How many times have you been navigating through a website to hear that ping alerting you of the chatbot who is ready to blurt out answers to questions you don’t even have yet? Often, stakeholders decide that because their competitors have an AI-powered tool, they need to have one as well. Wrong. There is a high probability that the urge to get a chatbot, for instance, was some instinct-based decision that is not truly proven by math. To say it clearly, this is a typical depiction of a problem based on hunches and not data. For organizations with streams of data, whereas that is a software company, telecommunications, eCommerce, and so on, algorithms play a crucial role in understanding the overall business ecosystem. Whether that is understanding their operations, marketing, technology, and even HR.

Solving business problems requires a holistic approach towards a specific issue. The caveat lies on how to interpret the problem to be able to choose how to solve it. Often, companies look for problems they believe AI can solve, instead of taking into consideration real problems they’re facing and breaking them apart with the right tools.

Andrew Ng, one of the world’s most prominent leaders on AI, says the best way to implement AI into your business is to start small. In a recent talk, he gives the example of his own experience at Google and how he used Machine Learning (ML) to add value to speech recognition and Google Maps before tackling bigger business problems in the advertising department.

We won’t go on about how to solve business problems, since the internet is crammed with article after article breaking down the process. But we will point you in the right direction to choose the right algorithm to solve the business problems at hand.

**Regression Algorithms**

**Why use Regression? **

Let's say you work in operations. Performance is based on how your employees are being managed. Whether that is through incentives or processes, we want to predict performance based on incentives. Intuitively, you know that performance would be related to the number of incentives you could put in place, the better performance, the higher the revenue. Logistic Regression takes two variables, a dependent variable (revenue) and an independent variable (traffic). The independent variable drives the dependent variable because there is some correlation between the two. Then, we can use the independent variable to make predictions about the dependent variable. For example, in the chart below, you can determine how much revenue there will be with several incentives per month for your employees.

You could draw a line plotting the answer on the Y axis, but in this case, we would generate an equation, and we would call that equation a model, and then we could plug in the independent variable into the equation to find out the dependent variable output which we would call our prediction.

**So what is Regression?**

Regression is a statistical relationship between two or more variables where a change in the independent variable is associated with a change in the dependent variable.

**Types of Regression**

**Linear Regression**

When there is a linear relationship between a dependent variable (continuous) and an independent variable (continuous or discrete).

Image source: Wikipedia

**Examples of when to use Linear Regression?**

- Assess risk
- Make estimates
- Conduct forecasts
- Analyze campaign effectiveness

For example, a health insurance company might conduct a linear regression analysis of claims per customer against age and discover that older customers tend to make more health insurance claims.

**Logistic Regression**

When the Y value in the graph is categorical in nature (Yes/No) and depends on the X variable.

Image source: Wikipedia

**Examples of when to use Logistic regression?**

- Predict the likelihood of an event happening or a choice being made
- If a product will be purchased or not
- Predict customer retention
- Credit scoring

**Polynomial Regression**

When the relation between the dependent variable Y and the independent variable X is in the nth degree of X. If your data points clearly will not fit a linear regression (a straight line through all data points), it might be ideal for polynomial regression.

Polynomial regression, like linear regression, uses the relationship between the variables x and y to find the best way to draw a line through the data points.

Image source: Wikipedia

**Examples of when to use Polynomial regression?**

- It is used in many experimental procedures to produce the outcome
- To provide a defined relationship between the independent and dependent variables
- To study the rise of different diseases within any population.

- To study the generation of any synthesis.

**Decision Tree**

A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

For example, if you work in a home goods warehouse, you use decision tree models to decide how to organize the product shelves. So you would start with a bunch of products ranging from sofas to chairs and tables. A decision tree can help you organize them by consequence, such as if they have a longer length like a sofa, or they’re single seating like a chair. Then the tree can become even more organized by separating them by fabric or by color.

Image source: Wikipedia

**Examples of when to use a Decision Tree?**

- Classification
- Regression (Predicting the profits of a company)

**Clustering Algorithm**

Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space. Clustering techniques apply when there is no class to be predicted but rather when the instances are to be divided into natural groups.

Image source: GeeksforGeeks

**Types of Clustering Algorithms**

Affinity Propagation

Agglomerative Clustering

BIRCH

DBSCAN

K-Means

Mini-Batch K-Means

Mean Shift

OPTICS

Spectral Clustering

Mixture of Gaussians

**Examples of when to use a Clustering Algorithm?**

- Separating clusters based on their natural behavior to segment a market.
- Image segmentation
- Grouping web pages
- Information retrieval

**How to run Machine Learning algorithms?**

Dive right in by running Machine Learning models with little to no code using Datagran’s tool. Datagran brings the ML world with the Business world together. It lets you connect your favorite apps, run ML models and automate workflows with a friendly user interface made for almost anyone in your team to use.

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