This repository includes my House Prices Multi-Variate Linear Regression-Flatiron School Module 2 Project. In this project I made use of the OSEMN methodology incorporating packages such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn.

This repository contains different scenarios and its solutions with R programming.

Personal Finanace Analysis using OSEMN Framework.

Analisi dell'andamento decennale degli indici S&P 500 ed EURO STOXX 50 in Python: notebook Jupyter con pandas e matplotlib, metodologia OSEMN, EDA, outlier detection (IQR) su rendimenti e volumi, con insight di investimento.

Early warning system predicting student grade risk (D/F) using Random Forest — OSEMN pipeline, Flask demo, 83.3% accuracy

In this project, I used the OSEMN data science workflow to obtain, scrub, explore, model, and interpret a King County dataset with a multivariate linear regression to predict the sale price of houses.