| dc.description.abstract | This project is based on preparing machine learning model random forest
regression to understand the relationship between gold price and selected factors
influencing it, namely stock market, crude oil price, dollar/euro ratio, gold price
and silver price. All the operations to train the model are performed using Google Colaboratory. Monthly price data used for period was used for the study the dataset is collected in
csv file format from kaggle. The data was further split into two periods, training data and testing data using
sklearn library and also use it to import random forest regressor to predict the price
of gold using different factors that are influencing gold price. Machine learning algorithms, random forest regression was used in analyzing
these data. It is found that the correlation between the variables is strong during the
period I and weak during period II. While these models show good fit with data
during period I, the fitness is not good during the period II. While random forest regression is found to have better prediction accuracy for the
entire period. We will get the the accurate data of price after training and testing of
model. | en_US |
