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…