ASSIGNMENT--6--Decision_tree

Decision Tree Classifier

This repository contains a Jupyter Notebook for building and analyzing a Decision Tree Classifier. The notebook demonstrates the step-by-step process of importing data, preprocessing, model training, and evaluation.

Contents

• Introduction: Overview of the decision tree algorithm.
• Dataset: Details about the dataset used (e.g., source, structure, and features).
• Preprocessing: Handling missing data, encoding categorical variables, and feature scaling.
• Model Building: Training a Decision Tree model and tuning its parameters.
• Evaluation: Performance metrics such as accuracy, confusion matrix, and visualization.

Prerequisites

To run the notebook, you need the following:

• Python 3.x
• Jupyter Notebook or JupyterLab
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/your_username/decision-tree-classifier.git
   
   

Visualization

The notebook includes visualizations such as:

Feature importance Decision tree structure Evaluation metrics Contributing Feel free to open issues or submit pull requests to enhance the notebook or include additional features.