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+# 🧠 Automated E-Commerce Performance & Sentiment Analysis Dashboard
+
+## 🎯 Project Overview
+This project automates the process of analyzing **sales performance** and **customer sentiment** by combining:
+- **Python** – for data cleaning, aggregation, and sentiment analysis.
+- **n8n** – for workflow automation, integration, and notifications.
+
+The goal is to deliver **real-time, actionable insights** to business stakeholders.
+
+---
+
+## 💼 Business Goal
+To automatically track:
+- Which products are performing well.
+- What customers feel about them.
+- When there’s a sudden drop in sales or rise in negative reviews.
+
+This enables teams to respond quickly to business or customer issues.
+
+---
+
+## 🧰 Tools Used
+
+| Tool | Purpose |
+|------|----------|
+| **n8n** | Automates data fetching, scheduling, and notifications. |
+| **Python** | Handles data processing and sentiment analysis using NLP. |
+| **Google Sheets / Database** | Stores analyzed data for reporting or dashboards. |
+| **Slack / Email** | Delivers automated summaries and alerts. |
+
+---
+
+## 🔄 Workflow Overview
+
+[Start: Cron Node]
+↓
+[Fetch Sales Data: HTTP/DB Node]
+↓
+[Fetch Reviews: HTTP Node]
+↓
+[Execute Python Script]
+↓
+[Store Data: Google Sheets/DB Node]
+↓
+[Send Report: Slack/Email Node]
+↳ [If Node: Alert on Issues]
+
+
+
+---
+
+## ⚙️ Step-by-Step Process
+
+### **1. Data Ingestion**
+- **Cron Node**: Triggers workflow daily (e.g., every morning at 3 AM).
+- **HTTP/DB Node**: Pulls sales data (e.g., order ID, product, quantity, price).
+- **HTTP Node**: Fetches customer reviews (e.g., rating, review text).
+- **Merge Node**: Combines both datasets.
+
+**Example:**  
+Sales data → 500 daily transactions  
+Reviews data → 120 new reviews fetched via API.
+
+---
+
+### **2. Data Processing & Sentiment Analysis**
+- **Python Node** cleans, aggregates, and analyzes the merged data:
+  - Cleans missing or duplicate records.
+  - Calculates total revenue, units sold, and average sentiment per product.
+  - Uses **VADER** sentiment analysis to categorize reviews as *Positive*, *Neutral*, or *Negative*.
+
+**Example Output:**
+
+| Product | Revenue | Units Sold | Avg Sentiment | Negative Reviews |
+|----------|----------|-------------|----------------|------------------|
+| SmartWatch X2 | $15,000 | 120 | 0.78 | 2 |
+| Wireless Charger Mini | $8,500 | 70 | -0.32 | 8 |
+
+---
+
+### **3. Data Storage & Reporting**
+- **Google Sheets / Database Node**: Saves processed data for dashboarding (e.g., in Looker Studio or Tableau).
+- **Slack / Email Node**: Sends a daily report.
+
+**Example Slack Message:**
+> 📊 *Daily E-commerce Report (Oct 6, 2025)*  
+> Top Product: **SmartWatch X2** – $15,000 in revenue  
+> Most Negative Reviews: **Wireless Charger Mini** (8 negative reviews)
+
+---
+
+### **4. Automated Alerts**
+- **If Node** checks conditions:
+  - Sentiment score < -0.5  
+  - >10 negative reviews  
+  - >50% drop in revenue compared to yesterday  
+
+If triggered, a **Slack/Twilio alert** is sent.
+
+**Example Alert:**
+> ⚠️ *Alert:* Product **Wireless Charger Mini** sentiment dropped to -0.65 with 12 new negative reviews.
+
+---
+
+## 📂 Data Sources
+
+| Data Type | Example Source | Connection Method |
+|------------|----------------|------------------|
+| **Sales Data** | Shopify / WooCommerce | API or SQL |
+| **Product Reviews** | Store API / Trustpilot | HTTP Request |
+| **Storage** | Google Sheets / PostgreSQL | API or DB connection |
+| **Notifications** | Slack / Email | n8n integration |
+
+---
+
+## ✅ Summary
+
+| Role | Tool | Description |
+|------|------|--------------|
+| **Workflow Automation** | n8n | Handles scheduling, API calls, and alerts. |
+| **Data Analysis** | Python | Cleans and processes data; performs sentiment analysis. |
+| **Storage & Dashboards** | Google Sheets / DB | Maintains daily summary records. |
+| **Reporting** | Slack / Email | Shares reports and alerts with stakeholders. |
+
+---
+
+## 🚀 Outcome
+A fully automated system that:
+- Fetches and analyzes e-commerce data daily.  
+- Tracks both **sales trends** and **customer sentiment**.  
+- Sends **visualized reports** and **alerts** automatically — saving time and improving response to performance issues.