Sabuhinaz

B.E. student in Artificial Intelligence & Data Science · Aspiring Data Analyst

Bengaluru, Karnataka, India

@sabuhinaz

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🎓 Bachelor of Engineering in Artificial Intelligence and Data Science at CIT Gubbi · Graduating 2027

About

Artificial Intelligence and Data Science engineering student with hands-on project experience in SQL, Python, Tableau, Power BI, and machine learning. Built data analysis and predictive modeling projects focused on dashboards, business insights, and model evaluation.

Education

Bachelor of Engineering in Artificial Intelligence and Data Science

CIT Gubbi

Artificial Intelligence and Data Science · 2023 – 2027

Skills

<3 months

SQL NumPy Python Pandas Seaborn Matplotlib scikit-learn Data Cleaning Model Training Model Evaluation Data Preprocessing Feature Engineering Exploratory Data Analysis

Tools / apps / platforms

<3 months

MySQL GitHub Tableau Google Colab Microsoft Excel Jupyter Notebook Microsoft Power BI

Projects

Adidas Sales Dashboard

Tableau

Designed and built an interactive Adidas sales dashboard in Tableau to visualize revenue, units sold, and profit performance. Created charts and filters to compare sales by product, region, retailer, and time period for quick business insights. Applied data cleaning, calculated fields, and dashboard design best practices to present key KPIs clearly.
Amazon Sales SQL Project

MySQL

Analyzed 128K+ Amazon sales records using MySQL to uncover business insights across revenue, products, order status, fulfilment, cancellations, and geography. Performed data cleaning and validation, and used SQL techniques including CTEs, subqueries, CASE WHEN, aggregate functions, and window functions such as RANK() and PARTITION BY. Identified key sales trends, top-performing products, category revenue contribution, cancellation patterns, and regional performance.
Cardiovascular Disease Prediction

Python, Pandas, scikit-learn

Built and evaluated machine learning models to predict cardiovascular disease using a dataset of 68,000+ patient records. Cleaned and preprocessed data, handled invalid values, calculated BMI, and converted age from days to years. Trained and compared Logistic Regression, KNN, Decision Tree, Random Forest, and SVM models. Achieved the best accuracy of 73.75% using a Support Vector Machine (SVM) model. Evaluated the model using precision, recall, F1-score, confusion matrix, and cross-validation. Saved the final trained model and preprocessing scaler using Joblib.

Courses & certifications

Python Certification ↗ · Coursera
Data Science ↗ · Coursera
Data Science Tools ↗ · Coursera
Data Science Methodology ↗ · Coursera
Python for Data Science and AI ↗ · Coursera
SQL for Data Science · Coursera
Data Analysis ↗ · Coursera
Data Visualization ↗ · Coursera
Machine Learning ↗ · Coursera

🎯 Hobbies & interests

reading books

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