https://github.com/selcia25/iris-dataset-classification
☘This repository contains a Python script for classifying the Iris dataset using the Random Forest algorithm.
https://github.com/selcia25/iris-dataset-classification
data-processing iris-classification pandas random-forest-classifier scikit-learn
Last synced: 8 months ago
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☘This repository contains a Python script for classifying the Iris dataset using the Random Forest algorithm.
- Host: GitHub
- URL: https://github.com/selcia25/iris-dataset-classification
- Owner: selcia25
- License: mit
- Created: 2024-02-05T13:10:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-05T13:14:59.000Z (over 1 year ago)
- Last Synced: 2025-01-02T08:14:33.438Z (9 months ago)
- Topics: data-processing, iris-classification, pandas, random-forest-classifier, scikit-learn
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Iris Dataset Classification using Random Forest
This repository contains a Python script for classifying the Iris dataset using the Random Forest algorithm. The script covers data loading, exploration, preprocessing, model training, evaluation, and making predictions for new data points.
## Usage
1. **Clone the repository:**
```bash
git clone https://github.com/selcia25/iris-dataset-classification.git
cd iris-dataset-classification
```
2. **Install Dependencies:**
```bash
pip install scikit-learn pandas
```
3. **Download Dataset:**
- Download the Iris dataset (Iris.csv) or replace it with your dataset.
- Update the file name in the script accordingly.
4. **Run the Script:**
```bash
python object_recognition.py
```
## Dependencies
- scikit-learn
- pandas
## Script Overview
1. **Load and Explore Dataset:**
- Load the Iris dataset using pandas.
2. **Explore Data:**
- Display first few rows, information, and summary statistics of the dataset.
3. **Data Preprocessing:**
- Split features and target variables.
- Encode target variables to numerical values.
- Split the data into training and testing sets.
4. **Choose Classification Algorithm and Train Model:**
- Use the Random Forest classifier with 100 estimators.
5. **Evaluate Model's Performance:**
- Display accuracy score, classification report, and confusion matrix.
6. **Make Predictions for New Data Points:**
- Provide sample data points and display predicted classes.
This script serves as a basic template for classification tasks on the Iris dataset and can be extended for other datasets or machine learning tasks.