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Predictive Analytics: How Machine Learning is used in Data Science

A subset of advanced analytics called predictive analytics uses historical data along with statistical modeling, data mining, and machine learning to forecast future results. Using trends in this data, businesses use predictive analytics to detect problems and opportunities. Big data and data science are frequently linked with predictive analytics.

Machine learning and data science are two related but distinct fields in the realm of artificial intelligence and data analytics. Machine learning involves using algorithms to enable machines to learn from data and make predictions or decisions based on that learning. Data science involves using statistical and computational methods to analyze and interpret complex data sets, derive insights, or solve real-world problems.

An example of predictive analytics in action is in the field of healthcare. Predictive analytics can be used to identify patients who are at high risk of developing certain conditions, such as diabetes or heart disease. The algorithms analyze patient data, such as medical history, lifestyle factors, and genetic information, to make predictions about future health outcomes. Healthcare providers can then intervene early to prevent or manage the condition, improving patient outcomes and reducing healthcare costs.

What are the different predictive modelings?

1. Classification models

The category of supervised machine learning models includes classification models. These models describe relationships within a particular dataset and classify data according to historical data. Common applications for this include fraud detection and credit risk assessment. Logistic regression, decision trees, random forests, neural networks, and Naive Bayes are examples of categorization models.

2. Clustering models

Clustering Models are a type of unsupervised learning. They classify data according to comparable qualities. K-means, mean-shift, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering with Gaussian Mixture Models (GMM), and hierarchical clustering are examples of common clustering techniques.

3. Time series models

Time series models employ different data inputs at predetermined time intervals, such as daily, weekly, monthly, etc. Time series models include the autoregressive (AR), moving average (MA), ARMA, and ARIMA types. A call center, for instance, might use a time series model to predict how many calls it will get per hour at various times of the day.

What are the key differences between predictive analytics and data science?

Use cases of Predictive Analytics in Data Science

1. Banking:

When forecasting credit risk and identifying fraud, financial services use quantitative and machine learning methods. BondIT specializes in fixed-income asset management services. They can accommodate both static market limits and dynamic market changes in real-time implementing predictive analytics. Through the use of technology, it can reduce risk and personalize services for each client.

2. Healthcare:

In the field of healthcare, predictive analytics is used to monitor specific infections like sepsis as well as to identify and manage the care of individuals with chronic illnesses. To better understand the detection and management of sepsis, Geisinger Health mined medical information using predictive analytics. Based on the medical records of more than 10,000 patients who had previously been given a sepsis diagnosis, Geisinger developed a predictive model with a high survival rate.

3. Human resources (HR):

To match potential job applicants, lower employee turnover, and boost employee engagement, HR professionals use predictive analytics and employee survey measures. Businesses may save hiring costs and boost employee happiness by combining quantitative and qualitative data, which is especially helpful when labor markets are unstable.


Data science and machine learning can collaborate. Think about how machine learning is defined as the capacity of a machine to interpret knowledge from data. Machine learning is severely limited in the absence of data. If anything, the expansion of machine learning across many industries will spur data science to become more relevant. Machine learning is only as effective as the data provided and the algorithms’ capacity to use it. A fundamental understanding of machine learning will soon be expected of all data scientists.

At Sambodhi, you will gain practical experience using several in-demand machine-learning techniques from experienced industry experts. You work with data as you learn because of our robust case study based training methodology. 

This training program includes industry projects, virtual labs, and instructor-led training, which is all you need to become an expert in machine learning and data science. So gear up. It’s time for you to earn your certification and conquer the world.

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