## 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 Python — LogisticRegression() from sklearn. I have taken the Titanic data set from Kaggle.  Here…

## Numpy Array -Stack

In this article, we will see how to join 2 Numpy arrays using built-in funcitons. Numpy – Joining two numpy arrays stack — Joins arrays with given axis element by element hstack — Extends horizontally vstack — Extends vertically Stack…

## 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 Regression and learn how to do prediction with Linear Regression. We have seen enough theories…

## 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 if the regression model is linear. SSE — Sum of Squares of Residuals (Errors) SSR is the sum…

## 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 or a line. This algorithm involves calculations with slope. To understand about Gradient Descent, we…

## Linear Regression — Part I

Linear Regression is a linear approach to model the relationship between a two or more variables by fitting a straight line i.e. linear, to predict the output for the given input data. To perform Linear Regression, Data should accomplish the below…