model_evaluation_metrics

📊 Model Evaluation Metrics

📌 Overview

This repository demonstrates the use of various metrics and scoring techniques to evaluate the performance of predictive models. It covers both Regression and Classification model evaluations using Python libraries like Scikit-learn, Matplotlib, and Seaborn.

🔍 Key Features

Regression Analysis: Computes MAE, MSE, RMSE, and R² Score.

Binary Classification Metrics: Evaluates models using Accuracy, Precision, Recall, F1-score, and Confusion Matrix.

Graph Plotting: Visualizes model performance using line plots, scatter plots, and confusion matrices.

🚀 Getting Started

Clone the repository:

git clone https://github.com/vaishnavibhutada/model_evaluation_metrics.git cd model_evaluation_metrics

Install dependencies:

pip install -r requirements.txt

Run the notebook or scripts:

jupyter notebook model_evaluation_metrics.ipynb

📊 Visualization Examples

Regression Error Plots

Confusion Matrix for Classification

🛠 Technologies Used

Python 🐍

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn