https://github.com/stella4444/linear-regression

learning about linear regression (currently a work in progress) ~ working with data
https://github.com/stella4444/linear-regression

linear-regression machine-learning numpy scikit-learn

Last synced: about 1 month ago
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learning about linear regression (currently a work in progress) ~ working with data

• Host: GitHub
• URL: https://github.com/stella4444/linear-regression
• Owner: stella4444
• Created: 2024-08-12T19:19:16.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-13T22:14:53.000Z (5 months ago)
• Last Synced: 2024-11-03T03:43:04.145Z (3 months ago)
• Topics: linear-regression, machine-learning, numpy, scikit-learn
• Language: Python
• Homepage:
• Size: 1.3 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Linear Regression:

Creating this model to teach myself linear regression since it is a fundamental concept in both data science and machine learning, and to begin using the scikit-learn and numpy python libraries. Linear regression provides a solid foundation for understanding data and making predictions. My next step is to find data train my model (This is currently a work in progress).

## Overview:

My script is simple and written for a straightforward scenario with one feature. 

+ Import Libraries: We use numpy for array manipulation, LinearRegression from scikit-learn for modeling, and matplotlib for plotting.

+ Data: X is the feature matrix, and y is the target vector.

+ Create Model: LinearRegression() initializes the linear regression model.

+ Fit Model: model.fit(X, y) trains the model using the provided data.

+ Predict: model.predict(X) generates predictions based on the fitted model.

+ Print Results: Displays the intercept and coefficient of the fitted line.

+ Plot: Shows the data points and the fitted line using matplotlib.