https://github.com/himanshumahajan138/lgmvip-datascience-1
This project aims to showcase the implementation of the logistic regression algorithm for the classification of the Iris dataset. It consists of 150 samples of iris flowers, with 50 samples for each of three different species: setosa, versicolor, and virginica. Each sample contains four features: sepal length, sepal width, petal length, and width.
https://github.com/himanshumahajan138/lgmvip-datascience-1
Last synced: 7 months ago
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This project aims to showcase the implementation of the logistic regression algorithm for the classification of the Iris dataset. It consists of 150 samples of iris flowers, with 50 samples for each of three different species: setosa, versicolor, and virginica. Each sample contains four features: sepal length, sepal width, petal length, and width.
- Host: GitHub
- URL: https://github.com/himanshumahajan138/lgmvip-datascience-1
- Owner: himanshumahajan138
- License: mit
- Created: 2023-07-17T11:27:40.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-20T08:08:04.000Z (about 2 years ago)
- Last Synced: 2025-01-27T04:34:46.183Z (8 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 356 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LGMVIP-DataScience-1
# Classification of Iris Dataset Using Logistic Regression Algorithm
## Introduction
This project aims to showcase the implementation of the logistic regression algorithm for the classification of the famous Iris dataset. The Iris dataset is a multivariate dataset introduced by the British biologist and statistician Ronald A. Fisher in 1936. It consists of 150 samples of iris flowers, with 50 samples for each of three different species: setosa, versicolor, and virginica. Each sample contains four features: sepal length, sepal width, petal length, and petal width.
The main objective of this project is to demonstrate how logistic regression can be used for multi-class classification problems and how to evaluate the model's performance on the Iris dataset.
## Requirements
To run this project, you will need the following:
- Python (3.x or later)
- Jupyter Notebook (optional but recommended)
## Installation
1. Clone this repository to your local machine:
```bash
git clone https://github.com/himanshumahajan138/LGMVIP-DataScience-1.git
```
2. Change your directory to the project folder:
```bash
cd LGMVIP-DataScience-1/Himanshu_iris
```
3. (Optional) Create a virtual environment and activate it (recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows, use: venv\Scripts\activate
```
4. Install the required Python packages:
```bash
pip install -r requirements.txt
```
## Usage
1. Open the Jupyter Notebook:
```bash
jupyter notebook
```
2. Navigate to the `Himanshu_iris.ipynb` file and open it.
3. Run the cells in the notebook to see the step-by-step implementation of the logistic regression algorithm on the Iris dataset.
## Files
The project repository includes the following files:
- `Himanshu_iris.ipynb`: Jupyter Notebook containing the implementation of the logistic regression algorithm.
- `iris.data`: The dataset file in data format.
- `requirements.txt`: List of Python packages required to run the project.
## Methodology
1. Data Loading: Loading the Iris dataset from the `iris.Data` file and performing initial data exploration.
2. Data Preprocessing: Handling missing values (if any), encoding categorical variables (if any), and splitting the dataset into training and testing sets.
3. Model Implementation: Building and training the logistic regression model using the training data.
4. Model Evaluation: Evaluating the model's performance on the test dataset using accuracy and confusion matrix.
## Conclusion
This project demonstrates the implementation of the logistic regression algorithm for the classification of the Iris dataset. By following the steps in the Jupyter Notebook, you will gain a better understanding of logistic regression and how it can be applied to multi-class classification problems like the Iris dataset.
Feel free to experiment with different machine learning algorithms and techniques to further enhance the classification accuracy and explore more insights from the dataset.
For any questions or suggestions, please feel free to reach out to the project's contributors. Happy coding!