Logistic Regression is a type of supervised learning problem where the output values are discrete i.e. the output is a fixed number of classes.

In this post, we’ll be going through:

- The Problem Solved By Logistic Regression
- Activation Functions
- Cost Function for Logistic Regression
- Gradient Descent for Logistic Regression
- The Need for Data Pre-processing
- Techniques of Data Pre-processing
- Solving the Titanic dataset on Kaggle through Logistic Regression

Logistic Regression is a type of supervised learning problem where the output values are discrete i.e. the output is a fixed number of classes.

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PySpark in Machine Learning | Data Science | Machine Learning | Python. PySpark is the API of Python to support the framework of Apache Spark. Apache Spark is the component of Hadoop Ecosystem, which is now getting very popular with the big data frameworks.

Learning how to build a basic logistic regression model in machine learning using python . Logistic regression is a commonly used model in various industries such as banking, healthcare because when compared to other classification models, the logistic regression model is easily interpreted.

Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also

AutoML makes the power of a Machine Learning algorithm available to you even if you don't have the complete knowledge of Machine Learning.You can use AutoML

I will be proposing a contact tracing algorithm that relies on GPS data, which can be used in contact tracing with machine learning.