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Built an end-to-end Customer Churn Analysis solution using Python, SQL, and Power BI. Python was used for data cleaning and preparation, SQL for exploratory data analysis and trend discovery, and Power BI for creating an interactive dashboard. The solution provides insights into customer churn, revenue impact, contract preferences, tenure distribution, and customer demographics, enabling businesses to take proactive retention measures.
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Connect with clients, get discovered, and run your business 100% commission-free
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🚀 Turning Manual E-commerce Order Processing into an Automated Workflow with n8n
Manual order management may seem simple, but copying customer details, checking information, preventing duplicate orders, and sending confirmation emails can quickly become repetitive and error-prone.
I recently built an end-to-end order processing automation using n8n to solve these challenges for a handmade products store.
Here's what I implemented:
🔹 Automated Order Capture: Receive new orders through a webhook.
🔹 Data Validation & Cleaning: Validate required fields, email formats, quantities, and prices before processing.
🔹 Duplicate Prevention: Detect existing Order IDs to prevent duplicate records.
🔹 Google Sheets Integration: Automatically store valid orders and calculate order totals.
🔹 Email Notifications: Send order updates to the store owner and confirmation emails to customers.
🔹 Error Monitoring: Capture failed executions, log error details in a dedicated Google Sheets tab, and send email alerts.
The goal was simple: reduce repetitive administrative work, improve data accuracy, and make order operations more reliable.
This is what I enjoy about building with n8n — turning complex, repetitive business processes into practical automation systems.
Have a repetitive workflow in your business that could be automated? Let's connect!
#n8n #WorkflowAutomation #EcommerceAutomation #BusinessAutomation #NoCode #Automation
Using AI Automation workflow in business , not only save the time ,also increase work Efficiency
End-to-end retail analytics pipeline that turns raw, messy CSV exports into business decisions, built in Python and SQL and runnable with one command.
What it does:
Cleans and validates 6 relational tables, fixes 585 data-quality issues and logs every action
Loads a SQLite warehouse with keys, constraints and window-function SQL
Customer analytics: RFM segmentation, cohort retention, CAC / LTV by channel, product Pareto, market-basket analysis
Churn prediction model validated on future data (ROC-AUC 0.71, no data leakage)
6-month revenue forecast, best of 3 models by backtest (4.2% MAPE)
Auto-generated Excel report (live formulas, native charts) and an interactive Streamlit dashboard
18 automated tests
Hello Contra community! 👋
I'm Emmanuel, an aspiring Data Analyst passionate about turning raw data into meaningful insights.
I'm currently building my skills in:
Excel and Power BI for data visualization and reporting.
SQL for data querying and analysis.
Python (Pandas, Matplotlib and Scikit-learn) for data cleaning, analysis and machine learning.
I'm using practical projects to strengthen my skills and explore how data can help businesses make better decisions.
Looking forward to connecting, learning and collaborating with other professionals here!
