https://github.com/himanshumahajan138/lgmvip-datascience-3
This repository contains code and documentation for a project that demonstrates the classification of the Iris dataset using the Decision Tree Classifier algorithm. The Decision Tree is a popular and interpretable machine learning algorithm used for both regression and classification tasks.
https://github.com/himanshumahajan138/lgmvip-datascience-3
Last synced: 7 months ago
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This repository contains code and documentation for a project that demonstrates the classification of the Iris dataset using the Decision Tree Classifier algorithm. The Decision Tree is a popular and interpretable machine learning algorithm used for both regression and classification tasks.
- Host: GitHub
- URL: https://github.com/himanshumahajan138/lgmvip-datascience-3
- Owner: himanshumahajan138
- License: mit
- Created: 2023-07-21T13:05:29.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-21T13:11:30.000Z (about 2 years ago)
- Last Synced: 2025-01-27T04:34:45.380Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 485 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LGMVIP-DataScience-3
# Classification of Iris Dataset Using Decision Tree Classifier Algorithm
This repository contains code and documentation for a project that demonstrates the classification of the Iris dataset using the Decision Tree Classifier algorithm. The Decision Tree is a popular and interpretable machine learning algorithm used for both regression and classification tasks.
## Iris Dataset
The Iris dataset is a well-known dataset in the field of machine learning and statistics. It contains samples of iris flowers, with 150 instances and four features: sepal length, sepal width, petal length, and petal width. The dataset is divided into three classes, each representing a different species of iris flowers: setosa, versicolor, and virginica.
## Decision Tree Classifier Algorithm
The Decision Tree Classifier is a supervised machine learning algorithm used for classification tasks. It works by recursively splitting the dataset into subsets based on the most significant attribute at each step. The process of building the decision tree involves selecting the best attribute to split the data and repeating this process for each branch until a stopping condition is met.
## Project Goals
The main objectives of this project are as follows:
1. Preprocess the Iris dataset and prepare it for training the Decision Tree Classifier.
2. Implement and train a Decision Tree Classifier using popular libraries such as scikit-learn.
3. Evaluate the performance of the classifier using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
4. Visualize the trained decision tree to gain insights into how the classifier makes decisions.
## Files and Directories
The project repository is organized as follows:
- `data`: Contains the Iris dataset in CSV format.
- `notebooks`: Jupyter notebooks with code for data preprocessing, model training, and visualization.
## Getting Started
To run the project on your local machine, follow these steps:
1. Clone the repository: `git clone https://github.com/himanshumahajan138/LGMVIP-DataScience-3.git`
2. Navigate to the project directory: `cd LGMVIP-DataScience-3/Himanshu_Iris_DTC`
3. Set up a virtual environment (optional but recommended): `python -m venv venv`
4. Activate the virtual environment: `source venv/bin/activate` (Linux/Mac) or `venv\Scripts\activate` (Windows)
5. Install the required dependencies: `pip install -r requirements.txt`
6. Open Jupyter notebooks and explore the code: `jupyter notebook`
## Running the Code
Open the Jupyter notebooks in the directory to follow the step-by-step implementation of the project. Execute each cell to preprocess the data, train the Decision Tree Classifier, and visualize the decision tree.
## Conclusion
This project serves as a practical example of using the Decision Tree Classifier algorithm for the classification of the Iris dataset. By visualizing the decision tree, we can better understand the decision-making process and gain insights into the data's patterns.
Feel free to explore the code, experiment with different parameters, and extend the project for further analysis or additional datasets.
**Note:** Don't forget to acknowledge the sources if you use code or information from this repository in your own projects.
Happy coding! 🌿🌺🌼