Machine Learning & Data Science | Practical Experience | Python, SQL, Data Analysis

Professional Bio

I am a Computer Science and Engineering graduate with a strong interest in Machine Learning, Data Science, Artificial Intelligence, and Web Development. I am passionate about solving real-world problems through technology and building data-driven solutions that create meaningful impact.

I have practical experience in Python programming, machine learning, data analysis, SQL, deep learning, and web development through academic projects, research work, and hands-on experimentation. I enjoy working with real-world datasets, identifying patterns, developing predictive models, and transforming technical solutions into practical applications.

One of my major academic projects focused on predicting employee attrition using machine learning techniques. The project involved data preprocessing, feature engineering, handling class imbalance, training predictive models, and evaluating their performance using industry-standard metrics. I explored techniques including XGBoost, SMOTE/SMOTE-Tomek, survival analysis, Cox Proportional Hazards, Kaplan-Meier analysis, and SHAP for explainable AI. A key objective was to extend traditional attrition prediction by exploring when an employee might leave, which also provided valuable experience in research methodology, technical writing, and model evaluation.

My technical skills include Python, NumPy, Pandas, Matplotlib, Scikit-learn, XGBoost, PyTorch, SQL, and Streamlit. I have worked on projects involving classification, regression, clustering, natural language processing, recommendation systems, and predictive analytics. I am also familiar with essential data science practices such as feature engineering, data preprocessing, encoding, scaling, cross-validation, and handling imbalanced datasets.

Beyond machine learning, I have built practical applications such as a Movie Recommendation System and a Fake News Detection System, gaining experience in integrating machine learning models into user-focused applications. These projects strengthened my understanding of model deployment and end-to-end application development.

I continuously improve my skills by practicing Python, data structures and algorithms, SQL, and machine learning concepts. I value writing clean, readable, and efficient code while approaching problems with a practical and analytical mindset.

I am seeking opportunities where I can contribute as a Machine Learning Engineer, Data Analyst, Data Scientist, AI Engineer, or Software Developer. My goal is to work on innovative technologies, collaborate with talented teams, and continue growing as a technology professional while delivering impactful, data-driven solutions.