Data Scientist
CurrentTrained different Machine Learning models using classification and regression algorithms like Linear Regression, Random Forest, KNN, SVM, Adaboost, Gradient Boosting, XGBoost, Bagging, Decision Tree, Neural Networks, etc.Gained hands-on experience with data visualization and exploratory data analysis.Web scraping projects done using tools like Beautiful Soup and Selenium.Developed effective presentations and visualizations to communicate complex technical concepts to non-technical stakeholders.Following are some of the projects done in Data Science and Machine Learning:1. Project: Insurance claim fraud detectionProblem Statement: To predict whether an auto insurance claim is faudulent or legitimate.Tech Stack: Python, Numpy, Pandas, Seaborn, Matplotlib, Statistical methods, ExcelSolution: Predicted fraudulent insurance claims with an accuracy of 88.73%.2. Project: Zomato restaurantProblem Statement: To predict average cost for two and price range in different restaurants across the world.Tech Stack: Python, Numpy, Pandas, Seaborn, Matplotlib, Statistical methodsSolution: Predicted average cost for two with an accuracy of 84.03% and price range with an accuracy of 94.19%.3. Project: Temperature forecastProblem Statement: To forecast the minimum and maximum temperature for the next day.Tech Stack: Python, Numpy, Pandas, Seaborn, Matplotlib, Statistical methodsSolution: Predicted next day minimum temperature with an accuracy of 85.55% and maximum temperature with an accuracy of 91.56%.4. Project: Library management systemProblem Statement: To build a library management system and answer complex queries.Tech Stack: MySQLSoution: Built a library management system and performed complex MySQL queries using joins, subqueries, group by, order by, views, etc.
