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Predicting Customer Subscription with Machine Learning, Random Forest, Logistic Regression & SHAP
📊 Can Machine Learning Predict Which Customers Are Most Likely to Subscribe?
I worked on a Bank Marketing Prediction project to explore customer behavior and identify the factors that influence subscription decisions.
Using Python and Machine Learning, I developed and compared two classification models:
Logistic Regression: Baseline classification model.
Random Forest: Achieved a better F1-score of 0.522 compared to 0.440 for Logistic Regression.
#DataScience #MachineLearning #Python #DataAnalytics #RandomForest #LogisticRegression #SHAP #PredictiveAnalytics #DataVisualization
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Awesome dude
Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
Income & Expense Control | Excel Dashboard
I developed a professional Excel dashboard as a portfolio practice project to demonstrate my skills in financial data management, income and expense tracking, and business reporting.
Project Features:
Automated income and expense calculations.
Financial dashboard with key performance indicators (KPIs).
Transaction tracking and organization.
Expense analysis by category.
Financial charts and visual reporting.
Net income calculation.
Project Results (Sample Data):
Total Income: $46,200
Total Expenses: $9,090
Net Result: $37,110
Tools: Microsoft Excel
Skills: Data Analysis, Accounting, Financial Reporting, Data Entry.
This project demonstrates my ability to organize financial records, analyze business performance, and create clear financial reports using Microsoft Excel.
Note: This is a portfolio demonstration using fictional financial data.
Great work 👏🏻