In this Python for Data Science Tutorial, You will learn about how to do Logistic regression, a Machine learning method, using Scikit learn and Pandas scipy in python using Jupyter notebook.
Checking for Independence in Logistic regression.
Checking for Missing Values
checking for target is ordinal or binary
Deploying and Evaluating logistic model
This is the 31th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist “the sexiest job of the 21st century.” Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We’ll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
Data Science, Deep Learning, & Machine Learning with Python
Deep Learning Prerequisites: Linear Regression in Python
Deep Learning Prerequisites: Logistic Regression in Python
Deep Learning: Convolutional Neural Networks in Python