https://github.com/moindalvs/learn_simple_linear_regression

Learn about Simple Linear Regression for Data Science
https://github.com/moindalvs/learn_simple_linear_regression

box-cox-transformation correlation data-science data-transformation log-transformation model-validation ols-regression prediction-model simple-linear-regression sklearn statsmodels

Last synced: 7 days ago
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Learn about Simple Linear Regression for Data Science

• Host: GitHub
• URL: https://github.com/moindalvs/learn_simple_linear_regression
• Owner: MoinDalvs
• Created: 2022-03-27T07:31:38.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-03-27T07:38:21.000Z (about 4 years ago)
• Last Synced: 2025-07-13T08:46:28.320Z (10 months ago)
• Topics: box-cox-transformation, correlation, data-science, data-transformation, log-transformation, model-validation, ols-regression, prediction-model, simple-linear-regression, sklearn, statsmodels
• Language: Jupyter Notebook
• Homepage:
• Size: 745 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Learn_Simple_Linear_Regression

## Predicting Adipose Tissue with the help of Waist Circumference

### Import libraries

### Import data

### Correlation

### Fitting a Linear Regression Model

### Predict for new data point

### Mean Squared Error (MSE)

### Root Mean Squared Error (RMSE)

## WC-AT example

![image](https://user-images.githubusercontent.com/99672298/160271715-8dcf4f03-d115-4b10-babd-910401a267c9.png)

### Creating model with SKLearn

### Model Validation

### Model with Statsmodels

### Applying Transformations

+ y =  𝛼 *log(x) + 𝛽

+ log(y) =  𝛼 *x + 𝛽 

+ log(y) = 𝛼x + 𝛼𝑥2 + 𝛽

+ BoxCox Transformation