| --- |
| license: mit |
| language: en |
| tags: |
| - tabular-classification |
| pipeline_tag: tabular-classification |
| library_name: sklearn |
| --- |
| |
| # 🌸 Iris Flower Classification using Ensemble Learning |
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| This repository focuses on building and evaluating a high-performance machine learning pipeline on the classic **Iris Dataset** using advanced **Ensemble Learning** methodologies. The goal is to optimize multi-class classification accuracy by combining multiple base estimators. |
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| ## 🛠️ Ensemble Techniques Implemented |
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| To achieve robust predictive stability, the project utilizes the following ensemble architectures: |
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| - **Max Voting / Hard & Soft Voting:** Aggregating predictions from diverse underlying algorithms (like Logistic Regression, SVM, and Decision Trees). |
| - **Bagging (Random Forest Classifier):** Training multiple decision tree estimators in parallel to reduce model variance. |
| - **Boosting (AdaBoost / Gradient Boosting):** Sequentially correcting errors from baseline estimators to reduce predictive bias. |
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| --- |
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| ## 📊 Dataset Structure |
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| The system processes the standard Iris dataset containing 150 instances tracking four core physical features: |
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| 1. Sepal Length (cm) |
| 2. Sepal Width (cm) |
| 3. Petal Length (cm) |
| 4. Petal Width (cm) |
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| --- |
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| ## 💻 Tech Stack & Dependencies |
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| - **Python 3.x** |
| - **scikit-learn** (For dataset sourcing, model pipelines, and ensemble algorithms) |
| - **pandas & numpy** (For structured matrix processing) |
| - **matplotlib & seaborn** (For confusion matrix heatmap plots and classification boundaries) |
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| --- |
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| ## 🚀 How to Run Locally |
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| Follow these quick implementation steps to clone, configure, and execute the ensemble model pipeline locally on your machine: |
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| ### 1. Clone and Enter the Repository |
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| ```bash |
| git clone [https://github.com/amirsohail100/Iris_datase_-with_Ensemble_Learning.git](https://github.com/amirsohail100/Iris_datase_-with_Ensemble_Learning.git) |
| cd Iris_datase_-with_Ensemble_Learning |
| pip install -r requirements.txt |
| ``` |
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| An optimized machine learning pipeline implementing Ensemble Learning (Voting, Bagging, Boosting) on the classic Iris Dataset to achieve high-accuracy multi-class classification. |
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