{"id":15158010,"url":"https://github.com/chandkund/iris-classification","last_synced_at":"2026-01-21T15:32:25.958Z","repository":{"id":252897923,"uuid":"841825014","full_name":"chandkund/Iris-Classification","owner":"chandkund","description":"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! 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Includes data exploration, model training with SVM, Logistic Regression, and Decision Tree, and performance evaluation. Contributions welcome! Python, Pandas, Scikit-learn, Seaborn, Matplotlib.\n# Iris Classification Project\n\nThis 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.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Models and Evaluation](#models-and-evaluation)\n- [Results](#results)\n- [Contact](#contact)\n\n## Introduction\n\nThe 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.\n\n## Installation\nClone the repository and install the required libraries:\n```bash\ngit clone https://github.com/chandkund/Iris-Classification.git\ncd Iris-Classification\n```\n```python\npip install pandas numpy matplotlib seaborn scikit-learn\n```\n\n\n##  Usage\n```python\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import accuracy_score\n```\n- **Load Data**:\n```python\nraw_data = pd.read_csv(\"Iris.csv\")\ndf= raw_data.copy()\n```\n- **Feature Selection**:\n```python\ninputs = df.iloc[:, 0:4].values\ntargets = df.iloc[:, 4].values\n```\n- **Split  train and test data**:\n```python\nx_train, x_test, y_train, y_test = train_test_split(inputs, targets, test_size=0.2, random_state=11)\n```\n- **Model 1**:\n```python\nmodel_svc = SVC().fit(x_train, y_train)\n```\n- **Model_1 Evaluation**:\n```python \nprint(\"SVM Accuracy:\", accuracy_score(y_test, model_svc.predict(x_test)) * 100)\n```\n- **Model 2**:\n```python \nmodel_logistic = LogisticRegression().fit(x_train, y_train)\n```\n- **Model_2 Evaluation**:\n```python \nprint(\"Logistic Regression Accuracy:\", accuracy_score(y_test, model_logistic.predict(x_test)) * 100)\n```\n- **Model 3**\n```python \nmodel_tree = DecisionTreeClassifier().fit(x_train, y_train)\n```\n- **Model_3 Evaluation**:\n```python\nprint(\"Decision Tree Accuracy:\", accuracy_score(y_test, model_tree.predict(x_test)) * 100)  \n```\n\n## Models and Evaluation\n\nThe project uses three models: SVM, Logistic Regression, and Decision Tree. Each model's accuracy is evaluated using the accuracy_score metric.\n- Support Vector Machine (SVM): A supervised machine learning algorithm which can be used for both \n  classification or regression challenges.\n- Logistic Regression: A statistical method for analyzing a dataset in which there are one or more \n  independent variables that determine an outcome.\n- Decision Tree: A decision support tool that uses a tree-like model of decisions and their possible \n  consequences.\n  \n## Results\n\nSVM Accuracy: 96.66%\nLogistic Regression Accuracy: 100%\n\nDecision Tree Accuracy: 100%\n\n## Contact\nFor any inquiries, please contact your email.\nhttps://github.com/Chandkund/Iris-Classification.git\n\nwebsite3login@gmail.com\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandkund%2Firis-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchandkund%2Firis-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandkund%2Firis-classification/lists"}