Gold has always held a prominent place in the country’s economy and among the people. In this article, I will introduce you to a machine learning tutorial on Gold Price Prediction with Python.
Gold Price Prediction
Gold prices have historically skyrocketed, peaking at $ 1,800 in 2020. These swings that gold has faced can be attributed to several events that occurred during the period. Stock prices are strongly correlated with public information and world events, and gold is no exception.
Unlike other financial assets, precious metals behave positively in times of crisis. Indeed, in the face of events such as epidemics, wars and economic disruption, most assets experience a decline in their value. However, in search of cover, investors are turning their attention to gold. As a result, the demand for yellow metal increases, resulting in a rise in the market valuation.
Gold tends to be viewed as a strategic and tactical investment asset when considering portfolio diversification. However, the economic and financial aspects are not the only factors that can affect gold prices. Geopolitical factors may also influence the valuation of the yellow metal’s market.
Indeed, show that faced with geopolitical risks, gold acts as a safe haven. In fact, he has an affirmative reaction in times of threats resulting from geopolitical and international tensions. By analyzing financial, geopolitical and global data on natural disasters, it is possible to predict future fluctuations in the price of gold, which will allow investors to improve the management of their portfolio in the face of unexpected movements. of the market.
Gold Price Prediction using Machine Learning with Python
In this section, I will take you through a Machine Learning tutorial on Gold Price Prediction with Python. I will start the task of Gold price prediction by importing the necessary Python libraries and the dataset:
|USD (AM)||USD (PM)||GBP (AM)||GBP (PM)||EURO (AM)||EURO (PM)|
Now let’s do some data preparation to fit the data properly in our machine learning model:
Using Linear Regression Model
Now as we have prepared the data to fit in a machine learning model for the task of gold price prediction, the next step is to choose a machine learning algorithm. For this task, I will use the Linear Regression algorithm:
model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test)
Now let’s plot the results of gold price prediction that we got from the linear regression algorithm:
import matplotlib.pyplot as plt out_of_sample_results = y_test.to_frame() # Add a column of "out-of-sample" predictions to that dataframe: out_of_sample_results["Out-of-Sample Predictions"] = model.predict(X_test) out_of_sample_results.plot(subplots=True, title='Gold prices, USD') plt.show()
I hope you liked this article on a machine learning tutorial on Gold Price Prediction with Python. Feel free to ask your valuable questions in the comments section below.