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https://github.com/yuvrajsaraogi/-iris-flower-classification

Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according to their species, and the task is to train a machine learning model that can learn from the measurements of the iris species and classify them.
https://github.com/yuvrajsaraogi/-iris-flower-classification

classification data data-analysis data-science data-visualization flower flower-classification iris iris-classification iris-flower iris-flower-classification knn knn-classification machine-learning machine-learning-algorithms ml natural-language-processing nlp python

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Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according to their species, and the task is to train a machine learning model that can learn from the measurements of the iris species and classify them.

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README 

        
# 🌸 Iris Flower Classification 🌿  



## 📌 Project Overview  

The **Iris Flower Classification** project is a **machine learning** application that aims to classify different species of the **Iris flower** based on their petal and sepal dimensions. This is a **supervised learning** problem where the goal is to train a model to predict the **species** of a flower given four numerical measurements:  



- **Sepal Length (cm)**  

- **Sepal Width (cm)**  

- **Petal Length (cm)**  

- **Petal Width (cm)**  



This project utilizes the **Iris dataset**, a well-known dataset in machine learning, to develop a classification model. The dataset contains **150 samples**, equally distributed among three species:  

✅ **Iris-setosa**  

✅ **Iris-versicolor**  

✅ **Iris-virginica**  



By applying **machine learning algorithms**, we can accurately classify an Iris flower based on its measurements.  



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## 🗂️ Dataset Overview  

The dataset consists of **150 records** with the following **6 columns**:  



| Column Name       | Description | Data Type |

|-------------------|------------|-----------|

| **Id**           | Unique identifier (not needed for training) | Integer |

| **SepalLengthCm** | Length of the sepal | Float |

| **SepalWidthCm**  | Width of the sepal | Float |

| **PetalLengthCm** | Length of the petal | Float |

| **PetalWidthCm**  | Width of the petal | Float |

| **Species**       | Type of Iris flower (Target Variable) | Categorical (Setosa, Versicolor, Virginica) |



✅ **No missing values**  

✅ **Well-balanced dataset** (each species has 50 samples)  



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## 🔍 Project Workflow  

### 1️⃣ Data Preprocessing  

- Load the dataset using **pandas**  

- Drop unnecessary columns (e.g., `Id`)  

- Encode the **categorical target variable** (Species) into numerical labels  

- Split the dataset into **training (80%)** and **testing (20%)** subsets  



### 2️⃣ Exploratory Data Analysis (EDA) 📊  

- Visualizing **feature distributions** using **histograms, box plots, and scatter plots**  

- Checking for **correlations** between features  

- Plotting **pairwise relationships** to understand class separability  



### 3️⃣ Model Training 🤖  

We train a **classification model** to predict the **species of a flower** based on its features. The following steps are performed:  

- Use **scikit-learn** to train a **classification model**  

- Evaluate model performance using:  

  ✅ **Accuracy score**  

  ✅ **Confusion matrix**  

  ✅ **Precision, Recall, and F1-score**  



### 4️⃣ Model Evaluation 📈  

- Test the trained model on the **test dataset**  

- Analyze **misclassifications** and accuracy metrics  

- Compare different algorithms (e.g., **Decision Trees, SVM, KNN**)  



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## 🛠️ Technologies Used  

This project leverages the following tools and libraries:  

- **Python** 🐍 - Primary programming language  

- **Pandas** & **NumPy** - Data manipulation and analysis  

- **Matplotlib** & **Seaborn** - Data visualization  

- **Scikit-learn** - Machine learning algorithms  



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## 📊 Visualization & Insights  

We use various visualization techniques to **understand the dataset**:  

- **Pair plots** to see relationships between features  

- **Heatmaps** for feature correlation  

- **Bar charts & histograms** to analyze class distribution  



These visualizations help select the **best features** and improve model performance.  



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## 🚀 Next Steps  

Here are some potential improvements for the project:  

🔹 Try **different ML algorithms** (e.g., SVM, Random Forest)  

🔹 Perform **hyperparameter tuning** to improve accuracy  

🔹 Deploy the model using **Flask or Streamlit**  

🔹 Convert the project into an interactive **web application**  



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## 🎯 Conclusion  

This project demonstrates how **machine learning** can be used to accurately classify Iris flowers. By leveraging **data preprocessing, visualization, and model evaluation**, we can gain insights into the dataset and build a reliable classification model.  



✅ **Simple but powerful classification task**  

✅ **Great introduction to ML concepts**  

✅ **Useful for learning data preprocessing & model evaluation**  



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## 🤝 Contributing  

Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to:  



1. **Fork the repository**  

2. **Create a new branch** (`feature-branch`)  

3. **Commit your changes**  

4. **Open a pull request**  



We appreciate all contributions that help improve this project! 🚀  



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## 📜 License  

This project is open-source and available under the **MIT License**.