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This repository is dedicated to demystifying the fascinating world of regression in machine learning

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linear-regression-12

Regression

In machine learning, regression is a type of predictive modeling technique that analyzes the relationship between a dependent variable and one or more independent variables. The goal is to understand and predict the value of the dependent variable based on the input features.

Real-World Explanation

Imagine predicting the price of a house based on various factors such as the number of bedrooms, square footage, and neighborhood. Regression models can be applied to analyze historical data and make predictions about future house prices.

Use Case

Predicting Stock Prices: Utilize regression to forecast stock prices based on historical market data, helping investors make informed decisions.

Topics Covered

  • Linear Regression: Understand the fundamental linear relationship between variables.
  • Polynomial Regression: Explore non-linear relationships using polynomial functions.
  • Regularized Regression (Ridge, Lasso): Learn techniques to prevent overfitting and enhance model generalization.
  • Multivariate Regression: Handle scenarios with multiple independent variables.
  • Time Series Regression: Apply regression to time-dependent data for forecasting.

Modules to Use

  • Scikit-Learn: A powerful machine learning library for Python, providing easy-to-use regression algorithms.
  • NumPy and Pandas: Essential libraries for data manipulation and analysis.
  • Matplotlib and Seaborn: Create informative visualizations to understand model performance.
  • Jupyter Notebooks: Interactive notebooks for experimenting and visualizing regression workflows.

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This repository is dedicated to demystifying the fascinating world of regression in machine learning

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