[Week 1-House Price Prediction]
First Week-Introduction
Team Members: Harun Özbay, Halis Taha Şahin, Cihat Duman
This is the introduction blog of our house price prediction project of Machine Learning Course.We will regularly publish the progress of the project in Medium.
We are going to develop a python program which will estimate the prices of the houses whose features are given in the test dataset from Kaggle based on the training dataset from Kaggle as well.
The Dataset
One of the most important elements in machine learning is data. Number of features and size of data are important facts to make estimations in classification and particularly regression problems. And collecting and regulating data can be pain in the neck.Zillow’s Home Value Prediction in Kaggle -there is a link in the course web page-was one of the options. However the size of the data as well as its sparsness prevented us choosing it.Due to the fact that we have limited time and our schedule isn’t just reserved for this course project, we decided to find a more compact dataset on the internet. We found another Kaggle competition named “House Prices: Advanced Regression Techniques” which suits our demands. The training data consists of 81 features and 1460 rows.
Machine Learning Methods to be Used
We are in the stage of reviewing some articles implementing some distinct methods such as linear regression, convolutional neural networks, support vector machines,random forests etc..We will choose after we finish reviewing the papers and other sources on the internet.
