Flower Species Classification

Predict the species of an Iris flower Setosa, Versicolor or Virginica by using an end-to-end machine learning pipeline and deploy the best model in a web app for user testing.

Table of Contents

1. Project Overview
2. Dataset
3. System Overview
4. Installation & Usage
5. Results

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Project Overview

This repository implements a complete workflow for multiclass classification on the classic Iris dataset. It covers:

• Data gathering & Loading in Editor
• Preprocessing (cleaning, encoding, scaling, train/test split)
• Training and comparing multiple classifiers
• Selecting and persisting the best model
• Creat8hg a Flask WebApp & deploying the best model
• Getting predictions through a Flask web-interface

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Dataset

• Source: Kaggle Iris dataset (150 samples, 4 features)
• Classes:
  • Iris-setosa
  • Iris-versicolor
  • Iris-virginica
• Features: sepal length, sepal width, petal length, petal width

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System Overview

Phase 1 – Preprocessing & Modeling (Jupyter Notebook)

1. Environment & Libraries
2. Data Loading & Exploration
3. Data Preprocessing
4. Model Training & Comparison
5. Evaluation & Selection
6. Saving Model & Encoder as .pkl

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Phase 2 – Web Application (Python Script)

1. Dependencies
2. App Initialization
3. Input Handling & Prediction
4. Response Rendering

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Installation & Usage

1. Clone the repo

   git clone https://github.com/<username>/Flower-Species-Classification.git
   cd Flower-Species-Classification
   

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Results

These are results of the Five Algorithms used:

1. 95.19% : Logistic Regression
2. 95.14% : Decision Tree
3. 96.710% : Random Forest
4. 97.10% : SVM
5. 95.14% : Naive Bayes

The Choosen Best model is SVM with an accuracy of 97.10%.