Training AI models for the Marathi Language, contributing to the advancement of natural language processing

Traineeship Experience at MedTourEasy My traineeship at MedTourEasy immersed me in a data analytics project centered on the medical tourism sector. Here, I applied theoretical knowledge to real-world data, refining my expertise in machine learning, data preprocessing, and model evaluation. Additionally, I successfully completed the inaugural Google Data Analytics Professional Certificate course on Coursera. The project aimed to construct machine learning models to dissect medical tourism data, forecast outcomes, and guide industry decisions, encompassing critical phases like data preprocessing, feature engineering, model selection, and evaluation. Meticulously cleaned and prepared the dataset for analysis, employed feature engineering techniques to rectify missing values, outliers, and ensure data quality. Model selection and evaluation revealed commendable AUC Scores: 0.7858 for Logistic Regression, 0.7853 for TPOT Classifier, and 0.7877 for Neural Network Model. Leveraging Python libraries including Pandas, Scikit-learn, TPOT, and PyTorch optimized data analysis and model construction. Pioneering the integration of neural network modeling enriched project depth, showcasing advanced machine learning techniques. Proficiency in Machine Learning, Data Analysis, Programming, Visualization, and Project Management was acquired, alongside completion of the Google Data Analytics Professional Certificate course, fortifying foundational data analytics knowledge. Future endeavors include striving for heightened model accuracy, exploring additional machine learning models and algorithms, and staying abreast of emerging data science methodologies, ensuring readiness to tackle future challenges. The MedTourEasy traineeship provided invaluable hands-on experience, bridging theoretical knowledge with practical application in a real-world context, equipping me to navigate future opportunities and challenges within the realm of data science and analytics.