The amount of data generated is growing over time. Gaining valuable insights by manually analyzing massive amounts of data can be time-consuming and challenging. Data analytics has not been left behind with most manual tasks being automated. Machine learning algorithms have been developed to help perform complex data analytics tasks.
But have machine learning solutions succeeded in data analytics? Yes – ML algorithms have revolutionized data analytics, enabling businesses to extract meaningful insights from data. This has helped them make informed decisions based on complex and large datasets. But what are some popular machine learning algorithms and their use cases in data analytics? Let’s find out.
Top 7 Machine Learning Algorithms and Their Use Cases in Data Analytics
Table of Contents
Logistic Regression
Logistic regression is a machine learning algorithm used for predicting the probability of an event occurring based on input variables. In simple terms, logistic regression is employed to determine whether a specific input belongs to one group or another.
This ML algorithm is suitable for binary categorization, not predictive modeling. It allows you to allocate input data to one of the two classes depending on a defined threshold and the probability estimate.
Use Cases in Data Analytics
Logistic regression is useful in data analytics in various ways, including the following:
- Detecting fraud. It can classify tasks as normal or suspicious, helping you identify potentially fraudulent activities.
- Customer churn.
- Sentiment analysis.
Linear Regression
This machine learning algorithm is used to predict values that fall within a linear or continuous range. It is derived from statistics and establishes a connection between input variables and an output variable. In machine learning, this algorithm quantifies the relationship between input variables (x) and output variables (y).
Linear regression helps predict numerical values based on historical data and identify patterns or trends. This ML algorithm is applied in data analytics solutions in different sectors, including finance, marketing, and economics. Also, it provides meaningful insights and acts as the building block for more advanced ML algorithms.
Use Cases
Here are some common use cases of linear regression in data analytics:
- Estimating housing prices based on factors such as size and location.
- Forecasting sales based on marketing expenditure.
- Determining the impacts of different variables on business performance.
Decision Trees
As the name suggests, this machine-learning algorithm resembles a tree with a root node, internal branches (nodes), and leaf/end nodes. Decision trees are commonly used in predictive modeling and classification tasks.
The root node poses a specific question about the data. The data is directed downwards to different branches to the next internal nodes depending on the answer. These nodes ask additional questions, guiding the data to the subsequent branches. This cycle continues until the data reaches a leaf or end node, where no further branching happens.
Decision trees are beneficial because they handle categorical and numerical data. Also, they are easily interpretable and can capture complex interactions between variables.
Use Cases
In data analytics, decision trees are used for various purposes, including the following:
- Customer segmentation
- Credit scoring
- Medical diagnosis.
Naïve Bayes
Naïve Bayes is a series of supervised machine learning algorithms used to create predictive models for multi or binary classification. This ML algorithm is based on Bayes’ Theorem and functions on conditional probabilities. It estimates the likelihood of a specific classification based on the merged factors and assumes independence between them.
The Naïve Bayes ML algorithm can generate robust results and requires minimal computational resources. Its effectiveness and simplicity make it a popular choice in cases where real-time decision-making based on data is crucial.
Use Cases in Data Analytics
Naïve Bayes is often used in data analytics for various purposes, such as:
- Sentiment analysis
- Customer Review analysis
- Email Filtering
- Document categorization.
K-Means Clustering
This ML algorithm uses the concept of proximity to detect clusters or patterns in data. It groups data points based on how close or similar they are to each other. The algorithm iteratively allocates data points to clusters by reducing the within-cluster sum of squares.
Use Cases in Data Analytics
The k-means clustering algorithm is valuable in data analytics in various domains, such as:
- Customer segmentation
- Document clustering
- Image compression
- Anomaly detection
Random Forest
Random forests are an ensemble learning technique that combines several decision trees to make predictions. This ML algorithm is versatile and powerful, making it a popular choice in data analytics services. Instead of depending on one decision tree, random forests depend on several decision trees, enabling them to make more accurate forecasts.
Numerous decision trees are individually trained using distinct random samples from the training data set through a bagging sampling method. Each decision tree in a random forest generates a prediction. The random forest then tallies the results and takes the most frequent prediction as the final for the dataset.
Random forests solve the overfitting issue related to individual decision trees. Overfitting occurs when a decision tree gets closely aligned with its training data. This makes it less accurate, especially when presented with new data.
Uses Cases in Data Analytics
- Customer behavior prediction.
- Disease diagnosis.
- Natural disaster prediction.
- Credit card fraud detection.
Support Vector Machine (SVM)
SVM is a supervised machine learning algorithm often used in predictive modeling and classification tasks. This algorithm works by establishing a binary decision boundary known as a hyperplane. In 2-D space, a hyperplane is a line that divides two labeled datasets.
SVM finds the best possible decision boundary by optimizing the margin between two labeled data sets by looking at the widest space between the data classifications. New data points that fall on either side of the hyperplane are categorized based on the labels in the training dataset.
Hyperplanes can take different shapes when plotted in a 3-D space. This allows the SVM algorithm to manage more complex relationships and patterns within the data.
Use Cases in Data Analytics
Here are some common use cases of support vector machine algorithms in data analytics:
- Bioinformatics
- Image classification
- Stock market prediction
- Text categorization.
Final Thoughts
Machine learning has significantly revolutionized data analytics solutions. Numerous ML algorithms are used in data analytics, including K-means clustering, linear/logistic regression, and SVM. Each algorithm serves best in specific use cases. Therefore, the choice of ML algorithm should depend on the problem at hand, desired outcomes, and dataset characteristics. Also, it is essential to test, assess, and select the right machine-learning algorithm based on the scenario.
