Self-taught Data Scientist with hands-on experience building end-to-end ML solutions in
classification, regression, clustering, anomaly detection, price optimization, and time series
forecasting. Completed 20+ project…
Self-taught Data Scientist with hands-on experience building end-to-end ML solutions in
classification, regression, clustering, anomaly detection, price optimization, and time series
forecasting. Completed 20+ projects with strong focus on EDA, feature engineering, model
tuning, explainability. Skilled at handling large-scale datasets (1M–1.7M+ rows) and integrating
ML models with Flask for real-world use.
Technical Skills
Programming: Python, SQL, Java , JavaScript, PHP
Machine Learning: Classification, Regression, Clustering, Anomaly Detection, Time Series
Forecasting, Feature Engineering, Hyperparameter Tuning (Grid/Randomized/Optuna), SMOTE,
Scaling, Outlier Handling, SHAP
Frameworks & Libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, Prophet,
Statsmodels, Joblib, Scipy
Analysis: EDA (Exploratory Data Analysis), Correlation, VIF, ANOVA, t-test, Statistical Modeling
Deployment & Tools: Flask, Jinja, HTML/CSS, Git/GitHub, Jupyter Notebook, VS Code
Key Projects
ChurnShield – Customer Churn Prediction Web App
Flask, Scikit-learn, SQLite, HTML, CSS, JS | GitHub
• Developed a Flask web app for real-time churn prediction with authentication, admin
dashboard, and CSV export.
• Integrated Random Forest pipeline with explainability via feature importance.
• Implemented retention strategy generator based on model influence.
IEEE Fraud Detection – Transaction Classification
XGBoost, Feature Reduction, SHAP | GitHub
• Processed ~1M+ Kaggle IEEE-CIS records with Spearman correlation reduction (|ρ| >
0.9) and skew-aware preprocessing.
• Trained multiple ML models; tuned XGBoost achieving ROC-AUC 0.95, Accuracy 0.98, F1
0.66 for imbalanced fraud class.
Time Series Forecasting – Supermarket Retail Prices
LightGBM, Lag/Rolling/Calendar Features, Prophet | GitHub
• Forecasted SKU-level supermarket prices (1.7M+ rows) using LightGBM and Prophet.
• Applied TimeSeriesSplit CV and log-transformed target; evaluated vi
Retail Price Optimization – Competition-Aware ML Workflow Random Forest Regression, Feature Engineering, Model Explainability • Built full ML workflow integrating historical sales, competitor pricing, and demand features. • Developed Random Forest regression models for dynamic pricing; tuned hyperparameters for optimal performance. • Explained model predictions and pricing decisions using SHAP for actionable business insights.
Isolation Forest, Local Outlier Factor, KMeans, DBSCAN, Z-Score, IQR Developed pipelines combining model-based, cluster-based, and statistical anomaly detection on fraud and retail datasets. • Evaluated using confusion matrix, ROC-AUC, precision/recall; visualized anomalies with PCA and scatterplots.
Processed ~1M+ Kaggle IEEE-CIS records with Spearman correlation reduction (|ρ| > 0.9) and skew-aware preprocessing. Trained multiple ML models; tuned XGBoost achieving ROC-AUC 0.95, Accuracy 0.98, F1 0.66 for imbalanced fraud class.XGBoost, Feature Reduction, SHAP
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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