Prem Patel
Data Scientist | Teacher | Tutor | 1x Fantasy Football Champion
Hello!
Hi, I’m Prem! I am a Data Scientist by day and Educator by ... later in the day. I have previously taught as an Adjunct Professor in Economics at the Community College level and worked as a Financial Data Analyst.Currently I work as a Data Analyst in the public sector. In my spare time, I tutor math and economics courses.
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.
