It's a machine learning challenge given by Correlation One Company
Given the stocks dataset that consists of the (simulated) daily open-toclose changes of a set of 10 stocks: S1, S2, …, S10. Stock S1 trades in the U.S. as part of the S&P 500 Index, while stocks S2, S3, …, S10 trade in Japan, as part of the Nikkei Index.
The task is to build a model to forecast S1, as a function of S1, S2, …, S10 .
You should build your model using the first 50 rows of the dataset.
The remaining 50 rows of the dataset have values for S1 missing:
you will use your model to fill these in.
This project contains following files:
- MachineLearningTest.pdf = This file includes the objective and questions related to this project
- stock_returns.csv = This is a data file which includes 100 rows (50 train, 50 test) of stocks data.
- MC.R = This file includes all the code related to this project in R Statistical language.
- Machine Learning Challenge - Correlation One Final.pdf = This file contains answer to the test questions.
- BestModel_KnnRegPrediction.csv = This is the prediction of stocks using KNN Regression Model.
- AllModelPredictionWithDate.csv = This file has predictions of all other models with respect to date.