This project was completed as part of my Data Science Internship at CodeAlpha. It involves building a machine learning model to classify Iris flowers into one of three species: Setosa, Versicolor, and Virginica. Using the famous Iris dataset, I applied various machine learning techniques to predict the flower species based on sepal and petal measurements.
The Iris dataset consists of 150 records of Iris flowers, each with the following features:
Sepal Length
Sepal Width
Petal Length
Petal Width
The goal is to classify the flowers into one of three species:
Setosa
Versicolor
Virginica
- Data Exploration
Analyzed the distribution of features across different species.
Used Pandas, Seaborn, and Matplotlib to visualize relationships in the dataset.
- Data Preprocessing
Cleaned the dataset by handling missing values (if any) and scaling the features to improve model performance.
- Model Training
Trained and compared the following machine learning models:
Logistic Regression
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
- Model Evaluation
Evaluated models based on:
Accuracy scores
Confusion matrices
The best-performing model was chosen for the final classification task.
- Visualization
Used Matplotlib and Seaborn to create visualizations, such as pair plots and decision boundaries, to demonstrate the model's classification capability.
End-to-End Machine Learning Workflow: From data exploration to model evaluation.
Model Selection: Comparison of different algorithms to select the best fit for the dataset.
Python Libraries: Gained proficiency in Pandas, Scikit-learn, Matplotlib, and Seaborn for data manipulation, modeling, and visualization.
The machine learning model can accurately classify Iris flowers based on the measurements of sepals and petals.
The visualization of decision boundaries and evaluation metrics shows how well the model distinguishes between the three Iris species.
GitHub Repository:
Kaggle Notebook:
A big thank you to CodeAlpha for the opportunity to grow and expand my skills in Data Science and Machine Learning! 🙌
Contributions are welcome! If you have suggestions or want to contribute to improving this project
Go to the repository on GitHub and click on the 'Pull Request' tab. Submit your changes for review. I’ll review your pull request and merge it if everything looks good! 😊