amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning

🌸 Iris Flower Classification using Ensemble Learning

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

To achieve robust predictive stability, the project utilizes the following ensemble architectures:

• 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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📊 Dataset Structure

The system processes the standard Iris dataset containing 150 instances tracking four core physical features:

1. Sepal Length (cm)
2. Sepal Width (cm)
3. Petal Length (cm)
4. Petal Width (cm)

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💻 Tech Stack & Dependencies

• 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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🚀 How to Run Locally

Follow these quick implementation steps to clone, configure, and execute the ensemble model pipeline locally on your machine:

1. Clone and Enter the Repository

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

An optimized machine learning pipeline implementing Ensemble Learning (Voting, Bagging, Boosting) on the classic Iris Dataset to achieve high-accuracy multi-class classification.