🌸 Iris Dataset Analysis

A comprehensive data analysis and visualization project using the classic Iris dataset.

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📊 Project Overview

This repository contains an in-depth analysis of the famous Iris dataset using Python’s powerful data analysis and visualization libraries.
The assignment demonstrates skills in data loading, exploration, analysis, and visualization.

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📚 Dataset Information

• Samples: 150 iris flowers
• Species:
  • Setosa
  • Versicolor
  • Virginica
• Features (per flower):
  • Sepal length (cm)
  • Sepal width (cm)
  • Petal length (cm)
  • Petal width (cm)

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🗂️ Project Structure

iris-analysis/
├── Iris_Dataset_Analysis.ipynb   # Main Jupyter notebook
├── requirements.txt              # Python dependencies
├── iris_visualizations.png       # Generated visualizations
└── README.md                     # Project documentation

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⚙️ Requirements

• Python 3.6+
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• jupyter

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🚀 Installation

Clone the repository:

git clone <repository-url>
cd iris-analysis

Install dependencies:

pip install -r requirements.txt

Or manually:

pip install pandas numpy matplotlib seaborn scikit-learn jupyter

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▶️ How to Run the Analysis

1. Open a terminal/command prompt
2. Navigate to the project directory
3. Start Jupyter Notebook:

   jupyter notebook

4. In the web interface, open Iris_Dataset_Analysis.ipynb
5. Run the cells in order (Cell > Run All or Shift + Enter)

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📝 Assignment Tasks Completed

1️⃣ Load and Explore the Dataset

• Loaded Iris dataset with scikit-learn
• Displayed sample rows
• Explored structure & data types
• Checked for missing values (none found)

2️⃣ Basic Data Analysis

• Computed statistics for numerical columns
• Grouped by species and calculated means
• Identified patterns & differences

3️⃣ Data Visualization

• Line chart: Measurement trends across species
• Bar chart: Average petal length by species
• Histogram: Sepal length distribution
• Scatter plot: Sepal vs. petal length relationship

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📈 Key Findings

• Species Differences:
  Setosa (smallest), Virginica (largest), Versicolor (intermediate)
• Measurement Patterns:
  • Strong positive correlation between petal length & width
  • Sepal length and petal length are positively correlated
  • Setosa is easily distinguishable by petal measurements
• Data Quality:
  • No missing values
  • All measurements are numerical and correctly typed

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📦 Files Included

• Iris_Dataset_Analysis.ipynb — Main analysis notebook
• iris_visualizations.png — Combined visualizations
• requirements.txt — Python dependencies
• README.md — Project documentation

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💡 Additional Notes

• Includes try-except blocks for robust error handling
• Visualizations have clear titles, labels, and legends
• The analysis follows data exploration and visualization best practices

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Happy Analyzing!
For questions or suggestions, feel free to open an issue or submit a pull request.