The world creates more than 2.5 quintillion bytes of data every day . In the last two years alone, 90% of the world's data has been generated . In this scenario, it becomes crucial for companies and organizations to adapt to this Data-Driven . For this, Machine Learning plays a vital role in the task of moving from Data to Decisions . The process can be divided into four stages: Data Understanding, Prediction, Decision Making and Causal Inference.
Step 1: Understand the Data
The first step is to understand the data, both the technical aspects and area-specific knowledge. Both are necessary to understand the data and solve problems. Descriptive Statistics , Cluster Analysis and Data Visualization are very useful for summarizing, grouping and getting some initial insights from the data. In cases where the data has many dimensions, it is possible to apply techniques such as PCA to improve the situation. This method is very useful as it is able to summarize information from high-dimensional data into few dimensions. It is essential that the analyst understands the data very well before moving on to the prediction and modeling stages. Another very important point is asking the right questions from the beginning, a factor that makes knowledge of the area decisive.
Step 2: Prediction
The next step is prediction, that is, finding out what might happen. Not all predictive problems are equal, there are Regression and Classification problems. Supervised Learning methods , however the target is numeric in Regression, whereas in Classification it is a class. There are many predictive models for each problem, such as the traditional Linear Regression and Logistic Regression . Neural Network models have flourished in recent years. These techniques are known as Deep Learning and are great for dealing with unstructured data. In general, forecasting is a very powerful tool for modeling uncertainty and providing clearer insight into the future.
Step 3: Decision Making
Once you understand the data and make predictions of what will happen, it's time to decide what to do next. This step is decision making in a data-driven approach. A key aspect of decision making is modeling uncertainty, for this purpose predictive models are essential. Another very important point is to balance risk and reward in order to make the best decisions. The objective is to carry out actions that generate immediate rewards for the business, but also enable better data and information to be obtained for future decisions. To achieve all of this, it is crucial to understand the dynamics of the specific business problem. This dynamic is built by two factors: the way in which actions impact the state of the business and the rate at which data and information can be obtained. Once the scenario is identified and all factors are taken into account, the challenge is to make the right decision.
Step 4: Causal Inference
The next step is about Causal Inference and how it can provide the tools necessary to understand and quantify the relationships between cause and effect. In the search for causality, a key aspect is the Randomized Controlled Trial . This process is a random selection of elements for two groups and collection of their data. One group is the control, where no action was applied, and the other group is the treatment, where a specific action was applied. The scientific method comes into action with Hypothesis Testing , a systematic method that allows you to accept or reject hypotheses, based on the data generated from the experiment. Causal Inference plays a vital role in Machine Learning as it determines, based on data, cause and effect relationships. This is crucial for analyzing the decision-making stage, as Causal Inference determines which actions actually have an effect and which do not.
Applications
There are countless applications of machine learning in all types of industries. Companies in retail, finance, insurance, marketing, healthcare, and countless other areas use Machine Learning to solve their business problems. Statistical methods , such as Linear Regression, Logistic Regression and Time Series Analysis Deep Learning techniques are helping companies move from data to decisions . The world is increasingly Data-Driven. This scenario makes it crucial for companies, governments and organizations to move from decisions based on instincts to decisions based on data.
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