Iris Flower Classification Project

This project is part of the Data Science internship at Oasis Infobyte. It involves building a machine learning model for classifying Iris flowers into different species based on their sepal and petal measurements. The project includes data preprocessing, exploratory data analysis, model training, and deployment using Streamlit for a user-friendly GUI.

Dataset

The Iris dataset used in this project is sourced from Kaggle. The dataset contains measurements of sepal length, sepal width, petal length, petal width, and the corresponding species of the Iris flowers. The goal is to build a classification model that can accurately predict the species of an Iris flower based on its measurements.

Project Steps

Data Preprocessing:

• Load the Iris dataset and inspect its structure.
• Remove unnecessary columns, such as an ID column, that do not contribute to the classification task.
• Perform any necessary data cleaning or handling of missing values.

Exploratory Data Analysis (EDA):

• Explore the dataset by visualizing the relationships between different features.
• Use plots, such as scatter plots, histograms, or box plots, to understand the distributions and characteristics of each feature.
• Analyze any correlations or patterns in the data.
• Gain insights into the Iris flower species and their distinguishing features.

Feature Engineering: (Not needed in this project)

• If required, apply feature engineering techniques such as feature scaling, dimensionality reduction, or creating new features based on domain knowledge.

Data Splitting:

• Split the dataset into training and testing sets.
• The training set will be used to train the machine learning models, while the testing set will be used for evaluating their performance.

Model Training:

• Select multiple machine learning algorithms suitable for classification tasks, such as Logistic Regression, Decision Trees, and Random Forests.
• Train each model using the training data.
• Tune hyperparameters, if necessary, using techniques like cross-validation or grid search.

Model Evaluation:

• Evaluate the trained models using the testing data.
• Calculate relevant evaluation metrics, such as accuracy, precision, recall, and F1-score, to assess the performance of each model.
• Compare the models and identify the best-performing one for further use.

Model Deployment:

• Extract the best-performing model along with any necessary preprocessing components, such as the label encoder and scaler.
• Use the Streamlit library to create a web-based GUI for the Iris flower classification model.
• Allow users to enter the sepal and petal measurements as input.
• Utilize the deployed model to predict the species of the Iris flower based on the user input.
• Display the prediction result to the user.

Repository Structure

• iris.csv: Directory to store the Iris dataset and any additional data files.
• iris-flower-classification.ipynb: Jupyter notebooks documenting the step-by-step process of the project.
• app.py: Python script for the Streamlit web application.
• requirements.txt: List of required packages and their versions.
• README.md: Readme file explaining the project, its steps, and deployment instructions.

Live Demo

Access the web application in your browser at the URL.

Conclusion

This project demonstrates the end-to-end process of building a machine learning model for Iris flower classification. It includes data preprocessing, exploratory data analysis, model training, and model deployment using Streamlit. The deployed model allows users to input sepal and petal measurements and receive predictions of the Iris flower species. Feel free to explore the notebooks and the web application to understand the project flow and make improvements based on your requirements.

Enjoy the project and happy coding!