12 Algorithms you can use to predict the stock price using Machine learning

You may be wondering what algorithms you can use to make stock price predictions. Well, in this short article, we will be discussing the top 12 Machine learning algorithms that you can use in order to create a predictive model that can predict the stock market price. You can read the full article along with the Python code from this article.

Before going to the list of top 12 algorithms, I assume that you have a basic understanding of Python and Machine learning.

1. Long Short Term Memory Networks

The first algorithm in our list is the Long Short Term Memory Network which is a Neural network that is capable of learning order dependence in sequence prediction problems. This is a behavior required in complex problem domains like machine translation, speech recognition, and more. LSTMs are a complex area of deep learning. Check out the implementation using Python from here.

2. Gaussian process regressor

In the field of machine learning, Gaussian process regression (GPR) is a nonparametric, Bayesian method of regression that is causing a stir. GPR has a number of advantages, including the ability to evaluate prediction uncertainty and doing well on tiny datasets.

3. RANSAC algorithm

An iterative procedure known as random sample consensus is used to estimate the parameters of a mathematical model from a set of observed data that includes outliers when outliers should have no bearing on the estimates’ values. As a result, it can also be considered a technique for finding outliers as well.

We can also use the RANSAC algorithm to predict the stock market price using Python.

4. CatBoost algorithm

CatBoost is a technique for decision trees that uses gradient boosting. It is created by Yandex researchers and engineers and is used for a variety of jobs at Yandex and in other businesses, such as CERN, Cloudflare, and Careem taxi, including search, recommendation systems, personal assistants, self-driving cars, weather prediction, and many more.

We can use CatBoost algorithm to make predictions for the stock market using Python.

5. LightGBM

The distributed gradient boosting framework for machine learning known as LightGBM, or Light Gradient Boosting Machine, was created by Microsoft and is free and open source. It is used for classification, ranking, and other machine learning applications and is based on decision tree algorithms.

We can use the LightGBM to make stock price predictions using Python.

6. Extra tree regressor

The next algorithm in our list is the Extra tree regressor. An extra-trees regressor. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. We can also use an Extra tree regressor to predict the Stock market price using Python.

7. XGBoost

The next algorithm in our list is XGBoost which is a very popular boosting algorithm. XGBoost, which stands for Extreme Gradient Boosting, is a scalable, distributed gradient-boosted decision tree (GBDT) machine learning library. It provides a parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems. But we can also use this algorithm to make predictions for the stock price using Python.

8. Gradient boosting algorithm

Gradient boosting is a greedy algorithm and can overfit a training dataset quickly. It can benefit from regularization methods that penalize various parts of the algorithm and generally improve the performance of the algorithm by reducing overfitting. It can also be used to predict the price of the Stock market using Python.

9. AdaBoosting algorithm

AdaBoosting is considered to be the first boosting algorithm. AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level which means Decision trees with only 1 split. These trees are also called Decision Stumps.

We can use the AdaBoosting algorithm to predict the price of the stock market using Python.

10. Random forest regressor

The Random Forest Algorithm is a type of Supervised Machine Learning algorithm that builds decision trees on different samples and takes their majority vote for classification and average in case of regression. We can also use the Random Forest Regressor for the stock price predictions as well.

11. Decision tree for regression

The decision tree is the most commonly used and famous algorithm. Many boosting algorithms use decision trees as weak learners.

They are also helpful to predict the stock market price.

12. Linear regression

Last but not least, is the linear regression algorithm which is the simplest algorithm to make predictions about the regression dataset.

We can also use Linear Regression to predict the price of the stock market.

NOTE: All the algorithms mentioned above may not give good results when used with the default parameter values. So, it is recommended to do hyperparameter tuning in order to get good results.

Summary

I hope you like these top 12 algorithms that we can use in order to predict the price in the stock market.