This repository includes multiple data science projects.
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge.
This project focuses on sentiment analysis of movie reviews, aimed at determining the underlying sentiment expressed within a body of text. By analyzing the content of movie reviews, we strive to classify each review as positive or negative automatically.
MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
This is Kaggle's competition: Digit Recognizer, and I rank top 25% on this project.
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
This is Kaggle's competition: House Prices - Advanced Regression Techniques, and I rank top 35% on this project.
Jonathon, an insurance company serving nationwide, leverages extensive datasets in the insurance and finance sector. These datasets feature a unique label assigning a Customer Lifetime Value (CLV) score to clients who have purchased insurance derived from various factors. This initiative underscores Jobathon's commitment to harnessing data-driven insights to enhance service delivery and customer satisfaction.