https://github.com/miozilla/statsmodols
statsmodols :boot::triangular_ruler: : Linear Regression Model # statsmodels.formula.api.ols # train # fit # pandas # matplotlib # statistics
https://github.com/miozilla/statsmodols
api azure boot formula harness learning least machine ml ordinary square statistics statsmodels
Last synced: 4 months ago
JSON representation
statsmodols :boot::triangular_ruler: : Linear Regression Model # statsmodels.formula.api.ols # train # fit # pandas # matplotlib # statistics
- Host: GitHub
- URL: https://github.com/miozilla/statsmodols
- Owner: miozilla
- Created: 2025-07-24T04:10:57.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-08-19T12:04:37.000Z (5 months ago)
- Last Synced: 2025-08-19T13:15:54.386Z (5 months ago)
- Topics: api, azure, boot, formula, harness, learning, least, machine, ml, ordinary, square, statistics, statsmodels
- Language: Jupyter Notebook
- Homepage:
- Size: 525 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# statsmodols 👢📐
statsmodols : Linear Regression Model # statsmodels.formula.api.ols # train # fit # pandas # matplotlib # statistics
## Objectives
- To create and test a Linear Regression Machine Learning Model.
- Specifying models using formula strings and DataFrames
- To estimate parameters of linear regression model by minimizing sum of squared residuals using statistical OLS method.
## OLS Linear Regression ML Model
### OLS Regression Model General Formula
Predicted Value = β₀ + β₁·x₁ + β₂·x₂ + ... + βₚ·xₚ + ϵ
Where:
- β₀ = intercept
- β₁, β₂, ..., βₚ = regression coefficients
- x₁, x₂, ..., xₚ = input features (independent variables)
- ϵ = error term (residual)
