Welcome to the Linear Regression PySpark repository! This resource is dedicated to exploring Linear Regression in the context of Apache Spark, using PySpark. The repository not only covers theoretical aspects but also includes practical implementations and a consulting project exercise.
Linear Regression is a fundamental algorithm in predictive modeling and machine learning, especially for problems involving continuous values. This repository aims to provide a comprehensive understanding of Linear Regression, its implementation in PySpark, and how to evaluate its performance.
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Theory Overview Lecture: A detailed explanation of Linear Regression and its application in data science.
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Documentation Example: A step-by-step guide through PySpark's official documentation on Linear Regression.
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Custom Code Example: An example of implementing Linear Regression in PySpark with custom code.
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Consulting Project Exercise: A real-world-inspired project to apply your Linear Regression skills.
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Evaluating Regression: Understanding how to evaluate regression models in PySpark.
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Key Evaluation Metrics for Regression
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While metrics like accuracy or recall are pivotal for classification problems, regression requires different evaluation metrics designed for continuous values. This repository covers:
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Mean Absolute Error (MAE): The average of absolute errors.
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Mean Squared Error (MSE): The average of squared errors, emphasizing larger errors.
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Root Mean Square Error (RMSE): The square root of MSE, is popular due to its units being the same as the dependent variable (y).
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R Squared Values: Indicates the proportion of variance in the dependent variable explained by the independent variables.
Prerequisites
- Apache Spark with PySpark
- Basic understanding of machine learning and regression
- Installation and Setup Clone the Repository
git clone https://github.com/uannabi/LogisticRegrassion.git
Running the Exercises Navigate through the various notebooks and python files to explore different aspects of Linear Regression with PySpark. The consulting project exercise is an excellent opportunity to apply what you've learned.
Contributions to enhance the repository, add more examples, or improve documentation are highly appreciated. Please feel free to fork the repository and submit your pull requests.