Machine Learning for Prediction


Machine Learning for Prediction

What is machine learning?

The learning ability is the basic tool for acquiring knowledge and one of the important signs of human intelligence. Learning is integrated by different mental activities like thinking, perception, feeling, and memory.  Machine Learning(ML) is the primary approach to make the computer intelligent.

ML studies how to utilize machines and computers to imitate human brain learning functions and also to find self-improvement methods for computers to acquire new knowledge and new skills, identify existing knowledge, and continuously improve performance and achievement. The machine learning method focuses on the development of computer programs that can access data and use it to learn for themselves.

Nowadays we are surrounded by applications that are using machine learning methods. ML helps the mail services to detect spam and stop unwanted emails from reaching our inboxes. Websites suggest products and movies and songs based on what we bought, watched or listened to before, and smartphones identifying faces while taking photos or unlocking themselves.

Machine learning methods

Machine learning methods are divided into three main categories.

1.Supervised learning

Supervised algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system can provide targets for any new input after sufficient training.

This method needs less data than other methods, and the training process is easier. But, labeled data is expensive to prepare, and there’s the danger of overfitting, or creating a model so closely tied and biased to the training data that it doesn’t handle variations in new data accurately. Supervised learning uses classification and regression techniques to develop predictive models.

2.Unsupervised learning

Unsupervised algorithms are used when the training data is neither classified nor labeled. Unsupervised learning finds hidden patterns or intrinsic structures in data. Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data.

3. Semi-supervised learning

Having a labeled data set is an advantage, but most of the data sets in the real world are a mixture of labeled and unlabeled data. Semi-supervised learning is between unsupervised learning and supervised learning and combines a small amount of labeled data with a large amount of unlabeled data during training.

4. Reinforcement learning

Reinforcement machine learning is about taking proper action to maximize reward in a specific situation. In the supervised learning method, the training data has the answer key, and the model trains with the correct answer itself. The data set in reinforcement learning has no label, hence the reinforcement agent decides what to do to perform the given task.

In the absence of a training dataset, it is bound to learn from its experience.

Natural Language Processing and robotics are examples of this learning method.

How machine learning works

Implementing machine learning applications have four fundamental steps, choosing and providing a data set, training the data, choosing the best algorithm for each specific case, training the algorithm, and creating the model, using and improving the model.


  1.  Preparing data is a difficult and also important step in any ML project. The reason is that each dataset is different and highly specific to the project. The training data is the main object in machine learning, so having a clean and tidy data set is crucial. Selecting the right data must be the first step in preparing process. The choice of data entirely depends on the problem which the project is trying to solve.

The next step is handling missing data because the data sets are not perfect. Handling missing data in the wrong way can cause disasters.

The data set should also be divided into two subsets: the training subset, which will be used to train the application, and the evaluation subset, used to test and refine it.


    2. There are many algorithms for machine learning with different attributes. Choosing the best algorithm for each project is very significant because each project has distinct circumstances. The main criteria for deciding is the type and amount of the data. There are some of the most used algorithms in the following:

Regression algorithms

Regression is a method of modeling a target value based on independent predictors. This method is mostly used for forecasting and finding out cause and effect relationships between variables. Linear and logistic regression are examples of regression algorithms. Simple linear regression is a type of regression analysis where the number of independent variables is one, and there is a linear relationship between the independent(x) and dependent(y) variables. Logistic regression used when the dependent variable is binary in nature: A or B

Decision trees

The decision tree algorithm could be used in both regression and classification tasks. The algorithm keeps splitting the data into smaller parts until data has single instances. This process resembles a tree with Stems and leaves. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity.

K-Nearest Neighbor

 Although K-Nearest Neighbor(KNN) is utilized in both classification and regression projects, it is mostly used in classification problems in the industry.  KNN is used by making use of data from a sample set whose classes are known. The distance of the new data to be included in the sample data set is calculated according to the existing data and k number of close neighborhoods are checked.

Neural networks

Neural networks are the imitation of the human brain where hundreds of billions of interconnected neurons process information in parallel. The neural networks are consist of three main layers. The first layer, which is for receiving the inputs, is the input layer. And The last layer is called the output layer. The middle layer, where most of the complex calculations take place, is called the hidden layer. The hidden layer could be one or more layers. By increasing the number of hidden layers, the network could solve more complex problems. Artificial neural networks utilize activation functions for dealing with nonlinearity cases.


 3.  The next step after choosing the proper algorithm is training data or a portion of data. Training the algorithm is an iterative process–it involves running variables through the algorithm, comparing the output with the results it should have produced, adjusting weights and biases within the algorithm that might yield a more accurate result, and running the variables again until the algorithm returns the correct result most of the time.


 4.  The last step is to use the model with new data and, in the best case, for it to improve in accuracy and effectiveness over time. Where the new data comes from will depend on the problem being solved. This new data could be part of old data, which split before, and the model did not see this portion of data.


As mentioned before, we can see everywhere the example of using machine learning. Since most of the industries are working with big data, machine learning is also valuable on the industrial scale.

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