Springboard-Data-Science-Immersive
Springboard-Data-Science-Immersive is a comprehensive repository showcasing the practical application of data science concepts completed during the Springboard Data Science Immersive program. The collection features two major capstone projects: the first utilizes web scraping, natural language processing, and deep neural networks to predict cryptocurrency prices based on sentiment analysis of news articles; the second explores computational efficiency in object detection by optimizing convolutional neural network training through image preprocessing techniques using TensorFlow. Additional modules include clustering projects demonstrating customer segmentation via K-means and Principal Component Analysis, and exploratory data analysis on diverse datasets such as hospital readmissions and human body temperatures using statistical methods like hypothesis testing and bootstrap statistics. The repository also documents foundational machine learning implementations including linear regression, logistic regression,