{"id":19894607,"url":"https://github.com/amirjahantab/iris_classification","last_synced_at":"2026-05-16T05:03:08.923Z","repository":{"id":247387935,"uuid":"825611538","full_name":"amirjahantab/Iris_Classification","owner":"amirjahantab","description":"This project analyzes the famous Iris dataset using various machine learning techniques. The goal is to classify the iris flowers into three species: Setosa, Versicolor, and Virginica based on the features provided in the dataset.","archived":false,"fork":false,"pushed_at":"2024-07-08T11:06:57.000Z","size":59,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-11T20:15:23.992Z","etag":null,"topics":["classification","data-science","machine-learning","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amirjahantab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-08T07:17:45.000Z","updated_at":"2024-07-21T10:16:34.000Z","dependencies_parsed_at":"2024-07-08T14:10:44.125Z","dependency_job_id":null,"html_url":"https://github.com/amirjahantab/Iris_Classification","commit_stats":null,"previous_names":["amirjahantab/iris_classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirjahantab%2FIris_Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirjahantab%2FIris_Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirjahantab%2FIris_Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amirjahantab%2FIris_Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amirjahantab","download_url":"https://codeload.github.com/amirjahantab/Iris_Classification/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241322533,"owners_count":19944073,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","data-science","machine-learning","scikit-learn"],"created_at":"2024-11-12T18:33:59.932Z","updated_at":"2026-05-16T05:03:03.881Z","avatar_url":"https://github.com/amirjahantab.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Iris Classification Notebook\n\nThis Jupyter notebook demonstrates the process of classifying the Iris dataset using various machine learning techniques. The Iris dataset is a classic dataset in the field of machine learning and statistics, often used for testing algorithms.\n\n\n## Introduction\n\nThe Iris dataset contains 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The samples belong to one of three species: Iris-setosa, Iris-versicolor, and Iris-virginica. This notebook demonstrates how to load the dataset, preprocess it, train different classifiers, evaluate their performance, and visualize the results.\n\n## Requirements\n\nTo run the notebook, you need the following Python libraries:\n\n- `numpy`\n- `matplotlib`\n- `scikit-learn`\n\nYou can install these dependencies using `pip`:\n\n```sh\npip install numpy matplotlib scikit-learn jupyter\n```\n\n## Usage\n\n1. Clone the repository or download the notebook file.\n2. Ensure you have all the required libraries installed.\n3. Open the notebook using Jupyter:\n\n    ```sh\n    jupyter notebook iris.ipynb\n    ```\n\n4. Run the cells in the notebook to see the data loading, model training, evaluation, and visualization steps.\n\n## Notebook Structure\n\n### Data Loading and Exploration\n\n- **Loading the Iris dataset**: The dataset is loaded using `scikit-learn`'s built-in function.\n- **Basic data exploration and visualization**: Initial exploration of the dataset, including summary statistics and pair plots to visualize the relationships between features.\n\n### Data Preprocessing\n\n- **Splitting the dataset**: The dataset is split into training and testing sets to evaluate the performance of the models.\n\n### Model Training\n\n- **K-Nearest Neighbors (KNN) Classifier**: Classifier implementing the k-nearest neighbors vote.\n- **Multi-layer Perceptron (MLP) classifier**:This model optimizes the log-loss function using LBFGS or stochastic\ngradient descent.\n### Model Evaluation\n\n- **Evaluating the models**: The models are evaluated using accuracy score on the test set.\n- **Identifying incorrect predictions**: Incorrect predictions are identified and analyzed.\n\n### Visualization\n\n- **Plotting the classification results**: A scatter plot of the classification results is generated, highlighting the incorrectly classified samples.\n\n### Accuracy Scores\n\nThe accuracy scores of the different classifiers on the test set are as follows:\n\n- **K-Nearest Neighbors (KNN)**: $94.66$ %\n- **Multi-layer Perceptron classifier (MLP)**: $96.00%$ %\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirjahantab%2Firis_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famirjahantab%2Firis_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirjahantab%2Firis_classification/lists"}