https://github.com/bhimrazy/iris-species-prediction-using-decision-tree-algorithm-grip

Iris Species Intelligence: Classifying Iris Species with Confidence using Decision Trees | The Sparks Foundation: GRIP
https://github.com/bhimrazy/iris-species-prediction-using-decision-tree-algorithm-grip

decision-tree-classifier fastapi gripjan23 machine-learning python scikit-learn sparkfoundation

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Iris Species Intelligence: Classifying Iris Species with Confidence using Decision Trees | The Sparks Foundation: GRIP

• Host: GitHub
• URL: https://github.com/bhimrazy/iris-species-prediction-using-decision-tree-algorithm-grip
• Owner: bhimrazy
• License: mit
• Created: 2023-01-08T15:01:59.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-13T11:30:42.000Z (over 3 years ago)
• Last Synced: 2025-01-17T11:13:37.855Z (over 1 year ago)
• Topics: decision-tree-classifier, fastapi, gripjan23, machine-learning, python, scikit-learn, sparkfoundation
• Language: Jupyter Notebook
• Homepage: https://iris-classifier-production.up.railway.app
• Size: 3.68 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          













 



Iris Species Prediction using Decision Trees | The Sparks Foundation: GRIP


---

## 📝 Table of Contents

- [Problem Statement](#problem_statement)

- [Idea / Solution](#idea)

- [Usage](#usage)

- [Technology Stack](#tech_stack)

- [Deployment](#deployment)

- [Author](#author)

## 🧐 Problem Statement 

- Create the Decision Tree classifier and visualize it graphically.

  The purpose is if we feed any new data to this classifier, it would be able to

  predict the right class accordingly.

  

  Dataset : https://bit.ly/3kXTdox

  Sample Solution : https://bit.ly/2G6sYx9

  

## 💡 Idea / Solution 

 Iris Species Prediction using Decision Tree Algorithm is a machine learning task of classifying the species of Iris flowers based on their physical characteristics. This task involves training a Decision Tree model on the Iris dataset, which includes measurements of sepal length, sepal width, petal length, and petal width for 150 Iris flowers of three different species: Iris setosa, Iris virginica, and Iris versicolor. The trained model is then used to predict the species of new, unseen Iris flowers based on their measurements.

    
 



To create a model to classify the Iris dataset, the following tasks were performed:

For more detail, please chekout the 


[Notebook](/Prediction%20using%20Decision%20Tree%20Algorithm.ipynb)

### Data Preparation

![image](https://user-images.githubusercontent.com/46085301/212305130-abc63824-7726-4f2b-9f27-62a178114157.png)

### Data Visualization

![image](https://user-images.githubusercontent.com/46085301/212305229-0aadbaff-c901-4a34-b8e3-99cbd3733e43.png)

![image](https://user-images.githubusercontent.com/46085301/212305272-5fdfd34b-e035-4152-9022-ffbd3ed17efb.png)

![image](https://user-images.githubusercontent.com/46085301/212305186-5d1d4511-ccd8-4150-a81e-10561d9a8623.png)

### Model Training

![image](https://user-images.githubusercontent.com/46085301/212306136-45ce3a19-7c13-4fa9-a112-24379a4cdc43.png)

### Evaluating Model

![image](https://user-images.githubusercontent.com/46085301/212305512-2ab831db-308b-4d80-b092-df5aeb8fa674.png)

![image](https://user-images.githubusercontent.com/46085301/212305540-1a0c09f1-0609-4b54-aed2-1c3be1ef754f.png)

![image](https://user-images.githubusercontent.com/46085301/212305625-97c88c38-bc9a-4bbf-92d7-08052be63903.png)

### Visualizing and Saving the model

![image](https://user-images.githubusercontent.com/46085301/212305418-2ab6065a-5d16-446a-847d-afa8a995e7ea.png)

![image](https://user-images.githubusercontent.com/46085301/212305826-16ce2b59-3c45-4930-9d13-e1867b2974a5.png)

## 🎈 Usage 

Run my Project

```bash

  git clone https://github.com/bhimrazy/Iris-Species-Prediction-using-Decision-Tree-Algorithm-GRIP.git

  cd Iris-Species-Prediction-using-Decision-Tree-Algorithm-GRIP/app

  python -m venv venv

  source venv/bin/activate

  pip install -r requirements.txt

  uvicorn main:app 

```

## ⛏️ Built With 

- [scikit-learn](https://scikit-learn.org/) - Machine Learning Library

- [Pandas](https://pandas.pydata.org/) - Python Data Analysis Library

- [Matplotlib](https://matplotlib.org/) - Visualization with Python

- [Seaborn](https://seaborn.pydata.org/) - Statistical Data Visualization

- [FastAPI](https://fastapi.tiangolo.com/)- Python Web Framework for building APIs

## 🎉 Deployment 

[![Deployment of Iris Classifier](https://user-images.githubusercontent.com/46085301/212294874-329be9b4-80b6-4364-8ebe-58c972fc0cee.png)](https://iris-classifier-production.up.railway.app)

## ✍️ Author 

- [@bhimrazy](https://github.com/bhimrazy)