adding project

Author: iamAntimPal

Interview/Personal Project interview Question/Project 2/readme.md

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+
+# Customer Behavior Analysis & Personalized Marketing Strategy
+
+### **Project Title**:
+
+**Project Name**: Customer Behavior Analysis & Personalized Marketing Strategy  
+**Project Description**: Analyzed customer transaction data from an e-commerce platform to uncover purchasing patterns, segment users, and recommend targeted marketing strategies.  
+**Project Duration**: 1 month  
+**Project Team**: 2 members  
+**Project Location**: India   
+**Project Timeline**: 1 week
+  
+**Project Deliverables**:  
+  - RFM analysis report  
+  - K-means clustering report  
+  - Dashboards (Tableau/Power BI)  
+  - Recommendation system report  
+  - Cart abandonment analysis
+
+**Customer Behavior Analysis & Personalized Marketing Strategy**  
+**Tools**: Python (Pandas, Scikit-learn, Matplotlib/Seaborn), SQL, Excel/Tableau  
+
+---
+
+#### **Description**  
+
+Analyzed customer transaction data from an e-commerce platform to uncover purchasing patterns, segment users, and recommend targeted marketing strategies.  
+
+- **Data Sources**: Public datasets (e.g., [Online Retail Dataset from UCI](https://archive.ics.uci.edu/ml/datasets/Online+Retail)), or synthetic sales data.  
+- **Key Tasks**:  
+  1. Performed **RFM analysis** (Recency, Frequency, Monetary) to segment customers based on engagement.  
+  2. Identified high-value, at-risk, and dormant customers using **K-means clustering**.  
+  3. Visualized trends (e.g., seasonal purchases, product preferences) via dashboards (Tableau/Power BI).  
+  4. Built a simple recommendation system using collaborative filtering (e.g., cosine similarity for product suggestions).  
+  5. Analyzed cart abandonment rates and proposed interventions (e.g., discounts for at-risk customers).  
+
+---
+
+#### **Outcome**  
+
+- Proposed personalized email campaigns and loyalty programs, projecting a **15% increase in repeat purchases**.  
+- Highlighted underperforming product categories and recommended inventory adjustments.  
+- Created interactive dashboards for stakeholders to monitor KPIs like CLTV (Customer Lifetime Value) and churn risk.  
+
+---
+
+### **Why This Project Stands Out**  
+
+1. **Business Impact**: Directly ties to revenue growth, customer retention, and marketing efficiency.  
+2. **Technical Breadth**: Combines SQL for data extraction, Python for analysis, and visualization tools.  
+3. **Relevance**: Applicable across industries (e-commerce, SaaS, retail).  
+
+---
+
+### **How to Present in Your Resume**  
+
+**Customer Behavior Analysis | Python, SQL, Tableau**  
+
+- Segmented 5,000+ customers into 4 groups using RFM and K-means clustering; identified top 10% high-value customers driving 40% of revenue.  
+- Built a recommendation engine suggesting personalized products, increasing average order value by $12 in simulations.  
+- Visualized seasonal trends and cart abandonment hotspots, leading to targeted campaign strategies.  
+
+---
+
+### **Bonus Tips**  
+
+- **GitHub Repo**: Share cleaned datasets, Jupyter Notebooks, and dashboard snapshots.  
+- **Metrics**: Quantify results (e.g., "% increase in engagement," "X% higher click-through rate").  
+- **Domain Knowledge**: Mention how insights align with business goals (e.g., reducing churn, upselling).

Interview/Personal Project interview Question/readme.md

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 # All Project Name
 
-[Sales Performance Analysis & Forecasting](Interview\Personal Project interview Question\Sales Performance Analysis & Forecasting)
+[Personal Project interview Question](https://github.com/iamAntimPal/TechInterviewMaster/tree/main/Interview/Personal%20Project%20interview%20Question)
+[Sales Performance Analysis & Forecasting](https://github.com/iamAntimPal/TechInterviewMaster/tree/main/Interview/Personal%20Project%20interview%20Question/Sales%20Performance%20Analysis%20%26%20Forecasting)
+