About

Nikunj Prajapati is a highly skilled Data Scientist with 1.5+ years of experience in various business domains such as education, logistics, and retail. He is proficient in Machine Learning, Python, Data Acquisition, Data Validation, Predictive Modelling, Data Visualization, and Data Mining with large data sets of Structured and Unstructured data. Nikunj has extensive experience in statistical programming languages and data analysis tools such as Python and SQL. His expertise lies in transforming business requirements into analytical models, designing algorithms, and developing data mining and reporting solutions that scale across large volumes of structured, semi-structured, and unstructured data. Nikunj has a Master of Science in Artificial Intelligence/Machine Learning from Scaler Academy and a Bachelor of Engineering in Information Technology from LDRP Institute of Technology & Research, Gujarat Technological University. He also holds a 12th degree in PCM from Vishwa Vidyalaya Highschool and a 10th degree (86.8%) from Swastik Vidyalaya. Nikunj has extensive knowledge in mathematical modeling, statistical analysis, predictive analytics, and machine learning techniques. He is proficient in managing the entire Data Science project life cycle and actively involved in all phases including data collection, data pre-processing, EDA and statistical methods, model building, and model validation. He has experience in Applied statistics, Exploratory Data Analysis, and Visualization. Nikunj is currently working as a Data Scientist at Focus and has worked on complicated client problems, creating presentations, worksheets, and Dashboards to communicate precise insights. He has relevant experience of 4.35 years and has worked on Machine Learning algorithms such as Linear Regression, SVM, KNN, Naive Bayes, Random Forest, CART, K-means Clustering, Hierarchical clustering, K-Nearest Neighbor, PCA, Feature Selection, Neural networks, and NLP. His TechStack includes Data Scientist, Research Scientist, ML, SQL, AWS, Python, Naive Bayes, SVM, Random Forest, NLP, and Neural Networks.