You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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.
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.
Ten-year analysis of the S&P 500 and EURO STOXX 50 in Python: Jupyter notebook with pandas and matplotlib, OSEMN methodology, EDA, IQR outlier detection on returns and volumes.