Mall Customer Segmentation: Machine Learning with K-Means Clustering

Mall Customer Segmentation: Machine Learning with K-Means ClusteringMall Customer Segmentation: Machine Learning with K-Means Clustering

The network for creativity

Join 1.25M professional creatives like you

Connect with clients, get discovered, and run your business 100% commission-free

Creatives on Contra have earned over $150M and we are just getting started

Back to feedPost

Ankita Banubakode

• Apr 5

🛍️ Mall Customer Segmentation Project

Built an end-to-end machine learning solution to segment customers using K-Means clustering based on demographics and spending behaviour.

🔍 Performed data cleaning, pre-processing, and exploratory data analysis (EDA) 📊 Applied K-Means clustering with the Elbow Method to identify optimal customer segments 🧩 Engineered features to improve clustering performance 📈 Developed an interactive dashboard using Plotly Dash for real-time segment exploration

💡 Identified high-value and low-value customer groups, enabling targeted marketing and improved customer engagement strategies

🛠️ Tools Used: Python, Pandas, Scikit-learn, Plotly Dash

🚀 Delivered a business-focused solution for customer segmentation and data-driven decision-making

Jupyter Python Data Analysis Dash Plotly

Istiak Ahmed Khan

• Apr 7

Great job

Ankita Banubakode

• Apr 8

Thank you

Ankita Banubakode

• Apr 5

Superstore Sales Dashboard & Forecasting🙌 • Developed interactive dashboard analyzing 10,000+ transactions to track revenue and profit metrics. • Performed trend analysis and 15-day forecasting, reducing manual reporting effort by 40%. • Provided insights to improve inventory planning and sales monitoring.

Data Analysis Microsoft Excel Microsoft Power BI

Geethasree Naguboina

pro

• Apr 8

Adding a 15-day forecast alongside the sales dashboard is a smart move — most dashboards only show what happened, not what's coming. The YoY monthly comparison view gives really clear context too. Did you build the forecasting model using Power BI's built-in analytics or a custom formula approach?

Ankita Banubakode

• Apr 5

🎬 Netflix Titles Dashboard (Tableau)

Designed an interactive Tableau dashboard to analyse Netflix’s content library and uncover trends across genres, countries, and release years.

📊 Visualized content distribution between Movies & TV Shows 🌍 Analysed global content production with focus on top countries 📅 Identified growth trends in content additions over time ⭐ Explored ratings and genre patterns to understand audience targeting

💡 Delivered key insights on content diversity, growth, and viewing trends to support data-driven storytelling

🛠️ Tools Used: Tableau, Excel, Kaggle Dataset

🚀 Built a clean, interactive dashboard to transform raw data into meaningful visual insights