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https://github.com/Candidate's Name /codes Street Address -654-9771 EMAIL AVAILABLESUMMARY:Data Science graduate student at Rutgers University. Possess advanced coding & programming skills in Machine learning and python. EDUCATIONRutgers University, New Brunswick, NJMaster of Science in Statistics-Data Science, 3.417/4 Sep. 2023  May .2025 National Institute of Technology AgartalaBachelors of Technology, Mechanical, 8.29/10 Aug. 2019  May. 2023 Work Experience:Data Science intern at Decovita Ceramics Private Limited May. 2021  July.2023 The internship focuses on predicting the market value and demand of a product in a country using machine learning models such as K-means and logistic regression. Feature engineering is employed to select important features for price prediction. K-means is utilized to classify sales in each region within the country, while logistic regression is applied to predict price values. AI Engineer intern at Tekriders July.2024  Present During my internship, I developed a resume parser web application using Django, integrating spacy and regex for extracting key details like names, skills, and contact information from resumes. I also built an Applicant Tracking System (ATS) with AI features, incorporating OpenAI, Hugging Face, and Gemini API to analyze and match resumes with job descriptions. Additionally, I worked on an automated AI phone call system using OpenAI and Twilio to create real-time, voice-enabled communication. RESEARCH PROJECTS:Machine Learning: Cirrhosis Prediction 2024Main Objective of the Project is to remove null values using MICE: Multivariate Imputation by Chained Equations, Interpret Cirrhosis Prediction Dataset using Non-Parametric model - Kaplan Meier Curve for understanding 50 percentage survival rates and Survival of different gender groups. Finally, Fitting Cox Proportional Hazard Model to Interpret Summary of Dataset and how well Ascites and Hepatomegaly effects survival rate of patients suffering from Cirrhosis disease. Neural Networks: PAPA JOHNS PizzaCoupon Redemption 2024 Main objective of project is to predict coupon based on number of orders, Redeemed and Coupon code. In this project Neural Networks is employed by using Keras library in Tensorflow framework. Data visualization included bar plot based on Coupon code vs Redeemed and orders vs Redeemed. Imported Sequential and Dense from Keras for Model building of Neural Networks and Adam optimized is utilized for model compile. Finally prediction is accurate with loss function of approximate 2.2. Machine Learning: FIFA_2024 Soccer Dataset Player value prediction 2023 Project, focused on predicting player value, involves four key steps: Data cleaning, Data visualization, Feature Engineering, and Model selection. In the Data cleaning phase, null values were removed, and outliers were addressed. Data visualization included scatter plots and a heat map to identify feature correlations. Ridge was employed in Feature Engineering for feature selection. Model selection utilized a pipeline approach with Lasso, Ridge, Xgbooster, OLS-Box-cox, and OLS. A T-test was applied to player heights. Ultimately, OLS-Box-cox emerged as the best-fit model, demonstrating low Root Mean Square Error and high R-squared values. K-Means: Customer Segmentation based on annual salary and spending in mall 2023 Python libraries like Pandas, Matplotlib, NumPy, Seaborn, and scikit-learn to implement the K-means clustering algorithm. The main goal was to analyze customer data, determine the optimal number of clusters (k) using the Elbow method, and uncover opportunities for targeted marketing. Offering discounts to high-income, low-spending segments could boost spending aligned with their income, optimizing revenue and enhancing the overall shopping experience.Data analytics: Comparison between single and double pilot injection of diesel engine in python 2022 The project involves data manipulation using NumPy to scale data into a specific range. Matplotlib is utilized to create line and bar graphs representing engine pressure, crank angle, and heat release rate. The goal is to achieve Low Temperature Combustion (LTC) and identify the pilot injector that can attain this rate. The comparison of graphs indicates that the double pilot injector outperforms the single pilot injector in achieving LTC.TECHNICAL AND OTHER SKILLSCore Domain Expertise: Data science and statistics Computing and Programming: Python, Pandas, Numpy, Matplotlib, scikit-learn, Tensorflow, Machine learning, Deep Learning, Data analytics: Data cleaning and manipulating, Data visualization, R, SQL, Power BI, Tableau, NLP, LLM, Neural Networks, Keras, Seaborn, SAS, Mongo dB, Elasticsearch, Spacy, HuggingFace, OpenAI, GenAI, Django, Flask, Twilio, HTML,CSS, Javascript, AWS, Pyspark, Databricks. CERTIFICATESMachine Learning (Organized by Stanford University) 2022Supervised Machine Learning (Regression and Classification), Advanced Learning Algorithms, Unsupervised Learning, Recommenders, and Reinforcement Learning. Certified by CourseraNeural Networks and Deep Learning (Organized by Stanford University) 2023Understanding neural network with tensor flowImproving Deep Neural Networks (Organized by Stanford University) 2023