Prem Patel
Data Guy | Teacher link | 1x Fantasy Football Champion
Hello!
👋 Hi, I’m Prem! I’m a Data Analyst in the Fraud & Risk space at Chime and an educator at heart. Previously, I’ve worked at FEMA and Epic Systems, where I supported data analytics, automation, risk evaluation, and operational decision-making across cross-functional teams.Outside of industry, teaching has remained a constant throughout my career. I previously taught Introductory Microeconomics and Macroeconomics as an Adjunct Professor at Cypress College, served as a Teaching Assistant for Economics and Statistics during my Master’s program, and spent 3 years tutoring students during my undergraduate studies across subjects including Economics, Statistics, Linear Algebra, and Astronomy.Currently, I continue to tutor students part-time in AP Calculus, AP Microeconomics, AP Macroeconomics, Pre-Calculus, Algebra, and Statistics. I’m especially passionate about helping students build confidence by breaking down complex concepts into approachable, intuitive ideas.I’m deeply interested in continuing to teach at the university level and bringing both industry experience and a student-centered teaching approach into the classroom.📫 How to reach me: Email: [email protected] or my LinkedIn😄 Pronouns: He/Him/His
PROJECTS
Using Neural Networks to Predict Brain Tumors
Objective: Build a machine learning model to classify brain tumors using MRI scans.
Data Preprocessing: Applied techniques like image resizing and normalization to prepare MRI data for modeling.
Modeling: Utilized convolutional neural networks (CNNs) with TensorFlow and Keras for training.
Results: The model achieved high classification accuracy, indicating its effectiveness in distinguishing between tumor types from MRI images.
Using Natural Language Processing to Classify Subreddit Posts
Objective: Build a machine learning model to classify Reddit posts by subreddit based on textual data.
Data Preprocessing: Cleaned and tokenized Reddit post data to prepare for natural language processing (NLP) tasks.
Modeling: Implemented NLP techniques with machine learning algorithms like Logistic Regression and Naive Bayes.
Results: The model demonstrated strong performance, effectively classifying posts into their respective subreddits with high accuracy.
Predicting & Analyzing Injuries in the NFL
Objective: Analyze and predict injury occurrences in NFL players using game and player data.
Data Preprocessing: Cleaned and processed datasets, incorporating features like player position, game conditions, and injury history.
Modeling: Built machine learning models, including Logistic Regression and Random Forest, to predict injury likelihood.
Results: The models achieved strong predictive accuracy, offering insights into factors contributing to player injuries and enhancing injury prevention strategies.
Forecasting Bike-share Revenue
Objective: Build a model to forecast daily revenue for a bike-sharing service based on historical usage data.
Data Preprocessing: Cleaned and engineered features from the dataset, including weather conditions and user demographics.
Modeling: Applied time series forecasting models such as ARIMA and SARIMA to forecast daily revenue.
Results: The models provided accurate revenue predictions, helping inform business decisions related to demand and operational efficiency.
Predicting Home Prices
Objective: Develop a machine learning model to predict home prices based on various housing features.
Data Preprocessing: Cleaned and transformed the dataset, handling missing values and encoding categorical features.
Modeling: Implemented regression models such as Linear Regression and Decision Trees to predict home prices.
Results: The models achieved high predictive accuracy, providing valuable insights into key factors influencing house prices.
