4.5+ years of hands-on experience in Artificial Intelligence and Machine Learning with strong focus on applied business problems., Experience across the end-to-end Machine Learning lifecycle: data extraction, data cleaning, exploratory data analysis (EDA), feature engineering, model development, evaluation, and insight generation., Strong proficiency in Python-based data science using Pandas, NumPy, and Scikit-learn., Hands-on experience in supervised learning techniques including regression and classification models., Built predictive models using Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, and Support Vector Machines (SVM)., Experience in unsupervised learning techniques such as K-Means Clustering for customer segmentation and pattern discovery., Applied feature engineering, data normalization, missing value treatment, and outlier detection to improve model performance., Strong understanding of model evaluation techniques including train-test split, cross-validation, RMSE, MAE, accuracy, precision, recall, and F1-score., Experience in SQL-based data extraction and transformation from relational databases (MS SQL Server, MySQL)., Proficient in data visualization and insight communication using Power BI, including dashboards and KPI reporting., Exposure to Azure Machine Learning for model experimentation and lifecycle understanding., Ability to translate business requirements into data science solutions and deliver actionable insights to stakeholders in MNC environments.