Neural Networks are universal approximators that maps data to information. What does this mean? Can Neural Networks solve any problem? Neural Networks are a proven solution for scene-by-scene/frame-by-frame analysis, stock price prediction, in retail, and for many other purposes. Many of us are using it at the enterprise level, but how many of us truly understand it?

To answer the question,* ‘Can Neural Networks solve any problem?’*, let’s take it from the basics. A NeuralNet is made up of vertically stacked components called layers: *input, hidden, *and *output. *Each layer consist of a certain number of neurons. The input layer…

As the *variables* in the dataset increases, its dimension increases which can have the following challenges:

- With more variables, data visualization becomes difficult.
- All the variables might not be important for a particular business problem.
- More complex models as the model tries to learn from all of the variables, with more computation time.
- Exploratory Data Analysis becomes difficult.

Hence, ** Dimensionality Reduction** is the process of reducing the dimensions of the data to ensure it conveys maximum information. There are

A Telephonic survey of 1025 random Americans was conducted and they were asked their **perception** of the job market. The study was trying to find, *was* *it a good time to find a job¹?*

In the **second **part of the article, *Evaluation Metrics*, we will discuss different metrics to evaluate ** regression** algorithms. (First part can be found

In regression, we calculate **error** by comparing *predicted* values with *actual* values. Error determines how **far** predicted values are from the actual value. The sign (**+** or **-**) of the error lets us know the direction in which error varies from the *best-fit* regression line.

` +--------------+-----------------+----------------------+`

| Actual Value | Predicted Value | Error…

This is part 1 of the 2 article series where we discuss different evaluation metrics for Machine Learning (ML) problems. Evaluating an algorithm’s output is as important as modeling the algorithm itself. Evaluating a program helps in determining how *impactful* is the program and how it could be *improved*. In this article, we will be reviewing evaluation metrics for ** classification**. So, let’s begin.

** Confusion Matrix** is an

Continuous improvement is better than delayed perfection. -MT