## 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.…

## Want to Save and Reuse a model later?

In machine learning, training a model and testing it is definitely not an end. Should we run this source code of training, tuning everything again…

## 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…

## Numpy — Stacking Arrays

Joining two numpy arrays stack — Joins arrays with given axis element by element hstack — Extends horizontally vstack — Extends vertically Stack — Joins…

## 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…