● Analyzed large datasets to uncover trends and extract actionable insights.● Developed and applied machine learning models and algorithms to solve data-driven problems.● Conducted data preprocessing, feature engineering, and model evaluation for accurate predictions.● Communicated insights and results to stakeholders using clear data visualizations and reports.● Deployed web applications using Flask to deliver machine learning models and insights in a user-friendly format.● Develop hands-on exercises covering key AI and ML topics, including EDA, data preprocessing, model prediction, and building web applications with Flask for student academic projects.● Collaborate with instructors, program managers, and stakeholders to continually improve the training curriculum and materials.● Deliver engaging training sessions in-person to a wide range of learners, from beginners to advanced practitioners.

● Ensured the accuracy and quality of indexing processes.● Managed and resolved issues related to rejected accounts receivable (AR).● Handled multiple AR accounts simultaneously for various customers.● Worked with AWS (Absent with Script) to streamline processes for different clients.

● Conducted comprehensive credit score classification and prediction research, analyzing various platforms and methodologies to determine the most effective approach.● Performed data classification, visualization, model selection, and prediction to achieve accurate credit score predictions.● Developed and implemented ETL processes from multiple sources, handling outliers and missing values, ensuring data quality and consistency.● Developed a web application for credit score prediction using the Flask framework, enabling user-friendly access to predictive analytics.● Utilized Python, Tableau, and MySQL tools for data processing, visualization, and analysis.● Evaluated multiple machine learning models to identify and implement the best-performing model for credit score prediction.● Completed a project on Air Quality Index Prediction: Regression problem statement, Collected the data, performed Feature engineering, feature selection, hyperparameter tuning, exploratory data analysis, and Compared the results given by various Machine Learning models. The best RMSE score was given by the Random Forest Regressor. Used the Flask framework for web application and deployed the model on PythonAnywhere.

● Developed dynamic web applications using PHP and Laravel framework.● Developed and maintained RESTful APIs for efficient data retrieval and manipulation.● Implemented CRUD operations to manage data within the web application.● Coding and development of UI applications using VueJS.