In this exercise I am going to use linear regression techniques to predict the target variable (SalePrice). For this effect I will be using two datasets from kaggle. One is the train dataset, wich we will use to prform all the data preparation and modeling. The other one is the test dataset, that we will use at the end of the exercise to make our predictions. To make this possible I will be using R and jupyter notebook. All the needed packages are specified on the script.

Authors: Rodrigo Sarroeira

Keywords: regression, price, predict, machine learning

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In this notebook I will implement an K-Nearest Neighbors algorithm. KNN algorithm was created by Evelyn Fix and Joseph Hodges in 1951, it can be used for both regression and classification problems. In this example I will be using KNN for classification, since the problem consists on determining whether a person has diabetes or not. The data set in use is "diabetes.csv". This csv file contains medical information related with the diabetes disease.

Authors: Rodrigo Sarroeira

Keywords: Knn, health, machine learning, code

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