In this project, I worked with a Customer Segmentation dataset to analyze customer profiles and group them into meaningful clusters. The objective was to uncover hidden patterns in purchasing behavior, spending capacity, and preferences, enabling businesses to design personalized marketing strategies. Instead of looking at customers as one large group, I applied clustering techniques and visual analytics to identify distinct segments. This enhanced my skills in data preprocessing, unsupervised learning, and insight-driven storytelling.
- Imported the dataset using Pandas.
- Handled missing values, duplicates, and outliers for reliable clustering.
- Standardized numerical features to bring them on a common scale before applying ML models.
- Calculated descriptive statistics (mean, median, distribution) for income, age, and spending score.
- Examined frequency of purchases and customer loyalty over time.
- Conducted feature scaling for clustering suitability.
- Applied K-Means Clustering to group customers into distinct segments.
- Used the Elbow Method & Silhouette Score to determine the optimal number of clusters.
- Compared customer groups based on income, age, spending behavior, and frequency of purchases.
- I created a variety of visualizations to uncover business trends:
- Line Chart (Monthly Sales Trend) → Revealed peaks, seasonal spikes, and low-performing months.
- Bar Chart (Sales by Product Category) → Showed which categories drove the highest revenue.
- Histogram (Age Distribution of Customers) → Provided demographic insights into customer base.
- Gender Distribution Plot → Compared purchasing behavior of male vs female customers.
- Heatmap (Category vs Gender Sales) → Illustrated cross-segment revenue contribution.
I created multiple visualizations to highlight customer patterns:
- 📌 Scatter Plot (Income vs Spending Score) → Revealed distinct customer groups.
- 📌 Count Plot (Cluster Size) → Showed distribution of customers across segments.
- 📌 Box Plot (Income by Gender) → Highlighted earning differences between male & female customers.
- 📌 Bar Plot (Preferred Categories) → Showed which products are most popular.
- 📌 Histogram (Age Distribution) → Identified the main customer demographic.
- 📌 Cluster Comparison Plots → Visualized differences in spending & purchase frequency.
- High-Value Customers → Certain clusters showed high spending and frequent purchases, ideal for premium offers.
- Price-Sensitive Customers → Segments with lower income but higher frequency could be retained with discounts.
- Category Preferences → Different clusters showed interest in different product categories, useful for targeted campaigns.
- Loyalty Patterns → Customers with long membership years but moderate spending could be nurtured with loyalty programs.
- Python (Jupyter Notebook) → Core coding & analysis.
- Pandas & NumPy → Data cleaning, transformations, and feature engineering.
- Matplotlib & Seaborn → Professional plots & visual storytelling.
- Scikit-Learn → K-Means clustering, scaling, and evaluation metrics.
- 🤝 Customer Segmentation Skills → Learned how to split large datasets into meaningful customer groups.
- 📊 Unsupervised ML Mastery → Strengthened practical experience with clustering and validation methods.
- 🎨 Data Storytelling → Improved ability to present insights visually for business decision-making.
- 🌍 Marketing Strategy Support → Understood how segmentation helps in campaign targeting and customer retention.
- 🧑💻 Portfolio-Ready Project → Built a reusable workflow for real-world customer analytics.
A complete Customer Segmentation Analysis that uncovered actionable business insights and grouped customers into meaningful clusters. This project demonstrates my expertise in unsupervised learning, advanced analytics, and visualization-driven insights for real-world datasets.
💼 Portfolio: https://linktr.ee/AbdullahUmar.DataAnalyst
📧 Email: umerabdullah048@gmail.com
Show what the Code and Output look like.