ML professionals use prediction algorithms in machine learning to estimate future outcomes. The massive amounts of structured and unstructured data have become the key source for many businesses to make better business decisions. Predictive analysis is one of the essential subparts of machine learning as it helps organisations understand future risks and opportunities.
An automobile company can design a new product to maximise the chances of market adoption using prediction algorithms. Prediction algorithms in machine learning have helped many leading organisations avoid loss and grow their customer base. With this, the demand for ML professionals is rising rapidly in every industry.
If you want to build a career in Machine Learning, you must understand how ML algorithms are used to predict future outcomes based on past data. You can learn this thoroughly by enrolling in a Machine Learning course. Before that, let us help you understand what predictive analytics is and how it works.
Predictive Analysis is the process of estimating future outcomes using various statistical techniques, such as predictive modelling, machine learning, and data mining. In short, predictive analytics helps you understand the possible occurrence of a future event by analysing historical data. Businesses use predictive analysis to find patterns in past data and identify risks and opportunities for the future.
Predictive analytics and machine learning are often confused as the same thing, however, that is not the case. Predictive modelling uses machine learning algorithms along with predictive analytics to estimate outcomes. Though it largely overlaps with machine learning, predictive modelling cannot be termed the same as prediction ML.
Predictive algorithms use historical data to identify patterns and trends to use this information to predict future outcomes. Based on the type of data, the predictive algorithms in machine learning can be of different types are follows:
Classification models come under supervised machine learning. These models classify data based on past data and describe relationships within a particular data set. Some common types of classification models are:
A decision tree is a supervised machine learning algorithm used for multiple variable analysis. You can use decision trees for both regression and classification tasks. It starts at a single point called a node and then splits into two or more subparts called branches.
Each branch further offers multiple possible events. The process continues until an outcome called leaf node is achieved. It looks like a tree-shaped flow chart and hence the name –decision tree.
Logistic Regression is useful in predicting the probability of an event. ML professionals use it to predict the binary values (like ‘Yes or No’ and ‘0 or 1’) using an estimated data set. Here, the possible number of outputs is limited.
You either get a ‘0’ denoting non-occurrence or a ‘1’ denoting the occurrence of an event. However, you don’t get the exact values as 0 or 1, but the value is always between the two numbers. In this, an S-shaped logistics function is fitted with two maximum values (0 or 1).
A Random Forest deals with creating multiple decision trees at a time. This algorithm works for both classification and regression models. It combines the results of different decision trees to come up with a single output. Different samples are taken to build decision trees in a random forest algorithm.
Then, it takes the most votes as the output in the case of classification problems, and in regression problems, it takes the average of all results. One significant feature of a random forest is that every decision tree grows to its maximum extent with no pruning.
A neural network is a type of machine learning algorithm that uses interconnected neurons in a layered manner that mimics the human brain. It is a method to teach computers to identify relationships in a data set the way a human brain works.
Neural networks also allow computers to learn from their mistakes and improve accordingly. The most common applications of neural networks are facial recognition, handwriting recognition, speech recognition, and image recognition.
Naive Bayes is a supervised Ml algorithm that is used for binary as well as multi-class classification problems. This prediction algorithm works on the assumption that all the features in a particular class are independent of each other, even if they are related.
Prediction algorithms in Machine Learning based on Naive Bayes are easy to build and work well for large datasets. The system looks simple but is powerful enough to outperform even the top ML models for predictive analysis.
Clustering models come under unsupervised learning. These models group the data based on the same attributes, i.e., data points having the same features fall under the same cluster. Some common types of clustering models are:
Under a K-means clustering algorithm, unlabelled datasets are categorised into different clusters. Here, K denotes the number of clusters formed. All the data points in a particular cluster are homogeneous, i.e., they have the same features.
Also, these data points have different attributes from the data points present in other clusters. In this algorithm, the unlabelled dataset is the input that is divided into different clusters. The process is repeated until the best cluster is found.
A mean shift clustering algorithm is a non-parametric unsupervised Ml algorithm. It assigns data points to different clusters by shifting them towards the mode (the most frequent value in a dataset).
The most common applications of this algorithm include computer vision and image processing. Unlike the K-means algorithm, you don’t need to specify the number of clusters in advance.
The EM (Expectation Maximization) clustering algorithm is an unsupervised ML algorithm used for latent variables (variables that you can’t observe directly). This algorithm aims to estimate the mean and standard deviation for every cluster to maximise the likelihood of the observed data points.
Time Series models use different data inputs at a particular frequency, such as monthly, weekly, daily, etc. This model is used to plot the dependent variable in a dataset over time to identify trends and patterns, seasonality, cyclic behaviour, etc.
A few of the most common types of time series models are Autoregressive (AR), moving average (MA), ARMA, and ARIMA models. One example of a time series model is a call centre using this model to recognise how many calls it will receive on a particular day or at a particular time of the day.
If you aspire to become a Machine Learning engineer, the right ML course is the best way forward. Edvancer is among the top career-oriented learning platforms in India. The platform offers the following Machine Learning courses at Edvancer:
Being accessible online, these courses allow you to learn anytime and from anywhere at your convenience. You can choose to learn through live classes or the self-paced learning program. You get comprehensive coverage of all the necessary topics. Moreover, the course allows you to gain practical knowledge by working on real industry projects.
Predive analysis is the process of predicting future outcomes based on historical data. It can be done using different machine learning algorithms for prediction. These prediction algorithms include – Classification Algorithms, Clustering Algorithms, and Time Series Algorithms.
2. Can machine learning predict the future based on the past?Machine Learning algorithms can estimate future outcomes based on past data. Based on the trends and patterns in the historical data, machine learning algorithms can be used to make assumptions about the occurrence or non-occurrence of a certain event in the future.
3. Which type of machine learning is used for prediction?Both supervised and unsupervised machine learning models are used in predictive analysis. Supervised ML models include classification algorithms and unsupervised models include clustering algorithms.
4. Why is machine learning best for prediction?Machine Learning is best for predictions as it can adapt to changes quickly. As a particular data set changes, machine learning changes its predictions accordingly.
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