Assessing the Financial Performance of Copper Market and Stock Price Prediction using Python: Statistical Analysis

Among the most precious natural assets is copper, which is utilized extensively in a variety of industry segments. The complex variations in the price of copper have significant effects on numerous other sectors. This study aims to use statistical analysis of monthly copper prices based on historical prices with use a boxplot to visually portray the data, which displays the minimum, first quartile, median, third quartile, and maximum summary of the data. It helps figure out how a data collection is distributed and for locating outliers. A pair plot is another statistical technique used to visualize the correlations between various variables in a dataset. It is especially helpful in detecting trends, connections, and distributions since it produces a grid of histograms and scatterplots that enable a rapid and thorough analysis of the data. The monthly copper price dataset is from January 2017 to March 2023 from KGHM Dataset: A Comprehensive Financial and Economic Data Collection for Stock. In the dataset, the focus is on the three distinct financial indicators utilized to assess a company’s financial performance, which are net sales revenue, gross profit, and net profit. The statistical analysis is conducted using Python language.