The above example depicts the example of how we decide by looking at different parameters in our life. The same steps are followed in the decision tree classifier.

A Decision Tree is a simple representation for classifying examples. It is **Supervised** Machine Learning algorithm where the data is continuously split according to a certain parameter.

The decision tree is the most powerful and popular tool for classification and prediction. …

Hello Guys!!!

So far, we have discussed the Regression technique. Now, let us look at the second important Supervised learning algorithm, i.e. Classification.

Can classification problems be solved using Linear Regression? Why is Logistic Regression being a classification technique, still named regression?** **These questions along with a delineate on Logistic Regression will be addressed in this blog.

Classification is a subcategory of supervised learning, where the process is of categorizing a given set of data into classes. Classes such as *Good***/***Bad*(**reviews**), *Spam***/***Not spam*(**Emails**)* or Positive***/***Negative***/***Neutral *(**tweets**). It can be performed on both structured or unstructured data. The process starts with…

A brief introduction to Stepwise Regression.

So, you saw the name, and it says Stepwise. As the name stepwise regression suggests, this procedure selects variables in a step-by-step manner. Stepwise either adds the most significant variable or removes the least significant variable. It does not consider all possible models, and it produces a single regression model when the algorithm ends.

It is highly used to meet regression models with predictive models that are carried out naturally. With every forward step, the variable gets added or subtracted from a group of descriptive variables.

**The Backward Method**: Whenever a model is fully…

What if your linear regression model cannot establish the relationship between the target variable and the predictor variable**?** In other words, what if they don’t have a linear relationship at all. After this blog, you will definitely get all your answers. This is my third blog on Regression series. This blog requires prior knowledge of Linear Regression.

Polynomial regression is a special case of linear regression where we fit a polynomial equation on the data with a **curvilinear relationship** between the target variable and the independent variables.

We already learned Linear Regression, so what’s new here? What was the need for Ridge & Lasso Regression technique? We will be answering all these questions right here in detail. First: let’s look at possible categories of results that can be obtained from training a model with linear regression.

Whenever you come across linear regression, the first thing that should come to your mind is a scatter plot image somewhat like this.

To set up the possible **relationship **among different variables, various modes of statistical approaches are implemented, known as regression analysis. To understand how the *variation in an independent variable can impact the dependent variable*, regression analysis is specially molded out.

Basically, regression analysis sets up an equation to explain the significant relationship between one or more predictors and response variables and also to estimate current observations. …

Hello everyone, as the title suggests, in this blog, we will be going through different types and processes involved in Machine Learning. But, let's first understand how machine learning works.

- The Machine Learning algorithm is trained using a training dataset and a model is created.
- Then the trained model works on a test dataset and generates predictions.
- The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning model is deployed. If the accuracy is not acceptable, then the Machine Learning model is trained with other techniques.
- The type of technique (or algorithm) of ML used…

Python Pandas is an open-source library that provides high-performance data manipulation in Python. This tutorial is designed for both beginners and professionals.

- It’s fast and efficient DataFrame object indexing easy.
- Used for reshaping and pivoting of the data sets.
- Group by data for aggregations and transformations.
- It is used for data alignment and integration of the missing data.
- Provide the functionality of Time Series.
- Process a variety of data sets in different formats like matrix data, tabular heterogeneous, time series.
- Handle multiple operations of the data sets such as subsetting, slicing, filtering, groupBy, re-ordering, and re-shaping.
- It integrates with the…

In this blog, we will look at Python modules(NumPY & File Handling) necessary for Machine Learning. Please try to run the code by yourself for a better understanding.

Prerequisite; Basic knowledge of Python is necessary to understand this blog.

**NumPy**: NumPY is a Python library used for working with arrays. It also has functions for working in domain of **linear algebra**, **Fourier transform** (Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components), and **matrices**.

The study of history is important because it allows one to make more sense of the current world. It let’s you know the efforts done by our scientists for creating AI. Its always interesting to start a topic to learn from its history.