## How Random Forest works? – Why we need Random Forest?

In this article, we are going to learn how we get Random Forest from Decision Trees. How Random Forest works! How Ensemble learning helps to…

## Decision Tree

In this article we will learn about what is Decision Tree and How it works. How it gets Overfitted and how can we resolve Overfitting.…

## Logistic Regression — Part III — Titanic Disaster Survival Prediction

In this article we will be researching on the Titanic Dataset with Logistic Regression and Classification Metrics. Lets see how to do logistic regression with…

## Logistic Regression Part II— Cost Function & Error Metrics

In this post we will explore Cost function and Error Metrics of Logistic Regression. Logistic regression is a Classification Algorithm used to predict discrete values.…

## Logistic Regression Part I — Transformation of Linear to Logistic

In this article we will explore why we need Logistic, how we derived Logistic from Linear and a few more important facts in mathematics. Let’s…

## Underfitted— Generalized — Overfitted

A brief note on how bias and variance makes a model as Underfitted or Generalized or Overfitted! In this post, instead of writing so many…

## Overfitting — Bias — Variance — Regularization

When a Linear Regression model works well with training data but not with test data or unknown any new data, then it means the model…

## Linear Regression — Part IV — Chance of Admission Prediction

Ever thought about doing magic or predict future? Here is the guide! lol! In this article lets go programming with sklearn package to explore Linear…

## Linear Regression — Part III — R Squared

R Squared is one of the metrics by which we can find the accuracy of a model that we create. R squared metrics works only…

## Linear Regression — Part II — Gradient Descent

Gradient descent is an optimization algorithm used to minimize a cost function (i.e. Error) parameterized by a model. We know that Gradient means the slope of a surface…