About

Pramish T M is an analytical and process-oriented professional with a strong background in data analysis, data science, and machine learning. With over 6 years of relevant experience, he is actively seeking job opportunities and is ready to relocate anywhere. Pramish is proficient in Python, SQL, Power BI, Tableau, and various machine learning algorithms such as Simple Linear Regression, Multiple Regression, Polynomial Regression, Logistic Regression, SVM, kNN, Naive Bayes, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, K-fold cross-validation, Grid Search, and Random Search. Pramish has a clear understanding of database types, research methodologies, data capture, curation, manipulation, and visualization. He is skilled in minimizing the cost function based algorithms like Gradient Descent, Stochastic Gradient Descent, and Batch Gradient Descent and Regularizing Linear Models with the help of Ridge, Lasso, and Elastic Net. He is also experienced in feature engineering in Python, including missing value treatment, outlier handling, data transformation, feature selection, and reshaping data using Python packages like Numpy, Pandas, and Scikit Learn. In addition to his technical skills, Pramish has a good knowledge of BI tools like Tableau and Power BI and is proficient in libraries such as Numpy, Pandas, Matplotlib, Seaborn, Scikit Learn, etc. He has excellent communication skills and is a quick learner with a strong work ethic. Currently, Pramish is working as an AI Domain Trainer at Jsmies, where he is training graduate students in AI as a part of courses under DDU-GKY. Previously, he worked as an Instructor in Automobile Engineering at HMO Private ITI Manjeri, where he conducted classes for KGCE Automobile Engineering students, including basic workshop practicals and guided students in technical projects with their presentations. Pramish holds a Bachelor of Technology in Mechanical Engineering from the College of Engineering Trivandrum and is currently pursuing a Basic Data Science degree from Netzwerk Academy, which he will complete in 2022. His tech stack includes AI, ML, SQL, Python, Research Scientist, Seaborn, Data Scientist, Scikit, Pandas, Search, Naive Bayes, SVM, XGBoost, Random Forest, and Flask.