Machine Learning for Predicting house prices

Regression is one of the major machine learning and statistics tools which allows you to make predictions from data by learning the relationship between the data attributes and some observed, continuous-valued responses. Regression is applied to analyze and solve many real-life problems such as predicting stock prices, forecasting weather, etc. In this talk, I will discuss some approaches to performing the regression task through a specific case study: predicting the housing prices in Boston in the 1970s based on several attributes such as pupil-teacher ratio in schools, levels of crime, etc.

Time

Start: 2017-06-25 03:00
End: 2017-06-25 04:15

Speaker

PHD in University of Washington in St Louis
What do I do

I'm currently a PhD student at Washington University in St Louis, majoring in Machine learning

How can I help

Discuss Machine learning problems, algorithms analysis

Discuss