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usage patterns.\n\n--\n## Current Dashboard\n![Description](Images/Dashboard_v1.png)\n---\n\n## 🎯 Objective\n- Understand the overall customer profile\n- Identify purchasing behavior and preferred product categories\n- Analyze marketing campaign acceptance rates\n- Compare performance of different purchasing channels\n- Segment customers based on RFM (Recency, Frequency, Monetary) analysis\n\n---\n\n## 📂 Dataset\n\n**Source:** [Customer Personality Analysis – Kaggle](https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis)  \nThis dataset contains demographic, product preference, campaign response, and channel usage data for a set of customers.\n\n| Category   | Fields |\n|------------|--------|\n| **People** | ID, Year_Birth, Education, Marital_Status, Income, Kidhome, Teenhome, Dt_Customer, Recency, Complain |\n| **Products** | MntWines, MntFruits, MntMeatProducts, MntFishProducts, MntSweetProducts, MntGoldProds |\n| **Promotion** | NumDealsPurchases, AcceptedCmp1–5, Response |\n| **Place** | NumWebPurchases, NumCatalogPurchases, NumStorePurchases, NumWebVisitsMonth |\n\n---\n\n## ✅ Completed\n- **Demographic Analysis** – Education level, marital status, age distribution  \n- **Spending Analysis** – Total spend per product category  \n- **Campaign Analysis** – Participation and acceptance rates\n- **Customer Status** - Active/Inactive/Risk\n\n## 📌 Planned Features\n- **Channel Performance** – Web, catalog, and in-store purchases comparison\n- **RFM Segmentation** – Customer classification based on Recency, Frequency, Monetary values\n\n---\n\n## 🛠 Tools \u0026 Technologies\n- **Power BI Desktop**\n- **Power Query**\n- **DAX** – Measures, calculations, columns\n- **MySQL** – Data source 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