I'm a seasoned Data Analyst with a background in engineering and data science, focusing on leveraging data to drive strategic decision-making in technology-focused sectors.

👨‍💻 With a robust foundation in both the theoretical and practical aspects of data science and analytics, I strive to bridge the gap between data and actionable insights.

🔍 I'm particularly passionate about machine learning and predictive analytics, constantly seeking out new challenges that push the envelope of what's possible with data.

Python | R | SQL | NumPy | Pandas | SciPy | Tableau | Power BI | Machine Learning | AWS | Big Data

• Predicting Loan Default with A Machine Learning Approach: Paycast 💰
• Stroke Tool Prediction 🧠

• 📫 Email: masooud.arefi.69@gmail.com
• 💼 LinkedIn: Masoud Arefi
• 🌐 Website: Coming soon!

Feel free to explore my repositories, and let's connect to discuss opportunities, collaborations, or just to share ideas!

• Objective: Predict loan defaults using various machine learning models.
• Tools: Python, Scikit-learn, XGBoost, Matplotlib, Pandas, NumPy
• Highlights:
  • Implemented data cleaning and preprocessing on an extensive dataset.
  • Explored relationships and insights using Exploratory Data Analysis.
  • Trained and evaluated models, including Logistic Regression, Decision Tree, Random Forest, and XGBoost, for accurate loan default prediction.
  • Gained valuable insights into risk management, lending behaviors, and financial decision-making.
• View Project

• Objective: Develop a predictive tool for stroke risk assessment with a web interface.
• Tools: Python, Streamlit, Matplotlib, Scikit-learn, Nltk, Pandas, NumPy, Seaborn.
• Highlights:
  • Created an interactive web application for stroke prediction.
  • Implemented machine learning models to predict stroke risk based on user input.
  • Developed user-friendly interface to ensure ease of use for non-technical users.
• View Project
• Try the Tool

• Objective: Implemented topic modeling and sentiment analysis for user reviews.
• Tools: Python, Matplotlib, Scikit-learn, Nltk, Seaborn, Pandas, NumPy
• Highlights:
  • Focused on feature engineering and natural language processing techniques.
  • Employed classification models to gain insights into user behavior.
  • Improved conversion rate by understanding customer sentiments.
• View Project

• Objective: Employed ETL processes to prepare mosquito tracking data for analysis.
• Tools: Python, Seaborn, Pandas, NumPy
• Highlights:
  • Leveraged statistical knowledge to analyze West Nile Virus spread through extensive exploratory data analysis.
  • Utilized visualizations in Python and conducted hypothesis and correlation testing (t-test, Chi-square test).
  • Implemented regression predictive modeling to understand mosquito tracking data.

• Objective: Integrated in-depth data analysis, interactive Tableau visualizations, and proficient SQL querying for exploratory analysis to reveal trends and behaviors in Bixi bike usage.
• Tools: Python, Tableau, SQL
• Highlights:
  • Crafted an insightful visualization dashboard with drill-down capabilities to accommodate data inquiries.
  • Reported findings and insights effectively through professional documentation that merged data-driven insights with robust visualization techniques.