The good news for non-developers is that it’s possible to build a predictive model without machine learning or coding skills.
What is a predictive model?
Predictive modeling, also referred to as predictive analytics, is the process that uses a historical dataset to build a mathematical solution with the purpose to predict outcomes from new data. The new dataset has the same structure as the dataset that has been used for model generation.
Prediction, also called scoring, is the information you want to predict using machine learning algorithms. You may want to predict a problem or solution, or any other factor based on your dataset.
Examples of predictive modeling use cases
Predictive models can be used to predict anything from sports outcomes to the success of a movie. Here are a few other examples of applications of machine learning algorithms:
- Predictive analytics in healthcare: predictive modeling solution can identify individuals with elevated risks of developing chronic conditions as early as possible. This can help patients avoid long-term health problems that are costly and difficult to treat.
- Predictive maintenance: until now, maintenance personnel has performed maintenance according to a preset schedule. In most cases, an organization cannot afford the impact of failure. Predictive maintenance is a part of the industry 4.0 revolution used to prevent unplanned downtime.
- Managing supply chain: predictive tools help monitor the supply chain and make proactive data-driven decisions.
- Churn prediction: machine learning algorithms can predict which customers are most likely to stop purchasing your services and switch to competitors. An organization can undertake a preventive action to retain more clients, using predictive analytics.
Big Data or Small Data
Data is the fuel of machine learning. The better your data, the better your results. Until recently, there has been a lot of hype on Big Data and as a result, most of today’s platforms were developed to work with huge datasets. But more and more experts agree that the future is after less data. The algorithms that work well with big datasets do not perform the same way with small datasets. In contrast, algorithms that work with Small Data can perform well on big datasets too. There is no globally recognized definition of what Small Data is but, in short, it’s the size of the data that can be processed in Excel.
TADA, the automated machine learning platform, is designed to work exceptionally well with Small Data.
Simple steps to build a predictive model with TADA
One of the advantages of TADA is that you don’t need to be a data scientist to use it. Below you will see a simple step-by-step guide to build your predictive models.
The first step is to prepare your dataset. You will use historical data to train your model. The quality of the dataset indicates the quality of the model. If you use TADA, you only need a minimum preparation time for your dataset because you don’t need to handle outliers nor align value ranges. Simply upload your dataset and move to the next step.
2. The expected result
The next step is actually to decide what you want to predict. Look through your dataset and choose the goal among all variables.
TADA is a unique engine inspired by evolutionary algorithms. All you need to do is to click on one button and wait a few minutes or hours (depending on the size of your dataset) until the model is ready for your analysis and use.
Once created, the model can be run on your new dataset to get predictions. TADA generates code that can be used within scripts in your favorite language.
Building predictive models can be done without coding skills. The most critical step is to prepare a dataset and define your business objective. The rest is easy as a few clicks. Get free lifetime access or learn more about TADA packages.