Welcome to my data science portfolio, where I showcase my passion for extracting meaningful insights from complex datasets. With a strong foundation in statistical analysis, machine learning, and data visualization, I demonstrate a proven track record of transforming raw information into actionable intelligence to drive informed decision-making.
As a part of MIT’s Data Science and Machine Learning program, I engaged in a case study for ExtraaLearn, a fictional EdTech startup. Using Python, the project aimed to identify leads likely to convert to paid customers in the dynamic Online Education market. The analysis included a thorough exploration of lead attributes, employing Decision tree and random forest models to optimize ExtraaLearn’s resource allocation strategy.
Developed within MIT’s Data Science and Machine Learning program, this project is focused on constructing a recommendation system using rank-based and collaborative filtering techniques. Leveraging the Amazon product reviews dataset, the objective was to enhance the online shopping experience by providing personalized product recommendations based on customers’ past ratings. Inspired by industry leaders like Amazon, the project aimed to contribute to the ongoing advancements in recommendation systems, addressing the challenges posed by information overload in the e-commerce landscape.
In this project, I applied Python to create a rainfall prediction classifier using key machine learning algorithms. The project involves implementation of algorithms like linear regression, logistic regression, and more. The resulting comprehensive report evaluates model performance metrics.
Embarking on the IBM Data Science Specialization capstone project, I delved into predicting the success of Falcon 9 first stage landings in SpaceX rocket launches. By harnessing the SpaceX API, I meticulously cleaned and analyzed the data, applying feature engineering and exploring various machine learning models. The project’s culmination involved determining the optimal predictive model, providing valuable insights for companies competing in the rocket launch market against SpaceX’s cost-efficient launches.
As a Data Scientist within MIT’s IDSS program, I utilized Python, pandas, and numpy to analyze FoodHub’s extensive dataset on online food orders in New York. The project focused on extracting insights into restaurant demand, customer preferences, and operational efficiency. Leveraging data on order details, customer ratings, and delivery times, the analysis provided actionable recommendations to enhance FoodHub’s services and improve the overall customer experience.
In this case study, I navigated the intricacies of data analysis, showcasing proficiency in R programming. By delving into the company’s smart device usage data, I uncovered valuable insights that could shape Bellabeat’s marketing strategy. This project not only demonstrated my technical prowess but also highlighted my ability to translate complex data into actionable recommendations for informed decision-making within the health-tech industry.
In this project, I delved into the intricate world of stock analysis, meticulously examining the performance of key players such as Tesla and GameStop. Leveraging advanced data science techniques, I extracted and processed historical share prices and quarterly revenue reports. Through detailed analysis, I unearthed trends and patterns within the financial data.