https://github.com/hiteshydv001/iris_classification
https://github.com/hiteshydv001/iris_classification
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/hiteshydv001/iris_classification
- Owner: Hiteshydv001
- Created: 2023-09-24T05:54:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-27T14:31:59.000Z (over 1 year ago)
- Last Synced: 2025-02-13T03:44:22.539Z (3 months ago)
- Language: Jupyter Notebook
- Size: 197 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Iris Flower Classification with Machine Learning
The classification of iris flowers is a fundamental machine learning problem. This project showcases how to build, train, and evaluate a machine learning model for this task using Python and popular machine learning libraries. This project is an example of a machine learning classification task to identify three species of iris flowers: Setosa, Versicolor, and Virginica, based on their sepal and petal characteristics. The Iris dataset is a classic dataset often used for machine learning tutorials and demonstrations.
## Dataset Used
**Dataset from kaggle:**
We used the Iris dataset from Kaggle. This dataset contains 150 samples of iris flowers, with 50 samples for each of the three species. Each sample has four features: sepal length, sepal width, petal length, and petal width.
## Roadmap
**1. Data Collection:**
Gather data containing wine features (e.g., acidity, pH, alcohol content) and wine quality ratings.
Ensure data is clean, well-structured, and includes a target variable (wine quality).
**2. Data Preprocessing:**
Handle missing values, outliers, and data scaling.
Select relevant features and perform feature engineering.
Encode categorical variables if needed.
Split the data into training, validation, and test sets.
**3. Exploratory Data Analysis (EDA):**
Explore data distributions, correlations, and visualizations.
Gain insights for feature selection and model building.
**4. Model Selection and Training:**
Choose a suitable classification algorithm (e.g., Decision Trees, Random Forest, SVM).
Train initial models on the training data.
Optimize model hyperparameters for better performance.
**5. Model Evaluation:**
Evaluate model performance on the validation set using metrics (e.g., accuracy, precision, recall).
Fine-tune models based on validation results.
## Screenshots
## Tech Stack
**Languages:** Python
**Framework:** Jupyter Notebook || Pycharm
## Run Locally
Clone the project
```bash
git clone [https://link-to-project](https://github.com/Hiteshydv001/iris_classification.git)https://github.com/Hiteshydv001/iris_classification.git
```
## 🔗 Links
## let's connect
[](https://www.linkedin.com/in/hitesh-kumar-4b2735252/)