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Machine Learning and Predictive Modeling

Machine learning and predictive modeling are a part of artificial intelligence and help in problem-solving or market research. The models can be used together by a business for making intelligent business decisions.

Predictive Modeling

Predictive modeling is a part of predictive analytics. Predictive modeling uses mathematics and computational methods to develop a predictive model to examine and make probabilities.

Historical datasets and current data get fed into the model for analysis. As newer data becomes available, that gets included in the model for revised analysis.

There are several predictive models used. It depends on the client’s requirements—the two categories, parametric and nonparametric.

The Predictive Modeling Techniques

Data scientists use predictive models by data sampling. The data points in the sample help develop the predictive model needed.

There are a few noteworthy predictive modeling examples used:

  • Time-series Analysis: This method illustrates the data to analyze the time series for the statistical output.
  • Decision Trees: This is a flowchart. It is a statistical method often used in business decisions or otherwise. The leaf node holds a class label, the internal node denotes a test, and the branch represents an outcome of the trial.
  • Logistic Regression: This is a statistical method too. The data parameters are predicted based on older data sets in a logistical model.
Making a Predictive Model

There are a lot of predictive models that are in use. Finding the best model depends on certain factors, especially the data. There are steps to follow before the predictive analysis. These steps are:

  • Data acquisition and removing outliers.
  • Look at the data and decide between parametric or nonparametric predictive models.
  • The data acquired needs to be formatted appropriately for the predictive model.
  • The model needs training. The data scientist will choose a data subset for this.
  • The parameters need calibration. The data subset selected will be used for this also.
  • The model efficacy needs testing, so predictive model performance testing is necessary.
  • The next step is validating the model using a newer data set from the data.
  • Once every test is cleared, the model is ready.
Evaluation of Predictive Model

Cross-validation methods for the evaluation of the model take place. The data is split into smaller datasets for training, testing and validating the predictive model.

The model needs training using the training dataset. Later, it is given the testing dataset for performance testing. Lastly, using the validation dataset, the neutral estimation accuracy is tested.

The evaluation testing is a cycle that keeps happening when newer data is available. All the datasets are test datasets. The historical datasets become training data, while the current data becomes the validation data. This cycle for historical data increases the efficiency and accuracy of the predictive model.

Use of Predictive Modeling in Sectors

Several industries use predictive modeling. It is commonly used for customer management as they are the driving force for any business. We will take a look at these sectors.

  • Financial Sector. Financial predictive models are used to make investment decisions—these investments for buying/selling stocks using historical data predictive trends. The use of predictive modeling started after the financial crisis in 2007. The Bond ratings and other indicators proved unreliable when the crisis hit.
  • Geospatial Sector. This sector uses environmental factors and past data to predict the location of events.
  • Insurance Sector: The predictive model turns data into pricing and risk evaluation insights. It helps identify customers at risk of cancellation, fraud and outlier claims. It helps identify insurers’ needs which improve customer satisfaction too. The model helps in budget management and identifying potential markets as well.
  • Healthcare Sector. The predictive model helps identify high-risk patients who benefit most from treatment. The model tracks patients who skip appointments without prior notice. The model helps form clinical trials and predict existing tests’ results. The predictive model can help with the safe dosing of medicine for the patients.
Machine Learning

Machine learning is a subfield of computer science. It is a method that uses data and algorithms to imitate the way a human learns, thinks and improves, which is why big data is a vital part of machine learning.

Before machine learning algorithms, the machines would implement simple coded tasks without logic, like artificially intelligent machines. Businesses that used AI machines could only automate low-level tasks.

With machine learning, AI advancements and developments rapidly took place. The systems started evolving with each change and not just what they were programmed to do.

Machine learning can evolve, so it is a subset of artificial intelligence. Machine learning can go through large datasets and extract information. Using the extracted data, the machine using these ML algorithms evolves itself.

How Machine Learning Works

There are three parts to machine learning algorithms identified by UC Berkeley (IBM approved). Those are:

  • Decision Process: Predictions or classifications made using machine learning. The data provided will be used to make this prediction or the classification.
  • Error Function: Error function helps evaluate the model’s prediction used for the analysis. It tests the accuracy of the model as well.
  • Optimization Process: Constantly rerunning the testing dataset will reduce the discrepancy and optimize the model. The repetition of the algorithm will automatically update until the required threshold of accuracy.
Choosing Machine Learning Model

There are some steps to follow in choosing a suitable machine learning model. These are:

  • Data scientists with deep knowledge of the problem will align the data with solutions.
  • Collect the data in a structured format from data researchers and format it as desired. Labelling this data is optional.
  • Decide on an algorithm and run the data. See if the algorithm works; otherwise, calibrate it. Data scientists perform this step.
  • Keep tuning the machine learning algorithm output to get the desired level of accuracy.

Use of Machine Learning in Professions

There are machine learning examples in everyday encounters.

  • Speech Recognition: The most common machine learning uses are for speech recognition. Siri, Alexa and others use speech recognition for mobile phones.
  • Customer Service: Chatbots for online stores are using machine learning algorithms for communicating responses. They replace humans, especially during nighttime hours or answering frequently asked questions.
  • Self-driving Cars: Machine learning algorithms can identify objects on the road and warn the driver.
  • Recommendation Engines: Social media makes use of these machine learning algorithms using consumer behaviours. For example, Facebook uses machine learning to identify the age group and clicks to recommend similar ads. Similarly, Amazon provides recommended products depending on customer spending history.
Machine learning vs Predictive Modeling: Differences
  • Machine learning processes data without any set rules. In contrast, predictive modeling has rules to use historical and current data for pattern and behaviour identification.
  • Machine learning algorithms can improve and evolve their working through identifying mistakes. Predictive modeling does not have this feature available.
  • Machine learning is a new technology. Predictive modeling has existed since WW2.
Offshoring Machine Learning and Predictive Modeling

Offshoring can help overcome some challenges of machine learning and predictive modeling. Both have many features overlapping together. So, they have overlapping challenges and requirements also.

Both these artificial intelligence sub-sets require big data scientists to calibrate them. Offshore gives access to large pools of skilled big data experts at cheaper costs. The offshore vendor would be in charge of sourcing, managing and retaining these professionals.

Big data expert teams are expensive. They cost around $200,000/year in a developed market. An offshore team can work at flat rates, and hourly wages can ensure 100% utilization.

Mashkraft for AI Solutions

Mashkraft has extensive experience in servicing AI customers across the world. Our offshore team can provide solutions for both machine learning and predictive modeling. We have clients across Europe, North America, Asia and Africa.

Our tool, Scrapecraft, is a data scraper using machine learning algorithms. So, we have an expert team available to provide services for any artificial intelligence solutions.