Pinned Repositories
Breast_Cancer
Several measurements are computed from a digitized image of a fine needle aspirate of a breast mass. The goal of this project is to classify the samples as malignant or benign. The data set is provided by UCI Machine learning repository. The overall balanced accuracy of the proposed Ensemble method on the final validation set is 98.84%.
Computer_Vision_System_Dry_Beans
This data set is provided by the UCI Machine Learning Repository at the following web address. https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset
Financial_Activities_Forecasting
Training and testing several popular forecasting models on the Financial Activities Employment data from FRED
MovieLens_Capstone_Project
This is one of my final projects for the HarvardX Data Science Professional Certificate Program. As the title suggests, it is on the GroupLense database colloquially known as MovieLens. The goal of the project is to predict ratings with a RMSE below .86490. I was able to surpass the goal with 3 different models. Happy reading!
DPershall's Repositories
DPershall/Computer_Vision_System_Dry_Beans
This data set is provided by the UCI Machine Learning Repository at the following web address. https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset
DPershall/Breast_Cancer
Several measurements are computed from a digitized image of a fine needle aspirate of a breast mass. The goal of this project is to classify the samples as malignant or benign. The data set is provided by UCI Machine learning repository. The overall balanced accuracy of the proposed Ensemble method on the final validation set is 98.84%.
DPershall/Financial_Activities_Forecasting
Training and testing several popular forecasting models on the Financial Activities Employment data from FRED
DPershall/MovieLens_Capstone_Project
This is one of my final projects for the HarvardX Data Science Professional Certificate Program. As the title suggests, it is on the GroupLense database colloquially known as MovieLens. The goal of the project is to predict ratings with a RMSE below .86490. I was able to surpass the goal with 3 different models. Happy reading!