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

This project builds a linear regression model in Python to predict outcomes and derive insights from feature data. It covers data cleaning, feature analysis, and model evaluation, showcasing predictive modeling techniques using scikit-learn, pandas, and visualization libraries.
https://github.com/ngangawairimu/linear-regression-

data-analysis linear-regression machine-learning predictive-modeling python scikit-learn

Last synced: 8 days ago
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This project builds a linear regression model in Python to predict outcomes and derive insights from feature data. It covers data cleaning, feature analysis, and model evaluation, showcasing predictive modeling techniques using scikit-learn, pandas, and visualization libraries.

• Host: GitHub
• URL: https://github.com/ngangawairimu/linear-regression-
• Owner: ngangawairimu
• Created: 2024-10-29T06:26:04.000Z (10 days ago)
• Default Branch: main
• Last Pushed: 2024-10-29T06:28:07.000Z (10 days ago)
• Last Synced: 2024-10-29T07:23:35.477Z (10 days ago)
• Topics: data-analysis, linear-regression, machine-learning, predictive-modeling, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Project Overview

This project demonstrates the use of linear regression to predict target variables from structured data, focusing on identifying key factors that drive the predictions. The notebook provides a step-by-step approach, from data preparation to model evaluation, and emphasizes practical outcomes for data-driven decision-making.

### Key Outcomes

Predictive Insights: The linear regression model identifies and quantifies relationships between features and the target variable, enabling informed predictions and actionable insights.

Performance Metrics: Model performance is evaluated using key metrics such as:

R-squared: For goodness-of-fit, measuring variance explained by the model.

Mean Absolute Error (MAE) and Mean Squared Error (MSE): To assess prediction accuracy.

Feature Impact: Analysis of feature coefficients highlights the most influential variables, guiding focus on important predictors for further optimization or intervention

### Technologies Used

Programming Language: Python 

Libraries:

pandas and numpy: Data handling and manipulation.

matplotlib and seaborn: Visualization of data trends and feature relationships.