https://github.com/sayande01/iris_eda_classification

This project applies a Random Forest classifier to the Iris dataset to predict flower species based on four features: Sepal Length, Sepal Width, Petal Length, and Petal Width. It includes data exploration, visualization, model building, evaluation, and hyperparameter tuning for optimal performance.
https://github.com/sayande01/iris_eda_classification

exploratory-data-analysis multiclass-classification random-forest

Last synced: 2 months ago
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This project applies a Random Forest classifier to the Iris dataset to predict flower species based on four features: Sepal Length, Sepal Width, Petal Length, and Petal Width. It includes data exploration, visualization, model building, evaluation, and hyperparameter tuning for optimal performance.

• Host: GitHub
• URL: https://github.com/sayande01/iris_eda_classification
• Owner: sayande01
• Created: 2024-11-20T15:42:19.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-20T15:46:15.000Z (7 months ago)
• Last Synced: 2025-02-13T02:38:36.570Z (4 months ago)
• Topics: exploratory-data-analysis, multiclass-classification, random-forest
• Language: Jupyter Notebook
• Homepage:
• Size: 505 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# *Iris Flower Classification using Random Forest*

## Project Description

The **Iris Flower Classification** project focuses on applying machine learning techniques to classify three species of Iris flowers (Setosa, Versicolor, and Virginica) based on their physical features: Sepal Length, Sepal Width, Petal Length, and Petal Width. This dataset, commonly known as the Iris dataset, is one of the most widely used datasets in the field of machine learning and data science due to its simplicity and well-defined class separations.

In this project, we implement a **Random Forest classifier** to predict the species of Iris flowers based on the given attributes. The process includes performing **Exploratory Data Analysis (EDA)**, visualizing the data through various plots, building a classification model, and fine-tuning the model's hyperparameters to optimize performance.

This project serves as an educational and practical exercise to demonstrate key concepts such as feature engineering, model training, performance evaluation, and hyperparameter optimization, all applied to a well-known dataset.

## Objective

The primary objective of this project is to:

1. **Explore the Iris dataset** through comprehensive exploratory data analysis (EDA), including understanding distributions, correlations, and patterns in the data.

2. **Visualize key relationships** between the features of the dataset using various visualization techniques, such as histograms, pair plots, and box plots.

3. **Preprocess the data** by splitting it into training and testing sets, ensuring proper data handling and preparation for model building.

4. **Develop a Random Forest model** to classify the flower species based on the input features. The model will be evaluated using standard metrics, such as accuracy, precision, recall, and F1-score.

5. **Optimize the model** through hyperparameter tuning to enhance classification performance, achieving the best results with the Random Forest algorithm.

6. **Provide insights** into the classification results, model performance, and feature importance, offering a deeper understanding of how each feature contributes to the classification task.

## Key Steps and Methodology

1. **Exploratory Data Analysis (EDA)**: 

   - Visualized data distributions using histograms to identify feature distributions for each species.

   - Performed pair plot analysis to examine pairwise relationships between features and understand correlations.

   - Used a correlation matrix to study the linear relationships between features.

   

2. **Model Building**:

   - Built a **Random Forest classifier**, a robust ensemble learning technique, to classify the species based on the input features.

   - Evaluated model performance using accuracy and classification metrics to ensure a reliable model.

3. **Hyperparameter Tuning**:

   - Tuned the model’s hyperparameters, such as the number of trees in the forest and maximum depth, to optimize performance.

   - Utilized grid search or random search techniques for hyperparameter optimization.

4. **Model Evaluation**:

   - Analyzed the model's performance on unseen test data to assess its generalization ability.

   - Discussed key evaluation metrics like confusion matrix, classification report, and feature importance.

## Features of the Project

- **Data Preprocessing**: Handling missing values, scaling features (if necessary), and splitting the dataset into training and testing sets.

- **Visualization**: Histograms, pair plots, and correlation matrix to explore relationships in the dataset.

- **Random Forest Classifier**: Implementation of Random Forest as the classification model, a powerful ensemble technique for decision tree-based learning.

- **Hyperparameter Optimization**: Fine-tuning the Random Forest model using grid search or other methods to improve performance.

- **Evaluation Metrics**: Detailed performance analysis using accuracy, precision, recall, and F1-score.

## Tools and Libraries Used

- Python 3.x

- **pandas**: For data manipulation and preprocessing

- **matplotlib & seaborn**: For data visualization and plotting

- **scikit-learn**: For machine learning model building, evaluation, and hyperparameter tuning

- **Jupyter Notebook**: For interactive development and documentation

## Conclusion

By the end of this project, you will have gained hands-on experience in:

- Performing EDA to understand datasets

- Applying Random Forest for classification

- Tuning machine learning models for optimal performance

- Evaluating model results using various metrics