Name: Abhi Patel
Type: User
Company: MCS, Arizona State University
Bio: Enthusiastic code conjurer passionate about turning lines of logic into impactful solutions. Constantly evolving, one commits at a time.
Location: Tempe, Arizona, United States
Blog: [email protected]
Abhi Patel's Projects
Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.
Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. Complete lifecycle of ML project included.
Explore Java from basics to Object-Oriented Programming (OOP) concepts with practical examples and detailed explanations. Dive into approximately 55-60 Java programs covering topics like variables, control flow, arrays, OOP principles, exception handling, and more. Ideal for learners looking to solidify their understanding of Java fundamentals.
Java Inheritance Employee Management: Explore inheritance in Java with classes representing employees, including analysts and salespersons, showcasing salary raises and bonuses.
Explore the complete lifecycle of a machine learning project focused on regression. This repository covers data acquisition, preprocessing, and training with Linear Regression, Decision Tree Regression, and Random Forest Regression models. Evaluate and compare models using R2 score. Ideal for learning and implementing regression use cases.
Sentimental Insights: Python-based sentiment analysis tool without NLTK. Analyzes text, extracts sentiments, visualizes results using matplotlib. Simplified, efficient, and insightful. No-NLTK Sentiment Analyzer.
Snake Game: A classic implementation of the popular Snake game in Java using Swing. Control the snake, eat apples, and avoid collisions to win!
Utilizing SVM for breast cancer classification, this project compares model performance before and after hyperparameter tuning using GridSearchCV. Evaluation metrics like classification report showcase the effectiveness of the optimized model.
Python script conducts sentiment analysis on Twitter data via GetOldTweets3, omitting NLTK. It preprocesses, analyzes sentiments, and visualizes results with a bar graph.