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Linear Regression and Logistic Regression in Python

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Linear Regression and Logistic Regression in Python

Linear Regression and Logistic Regression in Python

Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners


Sure! Here’s a brief overview of Linear Regression and Logistic Regression in Python.


Linear Regression Linear regression is a statistical method that helps us understand the relationship between two continuous variables. It is used to predict values of one variable based on another variable. In Python, we can use the scikit-learn library to build a linear regression model. Here’s an example of how to build and train a linear regression model in Python 1.


Logistic Regression Logistic regression is a statistical method that helps us understand the relationship between two continuous variables. It is used to predict values of one variable based on another variable. In Python, we can use the scikit-learn library to build a logistic regression model. Here’s an example of how to build and train a logistic regression model in Python


What you'll learn

  • Learn how to solve real life problem using the Linear and Logistic Regression technique
  • Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
  • Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
  • Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
  • Basic statistics using Numpy library in Python
  • Data representation using Seaborn library in Python
  • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python
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