I’m a tech-driven problem solver with a knack for uncovering insights hidden in data and transforming them into actionable outcomes. My passion for technology, coding, and design fuels my enthusiasm for tackling real-world challenges through innovative solutions.
My background as an Analytics Engineer and Machine Learning Enthusiast involves building data pipelines, developing predictive models, and integrating diverse data sources to create impactful strategies. I enjoy automating workflows, interpreting complex data, and delivering insights that enable informed decision-making. Collaboration and mentorship are key to my professional journey, as I believe shared knowledge and teamwork drive the best outcomes. Outside of work, you’ll find me on the cricket field—where I’ve learned valuable lessons in strategy, adaptability, and perseverance, which I apply in both my professional and personal life.
Let’s connect to explore how my skills and passion can help solve your most pressing challenges. Together, we can turn ideas into results!
Built predictive models integrating TalentLMS, QuestionPro, and SurveyMonkey into Snowflake, reducing data processing time by 35%.
Automated workflows using AWS Lambda and Apache Airflow, improving model training efficiency and data ingestion.
Built strong data pipelines by integrating social media APIs for monitoring tobacco discussions. Used Python, SQL, TensorFlow, PyTorch, and NLP for data analysis, applying Machine Learning for content prediction. Led on-time, under-budget project delivery, exceeding goals by 30%.
Analyzed CTE Program data, revealing strong links between program details and job rates. Categorized data for program selection. Conducted A/B Testing to fine-tune improvements, resulting in a 5x increase in enrollment and job rates, earning recognition from the NJ Education Department. Improved workforce decisions using data insights.
As the Head of Analytics Onboarding, my role encompasses guiding data-driven decisions and integrating 20+ clients successfully. I've driven substantial revenue growth for 40+ global retail enterprises through analytics, data analysis, and strategic guidance, resulting in impressive 5x to 10x increases.
My efforts extend to enhancing team efficiency with an automated email-based reporting system, reducing workload by 50%. I've showcased my expertise in data extraction, aggregation, and ETL, facilitating scalable data pipelines. Additionally, I've implemented automated verification and reconciliation systems, improving data accuracy, and conducted A/B testing for recommendation engines.
Designed a sentiment analysis model for social media activity classification. Utilized Selenium, NLP techniques, and LSTM networks for an impressive 94% accuracy rate in processing sequential data.
GPA: 3.96
CGPA: 8.1
Percentage: 83%
Unlock valuable insights from your data through cutting-edge analytics.
Seamlessly connect and unify your data sources for a complete view of your business.
Harness the power of machine learning to drive intelligent decision-making.
Transform raw data into actionable insights with stunning visual dashboards.
Crafting exceptional web experiences tailored to your unique needs and goals.
Bringing creativity to life through visually striking and effective graphic designs.
Driving impactful results through projects encompassing analytics, machine learning, ETL pipelines, and visualization for enhanced data interpretation.
This movie recommendation system, built using KNN, Slope One, and SVD algorithms, offers personalized and accurate movie recommendations to users. Developed in Python, it uses large datasets and various metrics for evaluation. The project includes a web interface for users and documentation for easy setup and contributions.
The Online Auction website is developed using JavaScript, Java, SQL Workbench, and Tomcat Server. It offers a user-friendly interface with features like bidding, item listing, user authentication, and payment processing. The project includes a documented database schema and scripts, enabling easy setup and contributions by developers.
Developed an informative dashboard to view the overall number of persons affected, recovered, and died by the devastating COVID-19 virus in a Demographic and Gender Level, as well as perform time-series analysis on this data.
Created a model to predict diabetes in patients based on parameters such as pregnancy, glucose, blood pressure, body mass index (BMI), age, and so on. 80% accuracy was projected using algorithms such as Logistic Regression, Naive Bayes, Random Forest, and SVC.
The main objective of this research is to forecast the precision of a linear regression model based on the used-car sale price. Both buyers and sellers may utilize this model to make well-informed decisions about the price of a vehicle.
Built classifiers for identifying faces and categorizing numbers. The three classifiers were that we used are Naive Bayes, Perceptron, and K Nearest Neighbor.
The goal is to examine the overall quantity of rainfall in India from 1900 and 2016. I completed the pre-processing processes by cleaning, validating, analyzing, and displaying the data to gain clear and improved insights.
Maze generation is done using DFS. The BFS and A star algorithms are used to identify a path for the agent to take in order to achieve the objective state. .
Analyzed wine quality, predicting it based on ingredients using machine learning techniques like Logistic Regression, Naive Bayes, and Random Forest, achieving a 98% accuracy.
The project's goal is to use a decentralized WebApp to monitor the drugs that are supplied to consumers. HTML, CSS, JavaScript Framework, and Solidity are used to build the Web Application.
The main objective is to build a website for the users to play games that are built using JavaScript and the Java platform. .
Driving impactful results through projects encompassing analytics, machine learning, ETL pipelines, and visualization for enhanced data interpretation.
Let's turn your ideas into reality – I'm just a message away to discuss your next project or answer any questions.