This project involves the classification of Iris flower species using data analysis and machine learning.
I analyse the classic Iris dataset, which includes 150 samples of three species: Setosa, Versicolor, and Virginica. Each sample is characterized by four features: sepal length, sepal width, petal length, and petal width.
I aim to build a model that accurately predicts the species of an Iris flower based on its features.
I implemented and evaluated two models. The first model uses Logistic Regression, achieving an accuracy of 97.37%. The second model is a custom-built classifier that I created achieved a 96.00% accuracy.
The repository includes all necessary code for
- Data preprocessing
- Exploratory data analysis
- Model training
- Evaluation
- Comparison of the models