https://github.com/ferdos-coder/linear_model_grid_search
https://github.com/ferdos-coder/linear_model_grid_search
gridsearchcv linear-models linear-regression python
Last synced: 10 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/ferdos-coder/linear_model_grid_search
- Owner: ferdos-coder
- Created: 2025-04-22T19:30:36.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2025-04-22T22:15:32.000Z (10 months ago)
- Last Synced: 2025-04-23T15:16:13.604Z (10 months ago)
- Topics: gridsearchcv, linear-models, linear-regression, python
- Language: Jupyter Notebook
- Homepage:
- Size: 195 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Linear Model Grid Search
**linear_model_grid_search** is a Python project that demonstrates how to perform hyperparameter tuning for linear regression models using GridSearchCV. This repository contains a Jupyter Notebook that walks through building linear models, tuning their parameters, and evaluating their performance step-by-step.
---
## Table of Contents
- [Project Overview](#project-overview)
- [Dataset](#dataset)
- [Features](#features)
---
## Project Overview
The goal of this project is to teach how to use GridSearchCV to optimize linear regression models. Using Python’s scikit-learn library, the project covers:
- Loading and preprocessing data
- Defining linear regression models
- Performing grid search for hyperparameter tuning
- Evaluating model performance with appropriate metrics
- Visualizing results for better understanding
This project provides a practical example of improving linear model performance through systematic hyperparameter tuning.
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## Dataset
The dataset used in this project is provided in the file `AMES_Final_DF.csv`. It is a cleaned version of the Ames Housing dataset, commonly used for house price prediction tasks.
- Contains various features describing houses (e.g., size, location, quality)
- The target variable is typically the house sale price
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## Features
- Jupyter Notebook (`linear_model.ipynb`) includes:
- Data loading and exploration
- Feature selection and engineering
- Defining linear regression models (Linear Regression, Ridge, Lasso, etc.)
- Running Grid Search for hyperparameter tuning
- Model evaluation and visualization
- Dataset file (`AMES_Final_DF.csv`) for training and testing models