions data in order to divide customers into groups. Our goal in this Notebook is to cluster our customers to get insights in: Increasing revenue (Knowing customers who present most of our revenue) Increasing customer retention Discovering Trends and patterns Defining customers at risk We will do RFM Analysis as a first step and then combine RFM with predictive algorithms (k-means). RFM Analysis answers these questions: Who are our best customers? Who has the potential to be converted in more profitable customers? Which customers we must retain? Which group of customers is most likely to respond to our current campaign? More about RFM here.

This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

Data science project using the UCL Online Retail Dataset to perform customer segmentation based on spending behavior. Includes RFM analysis, K-Means clustering, customer profiling, and actionable business insights with visualizations.

Clustering is a popular unsupervised machine learning technique used to group similar data points based on specific criteria. It has many applications in various fields such as customer segmentation, image recognition, and anomaly detection. K-means clustering is a widely used clustering algorithm that partitions the data into k clusters, where eac

This project focuses on customer segmentation using machine learning techniques to analyze and group customers based on their behaviors, preferences, and demographics. It applies clustering algorithms like K-means to identify distinct customer segments, helping businesses target marketing efforts and improve customer experiences. Built with Python

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I use various Data Science and machine learning techniques to analyze customer data using STP framework. I preprocessed the data, performed segmentation, hierarchical clustering, k-means, PCA techniques with a lot of visualizations to segment and understand customer data. I have performed Purchase Analytics (both descriptive analysis and predict…

This project deals with the segmentation and grouping of the bank credit card customers using UnSupervised K-Means Clustering Algorithm. The project involves below steps in the life-cycle and implementation. 1. Data Exploration, Analysis and Visualisations 2. Data Cleaning 3. Data Pre-Processing and Scaling 4. Model Fitting 5. Model Validation using Performance Quality Metrics namely WCSS, Elbow Method and Silhouette Coefficient/Score 6. Optimized Model Selection with appropriate number of clusters based on the various Performance Quality Metrics 7. Analysis Insights and Interpretations of 2 different business scenarios with various Visualisations

Customer Segmentation for Credit Card Users (Banking domain): Defined a marketing strategy by developing a customer segmentation profile using K-means cluster and factor analysis. Used the derived KPI's to gain insights on the behavioral segments of credit card customers. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Customer segmentation using K-Means clustering algorithm to understand customer base and get meaningful insights.

Customer segmentation using k-means clustering with python and pandas

Customer segmentation analysis using unsupervised learning with k-means clustering algorithm

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Customer segmentation using K-Means clustering and PCA, with an interactive Streamlit app for real-time segment prediction.

Customer Segmentation project using K-Means, Hierarchical Clustering, and DBSCAN on the Mall Customers dataset with comparative analysis and evaluation metrics.

Customer segmentation using FLO OmniChannel data with K-Means and Hierarchical Clustering. Includes feature engineering, scaling, optimal cluster selection, and statistical analysis to identify loyal, high-value, inactive, and VIP customer groups..

Customer Segmentation in Marketing using Python — includes data preprocessing, EDA, feature engineering, clustering with K-Means and Hierarchical Clustering, segment interpretation, and marketing strategy insights based on acquisition channels and regions.

Unleash data-driven marketing with this customer segmentation project powered by K-Means clustering. We take raw customer data (like age, income, and spending behavior), clean it, visualize it, and group similar customers into clusters that actually make sense.

Explored market segmentation and unsupervised learning with a focus on K-Means clustering in this Data Science notebook. Leveraged descriptive statistics and visualization for insights into an extensive dataset. Unveiled the power of K-Means in categorizing customers. Experimented with alternative methods like DBSCAN and Mean Shift.

Customer segmentation project using the Online Retail dataset. Includes data cleaning, normalization, PCA, and clustering with K-Means and Mean Shift to group customers by purchasing behavior and uncover insights for targeted marketing and business strategy.

Here I have tried with four different applications of Natural Language Processing. Those are- Spam Classification, Sentiment Analysis, WordCloud and Customer Segmentation. I applied K-Means and Hierarchical clustering in creating Descriptive Unsupervised analysis. I developed WordCloud on spam dataset using python. I built Sentiment Analysis model using two different approaches: Traditional Machine Learning and Deep Learning. Using Keras and deep learning pipeline I achieved 87.93% accuracy. Soon, I will be uploading spam classification problem using traditional machine learning and deep learning-LSTM approach.

Use a customer purchase dataset to identify distinct segments using the K-Means clustering algorithm.

Customer Segmentation with K-means Clustering Algorithm

Mall customer segmentation using K-Means Clustering based on Annual Income and Spending Score. Identifies 5 distinct customer groups for targeted marketing strategies.

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