Decision Tree Classifier - Prediction using Decision Tree Algorithm

The project focuses on the classification of iris flowers using the decision tree algorithm. Several Python libraries such as Pandas,NumPy, Matplotlib, and Seaborn were utilized. These libraries provide essential functionalities for data manipulation, visualization, and analysis.

The dataset used for this project contains various attributes of iris flowers, such as sepal length, sepal width, petal length, and petal width. The goal is to train a decision tree model to predict the correct species of the iris flower based on these attributes.

Data preprocessing techniques were employed to handle missing values, normalize the data, and split it into training and testing sets. Exploratory data analysis was conducted using scatter plots, heatmaps, and pair plots to gain insights into the relationships between different attributes and the target variable.

The decision tree algorithm was then implemented, and hyperparameter tuning was performed to optimize the model's performance. The decision tree graph was generated to visualize the decision-making process of the trained model.