Predicting The Stock Value

216 Comments / Artificial Intelligence, Machine Learning / By
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Machine learning is all about two things 

  • to classify things – Classification
  • to make quantitative predictions – Regression

 

Let’s say there is a huge dataset on the weather forecast

CLASSIFICATION is used to classify whether it will rain tomorrow or not.

REGRESSION is used when a quantitative prediction has to be made like the precipitation percentage.

I have taken a dataset of a particular stock.  

This is the first 5 samples of the data. (Sample: it is the data in a single row i.e. a single instance of the data)

Then I analyse the data by plotting different histograms to find the uniqueness of each attribute of the data. (Attribute is the property i.e. the column)

Next, the dataset needs to be divided into training, validation and testing subsets.

I am placing the split in the dataset in such a way that the first 60% of the data is in the train set, the following 20% is in the valid set and the remaining 20% is in the test set.

Training dataset:

As the name implies it is used to train the model. So, the difference in the output predicted and the actual output called the loss is fed into the model to make corrections.

Validation dataset:

Again, as the name implies this dataset is used to validate the model after it has been trained. It checks whether the output predicted corresponds to the real output.

Testing dataset:

This is used to test the model as a final step to check the correctness of the model. 

The dataset I have taken is unbalanced, the difference in the number of “yes” and “no” in the “worked?” attribute is high.

This can be resolved by either oversampling the one less in numbers or undersampling the one more in number.

Since generally more data is good, I am oversampling by creating a function. As the data in my dataset has some attributes in 1000s and some in 10s I am also normalising the dataset using “StandardScalar()” which brings the values of each column between 0 and 1. 

I am also splitting each subset into “x” and “y” where “x” is the input and “y” is the output. 

I have split the respective subsets and also created the F1 Score, Precision, and Accuracy array to compare their values later.

F1 Score:

It is a measure to understand the values of precision and recall with just one value. The traditional F-measure or balanced F-score (F1 score) is the harmonic mean of precision and recall.

First Classification for “worked?” (Class: “Yes” or “No”):

K-Nearest Neighbors:

The K-NN model follows the following algorithm:

  • Select the number ‘K’ of the neighbors(number of neighbors).
  • Calculate the Euclidean distance between the data point and the ‘K’ number of neighbors.
  • Take the ‘K’ nearest neighbors as per the calculated Euclidean distance.
  • Among these ‘K’ neighbors, count the number of the data points in each class.
  • Assign the new data points to that class for which the number of neighbors is maximum. 

Naive Bayes:

Bayes’ Theorem finds the probability of an event occurring given the probability of another event that has already occurred. The mathematical representation of Bayes’ theorem is as follows:

P(A|B) = P(B|A) * P(A) / P(B)

Support Vector Machine:

The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that I can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. 

I have used three classification models and now I can compare their accuracy, precision and F1 scores.

“Here, the KNN model has high accuracy, precision and f1 score. Therefore is better suited for this particular dataset.”

Subsequently, Regression for “percentage_return” (quantitative prediction):

I have defined a similar scalar and normalisation function for “percentage return”. 

Now I can use the regression models.

For regression models, I validate the model by mean squared error and R-squared(like precision, F1 score)

Mean Squared Error(MSE): 

The Mean Squared Error measures how close a regression line is to a set of data points. It is a risk function corresponding to the expected value of the squared error loss.

MSE = (1/n) * Σ(actual – forecast)^2

R-Squared:

It evaluates the scatter of the data points around the fitted regression line. For the same data set, higher R-squared values represent smaller differences between the observed data and the fitted values.

Linear Regression:

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable that has to be predicted is called the dependent variable. The variable used to predict the other variable is called the independent variable. 

Random Forest :

Random Forest contains a number of decision trees (hence the name forest) on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. 

Now I compare the MSE and R-squared values of the Models.

“The random forest model has a low MSE and a high R- squared value. Therefore it is best suited for this dataset.“

The performance of the model can be improved to get a better result.

Find the Python Notebook here.

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