customer segmentation

Project Description:

In this project, I conducted a comprehensive customer segmentation analysis to help businesses understand their customer base better and tailor their strategies effectively. The dataset included key demographic and behavioral variables such as age, gender, income, occupation, education, and purchasing behavior.

What I Did:

Data Preparation:

Cleaned and preprocessed the data, handling missing values and standardizing variables for analysis.

Created new features to enhance the dataset and provide additional insights.

Exploratory Data Analysis (EDA):

Used visualizations to identify patterns, trends, and relationships between variables.

Discovered key factors influencing customer behavior and spending habits.

Clustering Techniques:

Applied unsupervised machine learning algorithms (e.g., K-Means, Hierarchical Clustering) to segment customers into distinct groups based on similarities in their profiles.

Determined the optimal number of clusters using the Elbow Method and Silhouette Scores.

Results and Insights:

Identified distinct customer segments, such as high-value customers, budget-conscious buyers, and occasional shoppers.

Provided actionable recommendations for marketing strategies and personalized campaigns targeting each segment.

Tools and Technologies Used:

Python (pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn)

Jupyter Notebook for analysis and visualization.

This project demonstrated my ability to turn raw data into actionable insights, enhancing decision-making and customer engagement strategies for businesses.