Building A Gold Price Prediction Model Using Machine Learning

This article was published as a part of the Data Science Blogathon

Introduction :

Hello Readers, hope you all are doing well; In this article, we will be making a project on, how to analyze and predict, the upcoming prices of Gold, using Machine Learning’s Random Forest Regressor. Sounds fascinating? Let us get on with it then..!!

A Brief about Random Forest Regression : 

A simple yet crisp definition, to understand what Random Forest Regression Algorithm is, will be, “Random Forest Regression is a supervised learning algorithm that uses ensemble learning method for regression. It operates by constructing several decision trees during training time and outputting the mean of the classes as the prediction of all the trees“,  as stated in the levelup.gitconnected’s article.

The flow-chart depicting Random Forest Algorithm is shown below :

image source: Link

Let’s Begin with our project : 

Now that we know what we are going to use, let’s not with any further.

Please note, if you find this article is becoming monotonous, you can also refer to this video below. It traverses through everything in this article with a little more detail and will help you clear your concepts, even if you are a novice to programming, and understand the basics of the machine-learning model.

My suggestion would be, use this article and the video as complimentary items, and refer to both of them, to have a crystal clear concept

For a better understanding of what are we going to do in this project, let us refer to a workflow to know our plan.

image source: link

As we are now aware of what lies ahead, to accomplish this task, we shall begin with the very first and the most important thing needed in machine learning, a Dataset.

What is a Dataset?

A dataset, as the name suggests, is a collection of data. In Machine Learning projects, we always need a dataset. Firstly, we need the training dataset to train our model, to help it predict. Then, we use testing datasets to predict and check how accurate is our model.

In this project, I have used the dataset available on Kaggle. One can find various such sites to download from. (Note that the larger the dataset, the more time the model will take to train. As a beginner-friendly suggestion, I will tell you to take a medium-sized dataset with not too many values, to first understand its working.)

The dataset that I’ve used in my code was the data available on Kaggle. You can also download it from the link here.

Although, you must also know, the more data you feed to the model for training, the more we can train our model, and the more accurate our results come out to be, but also, at the same time, the compilation time increases, and if you are a beginner, you may lose your enthusiasm in that tedious times. Don’t worry if all of this sounds weird to you, it did the same to me when I started too, it will all make sense in a few minutes :).

Let’s Start Coding : 

As we will be using python for this project, we will also be needing a suitable environment for our code to run. You can use any environment that you prefer (E.g. Pycharm, VS Code, Sublime, etc.). In my case, I’ve used Google Collaboratory, as it removes the tedious process of compilation in the computer itself, and any type of code can be run very easily . All you need for using Google Colab is a stable internet connection.

Importing the dependencies : 

The first thing that we have to do, is we have to import the necessary dependencies, that we will be using in the upcoming part of the program. Here in this project, we shall be using numpy, pandas, matplotlib, sklearn.

As we proceed with our project, you will get to know the use of each of these modules.

Reading The Data from the Dataset : 

Downloading the dataset if not all, we need to feed the downloaded dataset into our program, so that our code can read the data and perform the necessary actions using it.

As the dataset file downloaded, is in the form of a CSV file, to read it, we will be needing the pandas module. It comes with a method read.csv() to read CSV files.

Let’s store it in a variable named ‘gold_data‘

To get a look at how the data was stored in the variable, we use the command variable_name.head(), to see the first five rows of the table.

The meaning of the Column values (SPX, USO, etc.) can be found on the website from which we have downloaded the dataset.

We saw on Kaggle while downloading the data set, that the data has 2290 rows and 6 columns.

Now, let’s check how many cells are left empty in the table, to get a better insight  : 

gold_data.isnull().sum()

And the output was :

Let’s Split the data into target values and feature values : 

X = gold_data.drop(['Date','GLD'],axis=1)
Y = gold_data['GLD']

As there were no empty cells, we could readily begin with the table manipulations;

Here, X is the feature variable, containing all the features like SPX, USO, SLV, etc., on which the price of gold depends, excluding the GLD and Date column itself.

Y, on the other hand, is the target variable, as that is the result that we want to determine,i.e, the price of Gold. (It contains only the GLD column)

Splitting X and Y into training and testing variables : 

Now, we will be splitting the data into four variables, viz., X_train, Y_train, X_test, Y_test.

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=2)

Let’s understand the variables by knowing what type of values they store :

X_train: contains a random set of values from variable ‘ X ‘

Y_train: contains the output (the price of Gold) of the corresponding value of X_train.

X_test: contains a random set of values from variable ‘ X ‘, excluding the ones from X_train( as they are already taken).

Y_train: contains the output (the price of Gold) of the corresponding value of X_test.

test_size: represents the ratio of how the data is distributed among X_trai and X_test (Here 0.2 means that the data will be segregated in the X_train and X_test variables in an 80:20 ratio). You can use any value you want. A value <  0.3 is preferred

Model Training: Random Forest Regressor : 

Here we name our model ‘regressor‘

regressor = RandomForestRegressor(n_estimators=100)

Now let us train the model, with our  values containing the training dataset which are(X_train, Y_train)

regressor.fit(X_train,Y_train)

The model trains in a way like this: “When the values of X are these, then the value of Y is this.”

Model Evaluation : 

Let’s now predict the values of the X_test dataset using the predict() method.

test_data_prediction = regressor.predict(X_test)

Calculating the R-Squared error from the predicted value. :

error_score = metrics.r2_score(Y_test, test_data_prediction)
print("R squared error : ", error_score)

The output comes out to be:  “R squared error: 0.9887338861925125”, which is an excellent score..!!

Comparing the Actual Values and the Predicted Values : 

Converting the values of Y_test into a list.

Y_test = list(Y_test)

Now, plotting values of actual prices, versus the predicted prices to know, how close ou predictions were to the actual prices :

plt.plot(Y_test, color='blue', label = 'Actual Value')
plt.plot(test_data_prediction, color='green', label='Predicted Value')
plt.title('Actual Price vs Predicted Price')
plt.xlabel('Number of values')
plt.ylabel('GLD Price')
plt.legend()
plt.show()

And the output came out to be :

Thus we can observe, that the actual prices and the predicted prices are almost the same, as the two graphs overlap each other. Thus, or model has performed extremely well..!!! Congrats..!!

Conclusion : 

As you saw in this project, we first train a machine learning model, then use the trained model for prediction. Similarly, any model can be made much more precise, by feeding a very large dataset, to get a very accurate score (but it will be pretty time-consuming). For a beginner, I feel the dataset that I had used was pretty decent.

Thanks for reading…

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Have a good day..!!

About the Author : 

Heyy, I am Pinak Datta, currently, a second-year student, pursuing Computer Science Engineering from Kalinga Institute of Industrial Technology. I love Web development, Competitive Coding, and a bit of Machine Learning too. Please feel free to connect with me through my socials. I always love to have a chat with similarly minded people.

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Till then, Goodbye, and have a good day.