Real estate valuation is often complex and subjective, making it difficult for buyers and sellers to determine the true value of a property. In this project, we analyze the Ames Housing Dataset, which contains 1,460 observations and 81 features, to provide data-driven insights into the housing market. Our study focuses on two core business objectives: Regression Task: Can we accurately predict the exact SalePrice of a house based on its attributes? Classification Task: Can we distinguish between "High Value" (above median price) and "Low Value" properties? We followed a structured data science pipeline (CRISP-DM), moving from rigorous Data Cleaning and domain-specific Feature Engineering to training advanced machine learning models, including Random Forest and Logistic Regression. This report details our methodology, model comparisons, and key findings regarding the drivers of house prices.

Predictive Analytics on Google Play Store Apps: Rating Prediction and Popularity Classification

testing

Not able to use Bioconductor to get the barcode ID_REF gene name for use in exploring known genes of the top 41 genes found to play a role in predicting with 100% accuracy if a sample has MS or is healthy. But reviewed the process of transcription and translation of a protein from mRNA when transcribed from its triplet codon sets by transfer RNA at the ribosome. Gaps of 1-2 RNAs were unpaired to a codon so gsub wasn't the best choice, but these barcodes are fragments and maybe those are deletions, insertions, or translations in genes that are risk associated genes for MS, or better yet are found in those with MS.