This project focuses on building and evaluating a machine learning model using the Iris dataset. The Iris dataset is a well-known dataset in the field of machine learning and statistics, often used as a beginner's introduction to pattern recognition. The dataset contains 150 samples of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width. These samples are classified into three species of iris flowers: Iris setosa, Iris versicolor, and Iris virginica.
The project is organized into the following sections:
Data Exploration: Initial analysis and visualization of the dataset to understand its structure and characteristics. Data Preprocessing: Handling missing values, feature scaling, and splitting the dataset into training and testing sets. Model Selection: Choosing appropriate machine learning algorithms for classification. Model Training: Training the selected models on the training data. Model Evaluation: Assessing the performance of the models using various metrics and visualizations. Hyperparameter Tuning: Fine-tuning model parameters to improve performance. Model Deployment: Exporting the trained model for future use in predictions.