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https://github.com/chandkund/iris-classification
Iris Classification project using machine learning to classify iris flowers into Setosa, Versicolour, and Virginica species. Includes data exploration, model training with SVM, Logistic Regression, and Decision Tree, and performance evaluation. Contributions welcome! Python, Pandas, Scikit-learn, Seaborn, Matplotlib.
https://github.com/chandkund/iris-classification
machine-learning matplotlib-pyplot numpy pandas python sklearn
Last synced: 15 days ago
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Iris Classification project using machine learning to classify iris flowers into Setosa, Versicolour, and Virginica species. Includes data exploration, model training with SVM, Logistic Regression, and Decision Tree, and performance evaluation. Contributions welcome! Python, Pandas, Scikit-learn, Seaborn, Matplotlib.
- Host: GitHub
- URL: https://github.com/chandkund/iris-classification
- Owner: chandkund
- Created: 2024-08-13T05:50:11.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-16T19:38:19.000Z (3 months ago)
- Last Synced: 2024-11-03T04:02:50.761Z (15 days ago)
- Topics: machine-learning, matplotlib-pyplot, numpy, pandas, python, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 445 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Iris-Classification
Iris Classification project using machine learning to classify iris flowers into Setosa, Versicolour, and Virginica species. Includes data exploration, model training with SVM, Logistic Regression, and Decision Tree, and performance evaluation. Contributions welcome! Python, Pandas, Scikit-learn, Seaborn, Matplotlib.
# Iris Classification Project
This project focuses on the classification of the Iris dataset using machine learning algorithms. The goal is to predict the species of iris flowers based on their features.
## Table of Contents
- [Introduction](#introduction)
- [Installation](#installation)
- [Usage](#usage)
- [Models and Evaluation](#models-and-evaluation)
- [Results](#results)
- [Contact](#contact)
## Introduction
The Iris dataset is a classic dataset in the field of machine learning. This project uses Support Vector Machine, Logistic Regression, and Decision Tree algorithms to classify iris species.
## Installation
Clone the repository and install the required libraries:
```bash
git clone https://github.com/chandkund/Iris-Classification.git
cd Iris-Classification
```
```python
pip install pandas numpy matplotlib seaborn scikit-learn
```
## Usage
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
```
- **Load Data**:
```python
raw_data = pd.read_csv("Iris.csv")
df= raw_data.copy()
```
- **Feature Selection**:
```python
inputs = df.iloc[:, 0:4].values
targets = df.iloc[:, 4].values
```
- **Split train and test data**:
```python
x_train, x_test, y_train, y_test = train_test_split(inputs, targets, test_size=0.2, random_state=11)
```
- **Model 1**:
```python
model_svc = SVC().fit(x_train, y_train)
```
- **Model_1 Evaluation**:
```python
print("SVM Accuracy:", accuracy_score(y_test, model_svc.predict(x_test)) * 100)
```
- **Model 2**:
```python
model_logistic = LogisticRegression().fit(x_train, y_train)
```
- **Model_2 Evaluation**:
```python
print("Logistic Regression Accuracy:", accuracy_score(y_test, model_logistic.predict(x_test)) * 100)
```
- **Model 3**
```python
model_tree = DecisionTreeClassifier().fit(x_train, y_train)
```
- **Model_3 Evaluation**:
```python
print("Decision Tree Accuracy:", accuracy_score(y_test, model_tree.predict(x_test)) * 100)
```
## Models and Evaluation
The project uses three models: SVM, Logistic Regression, and Decision Tree. Each model's accuracy is evaluated using the accuracy_score metric.
- Support Vector Machine (SVM): A supervised machine learning algorithm which can be used for both
classification or regression challenges.
- Logistic Regression: A statistical method for analyzing a dataset in which there are one or more
independent variables that determine an outcome.
- Decision Tree: A decision support tool that uses a tree-like model of decisions and their possible
consequences.
## Results
SVM Accuracy: 96.66%
Logistic Regression Accuracy: 100%
Decision Tree Accuracy: 100%
## Contact
For any inquiries, please contact your email.
https://github.com/Chandkund/Iris-Classification.git