Python-Customer-Segmentation-Clustering

Project Description: This project analyzes customer data from a mall and segments customers based on their demographic and spending behavior. Using Python, we conducted data cleaning, preprocessing, and visualization techniques to explore the data. We performed univariate and bivariate analysis to identify potential clusters and then used the K-means clustering algorithm to cluster the customers into groups based on their similarities. We evaluated the model performance and chose the optimal number of clusters. Finally, we visualized the clusters and interpreted the results. The insights gained from this project can be used to develop targeted marketing strategies and increase customer satisfaction and loyalty. This project demonstrates the power of clustering algorithms in analyzing customer data and identifying hidden patterns and insights.

Data:

'Mall_Customers.csv'

Key steps involved:

• Importing the necessary libraries
• Loading the dataset into a Pandas dataframe
• Exploring the dataset and visualizing the relationships between variables
• Preprocessing the data and scaling the features
• Using K-means clustering algorithm to cluster the customers
• Evaluating the model performance and interpreting the results

Future work:

• Implementing other clustering algorithms and comparing their performance
• Collecting more data and re-evaluating the model to improve its accuracy

Outline

• Introduction

• Overview of the project and its objectives
• Brief description of the dataset used and its attributes
• Data Exploration and Visualization

• Summary statistics of the dataset
• Data cleaning and preprocessing
• Visualizing the distribution of each attribute
• Visualizing the relationship between attributes
• Identifying outliers and anomalies
• Feature Scaling and Selection

• Scaling the features using StandardScaler
• Clustering Algorithm and Model Training

• Brief introduction to K-means clustering algorithm
• Choosing the optimal number of clusters using elbow method or silhouette score
• Implementing K-means algorithm using Scikit-learn
• Evaluating the model performance using inertia and silhouette score
• Visualizing the clusters and interpreting the results
• Conclusion and Future Work

• Summary of the project and its key findings
• Potential improvements and future work for the project