Machine Learning Data Analytics

ZEEL PRAJAPATI

Email: - ***************@*****.***

Phone number: - 267-***-****

GitHub Link: -https://github.com/prajapatizeel240024 PROFILE

Proficient in data analytics with experience in managing diverse datasets and skilled in Hadoop, data modeling, and mining. Adept in machine learning and deep learning, capable of deriving actionable insights and supporting data-driven decisions. Eager to apply analytical skills to accelerate product innovation and experimentation in my next role.

EDUCATION

The Pennsylvania State University, Malvern, PA (Expected Dec-2024) Master of Professional Studies in Data Analytics

GPA: 3.9

LD College of Engineering, Ahmedabad, Gujarat (Jun-2022) Bachelor of Engineering in Computer Engineering

GPA: 3.7

TECHNICAL SKILLS

Programming Languages & Scripting: Python, R, SQL, C++, Java, and PHP

Database Technologies: MySQL, PostgreSQL, Hive, Hadoop, HBase.

Data Visualization Tools: Power BI, Tableau, Matplotlib, Seaborn, and Plotly Dash

Machine Learning & Predictive Analysis: Supervised/Unsupervised learning, Linear/Logistic Regression, Clustering, Neural Networks

Statistical Analysis & Data Mining: Regression analysis, Time series forecasting, Classification, Predictive Modeling, Anomaly Detection, Recommendation Systems, Text Mining, NLP, Data Modeling, Cross Function, Data Strategy

Data Pipeline & Integration: Proficient in end-to-end data pipeline design and management, ETL processes, data integration, workflow automation with Apache Airflow, Luigi, and data versioning tools. Skilled in data quality and consistency.

Libraries & Tools Mastery: Pandas, NumPy, Openpyxl, Scikit-learn, PyTorch, TensorFlow, Keras, NLTK, Selenium, Beautiful Soup, OpenCV, XGBoost, LightGBM

PROFESSIONAL EXPERIENCE

Penn State University Malvern, Pennsylvania

Research Assistant in the Engineering Division Oct 2023 - Present

Stock Price Prediction: - Led a pioneering project to integrate historical stock data with real-time news sentiment analysis using Large Language Models

(LLMs), aimed at enhancing stock price prediction accuracy. Spearheaded the deployment on CauldOps AI for scalable AI operations, leveraging Langchain for efficient workflow management, demonstrating a robust approach to quantifying news impact on market trends.

Generative AI: - Initiated and led the assessment of Generative AI capabilities with advanced machine learning algorithms, developing a foundational similarity matrix to enhance the understanding of historical data. This role underscored my proficiency in handling complex AI frameworks and contributing to innovative technological advancements.

Interactive Manpower Solution Pvt. Ltd Ahmedabad, Gujarat Data Analyst Oct 2022 – July 2023

Efficiency Improvements in Data Handling: - Automated data retrieval processes, boosting efficiency by 50% and improving task performance by 25%. Utilized Selenium, Pandas, and NumPy to cut manual workload by 30-40%, showcasing skills in optimizing data workflows through automation and programming.

Dashboard Development and Leadership: - Directed the creation of automated data analysis reports for NHSP hospitals, slashing report preparation time by 70%. Employed Pandas and NumPy for data manipulation, and integrated with Plotly Dash for dashboard development, demonstrating leadership in data analytics projects.

End-to-End Project Management: - Oversaw entire project phases, from conception to delivery, acting as the main client liaison and guiding teams to exceed performance benchmarks, resulting in heightened project delivery efficiency and increased client satisfaction. Fingertips Data Intelligence Solution Pvt. Ltd Ahmedabad, Gujarat Data Science Intern June 2021 – June 2022

Data Quality and Visualization Enhancements: - Streamlined data processes using MySQL, Pandas, and NumPy, and enhanced stakeholder engagement by 40% through dynamic visualizations with PowerBI and Plotly Dash.

Advanced Predictive Modeling: Spearheaded the development of machine learning and deep learning models, achieving significant accuracies (up to 87.1%) in risk segmentation and price optimization projects.

Technical Expertise: - Mastered a suite of technologies including Excel, Python, PowerBI, and various machine learning algorithms, directly contributing to a 35% increase in decision-making efficiency through data-driven insights. PROJECTS

Facial Emotion Recognition with Facial Gesture using Deep Learning (Unstructured Dataset)

Engineered a CNN model to classify emotions from 34,000 facial images, achieving 75% training and 64% testing accuracy.

Processed and normalized images using Python and NumPy, enhancing data quality for effective model training.

Implemented ReLU-activated layers within the CNN, optimizing the network to accurately detect nuanced emotional expressions.

Visualized data distributions and model performance metrics using Matplotlib and Seaborn, aiding in analytical insights and model refinement.

Documented the development process and results comprehensively on GitHub, promoting transparency and collaboration. Google Stock Price Prediction (Structured Dataset)

Data Preparation and Standardization: - Prepared and standardized historical stock price data, applying feature scaling to ensure model accuracy, using Numpy and Pandas.

Advanced Model Implementation and Performance: - Developed an RNN LSTM model to predict stock price movements, achieving a notable R2 Score of 0.76, reflecting strong predictive performance in financial modeling.

Project Documentation and Knowledge Sharing: - Elaborated on the modeling process on GitHub, illustrating the capability to translate machine learning solutions into actionable financial insights.

Fraud Detection System Development and Analysis:

Data Analysis & Engineering: - Performed detailed feature engineering and analysis on a large financial transactions dataset, setting the stage for effective model training.

Machine Learning Pipeline Creation: - Created a comprehensive pipeline using advanced machine learning algorithms like LightGBM and XGBoost for fraud detection, showcasing skill in developing and implementing predictive models.

Model Training & Evaluation: - Attained high accuracy and reliability in fraud detection, evidenced by excellent F1, AUC, and precision scores, demonstrating the effectiveness of the models in real-world applications. EXTRACURRICULAR ACTIVITIES

Advanced to the second round of the Humana Competition in September 2023, ranking in the top 50 out of 200 teams, and collaborated in a trio for the case study."

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