Python, R, SQL, MATLAB,Cloudera, TensorFlow, Hadoop, Spark,Tableau

ABHIJEET SUDHAKAR

+1-857-***-**** • ********.*@************.*** • www.linkedin.com/in/abhijeetsudhakar Education

Master in Professional Studies, Analytics

Northeastern University, Boston, MA Jan 2020 – Dec 2021 Bachelor in Engineering, Electronics and Telecommunication University of Mumbai, Mumbai, India Jul 2015 – Jul 2019 Professional Experience

Data Analyst Intern Jan 2021 - Jun 2021

Findability Sciences, Woburn, MA

● Predicted credit card loan payment for Metcredit Company. Performed EDA and 5 trials of prediction using Findability Platform, a software for automated prediction of models.

● Predicted residual price of car using Linear Regression, Random Forest and Gradient Boosting with score of 85%, 94% and 93% respectively.

● Finding out the probability of customer to buy any product like iphone and services using KNN, Random Forest, Decision Tree, Gradient Boosting

● Research about new programming language Julia

● Understanding image segmentation and classification using example of mixed fruit basket images. Skills

Programming Languages: Python, R, SQL, MATLAB

Platforms: Cloudera, TensorFlow, Microsoft Azure, KNIME Visualization Tools: Tableau, Power BI

Big Data Tools: Hadoop, Spark, Impala, Hive

Analytical Techniques: Linear Regression, Logistic Regression, KNN, Random Forest, Gradient Boosting, Image Segmentation and Classification

Projects

Big Data Project Design Proposal, Data Management and Big Data Jul 2021 – Aug 2021

● Proposed a design plan for Big Data Analytics on New York yellow taxi fares for Jan 2020

● Utilized python and spark using pyspark

● Implemented SQL commands in Jupyter

Financial Planning Advisor, Integrated Experiential Learning Sept 2020 – Dec 2020

● Studied/Categorized 50 clients of Financial Assistance Company Dorval and Chorne using Excel and KNIME software performing Latent Dirichlet Allocation (LDA) for text mining

● Planned financial futures of clients aged 50s and 60s based on emotional state Credit Card Fraud Detection, Predictive Analytics Sept 2020 – Oct 2020

● Predicted future credit card transactions using figures from Sept 2019 dataset

● Trained model to predict whether credit card transaction was fraudulent, with logistic regression of 95% accuracy

● Employed 6 regression and classification techniques, including logistic regression, gradient boosting, decision tree, random forest support vector machines to train model Additional Information

● Descriptive Data Analysis of Contagious Diseases using Statistical Parameters- International Journal of Computer Science and Engineering, Volume 6, Issue 9 Jul 2019 – Sept 2019

● An Exploration of Various Data Mining Techniques for Application in Child Healthcare- International Journal of Recent Technology and Engineering, Volume 8, Issue 3 Jul 2019 – Sept 2019

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