Developer Intern Python

Jaineel Pravin Parmar

Syracuse, New York 315-***-**** ********@***.*** https://www.linkedin.com/in/jaineelparmar/ EDUCATION

Syracuse University – School of Information Studies Syracuse, New York Master of Science in Applied Data Science, GPA 3.78 May 2024 Coursework: Database Management, Introduction to Data Science, Quantitative Reasoning for Data Science (Statistics), Applied Machine Learning, Natural Language Processing, Business Analytics University of Mumbai Mumbai, India

Bachelor of Engineering in Computer Science May 2019 TECHNICAL SKILLS

Programming Languages: SQL, Python, R Programming

Databases: MS SQL Server, MySQL

Skills: Machine Learning, Statistics, NLP, Exploratory Data Analysis, Git, GitHub, Microsoft Suite (Excel, Word, PowerPoint), Predictive Modelling, Feature Extraction, Feature Engineering, Regression, Classification, Clustering Frameworks / tools: NumPy, Pandas, SpaCy, NLTK, Jupyter Notebook, Visual Studio, R Studio Data Visualization: Tableau, Power Bi, Matplotlib, Seaborn, R shiny PROFESSIONAL EXPERIENCE

Database Developer, iConsult Collaborative Syracuse University Oct 2022 – Present

• Spearheaded the design, development, and maintenance of high-performance databases utilizing SQL in MS SQL Server, ensuring efficient and reliable operations.

• Established a robust data integrity framework by creating and managing views, stored procedures, and triggers, resulting in consistent and accurate databases.

• Crafted insightful reports using Tableau, empowering clients to make data-driven decisions with ease and precision. Database Consultant, Ulysses Systems, Mumbai, India Dec 2020 - Nov 2021

• Collaborated with a team of 14 members in developing and optimizing innovative features for the Task Assistant Application

• Streamlined the software experience for clients by debugging SQL queries and modifying SQL scripts with T-SQL queries, leading to increased efficiency and productivity.

• Leveraged data visualization tools, including Tableau and Power BI, to deliver customized visualizations to clients, providing them with deeper insights into their business operations and driving better decision-making. Python Developer Intern, Try Catch Classes, Mumbai, India Jan 2020 - Mar 2020

• Designed and implemented software solutions using Python programming language, including leveraging machine learning techniques to analyze and interpret large datasets.

• Contributed to data analysis and visualization projects, utilizing Python libraries such as Pandas, NumPy, and Matplotlib to extract insights and communicate findings to stakeholders. Backend Developer Intern, CSISM Technologies, Mumbai, India June 2019 - Dec 2019

• Architected new databases and tables in MS SQL Server using SQL to meet clients' specific needs, ensuring precision and completeness of data.

• Collaborated with clients to design insightful reports, leveraging advanced visualization techniques in Excel to deliver high- quality results that met or exceeded their expectations. RELEVANT PROJECTS

Predicting Healthcare Insurance Charges with Machine Learning Sep 2022 - Dec 2022

• Developed a highly accurate Extra Trees Regression model using the scikit-learn library in Python to predict healthcare insurance charges.

• Conducted data pre-processing by standardizing numerical features and encoding categorical variables using Python libraries such as Pandas, Matplotlib, Seaborn, and NumPy to uncover valuable insights between various features and insurance costs.

• The model achieved a MAE of 2,361 and RMSE of 5,643 in predicting insurance charges on test data. Stock Market Forecasting Using Predictive Analytics Feb 2022 - May 2022

• Utilized advanced data exploration techniques with Pandas and NumPy libraries to conduct an in-depth exploratory data analysis on historical Stock Market Pricing dataset, identifying key predictors of stock prices with a high degree of accuracy.

• Applied Linear Regression in Scikit-learn library in Python to generate future predictions of stock prices, achieving an accuracy of 85%.

Twitter Sentiment Analysis Jan 2020 – Mar 2020

• Employed advanced data cleaning and pre-processing techniques, including regex (re) and TextBlob, to ensure the accuracy of the analysis by removing noise from the data.

• Developed a sentiment analysis model using machine learning algorithms that accurately classified Twitter data as positive, negative, or neutral, with an impressive accuracy rate of 82%.

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