https://github.com/kelvintechnical/decision-tree-classifier

https://github.com/kelvintechnical/decision-tree-classifier

Last synced: 27 days ago
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• Host: GitHub
• URL: https://github.com/kelvintechnical/decision-tree-classifier
• Owner: kelvintechnical
• Created: 2024-11-18T21:01:50.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-18T22:32:10.000Z (about 2 months ago)
• Last Synced: 2024-11-18T23:31:23.659Z (about 2 months ago)
• Language: Python
• Size: 57.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


# 
Decision Tree Classifier


Welcome to the Decision Tree Classifier project! This repository contains code to implement a basic machine learning classifier using the Decision Tree algorithm. A decision tree classifies data points by splitting the data into branches based on feature values and ultimately assigning a label to each point.


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## 
📫 How to reach me:




  


Email: [email protected]



  


LinkedIn: Kelvin R. Tobias



  


Bluesky: @kelvintechnical.bsky.social



  


Instagram: @kelvinintech





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## 
Project Overview


The Decision Tree Classifier is a simple and effective classification algorithm. In this project, we use Python libraries like pandas, scikit-learn, and matplotlib to build and visualize the decision tree. The goal is to classify data points based on specific features and evaluate the model's accuracy.


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## 
5 Things I Learned from This Project




  


The Purpose of Imports: Each Python library has specific functionalities that simplify machine learning workflows.

  


Data Splitting: Dividing data into training and testing sets ensures the model can generalize to unseen data.

  


Model Visualization: Plotting the decision tree helped me understand how the algorithm splits data at each node.

  


Parameter Tuning: Adjusting parameters like max_depth can simplify the model and reduce overfitting.

  


Evaluation Metrics: Accuracy alone might not always be enough, and exploring additional metrics like confusion matrices can help evaluate models better.



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## 
Code Explanation


Below is an overview of the key components of the code:




  


from sklearn.tree import DecisionTreeClassifier, plot_tree: Imports the Decision Tree Classifier for building the model and the plotting function for visualizing the tree.

  


from sklearn.model_selection import train_test_split: Splits the dataset into training and testing sets.

  


from sklearn.metrics import accuracy_score: Calculates the model's accuracy by comparing predictions to actual labels.

  


import pandas as pd: Used for handling the dataset as a DataFrame for easier data manipulation.

  


import matplotlib.pyplot as plt: Visualizes the decision tree with a readable plot.



```python

# Importing necessary libraries

from sklearn.tree import DecisionTreeClassifier, plot_tree

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

import pandas as pd

import matplotlib.pyplot as plt

# Create a simple dataset

data = {

    'Feature1': [1, 2, 3, 4, 5],

    'Feature2': [5, 4, 3, 2, 1],

    'Label': [0, 1, 0, 1, 0]

}

# Convert the dataset into a pandas DataFrame

df = pd.DataFrame(data)

# Separate the features (X) and labels (y)

X = df[['Feature1', 'Feature2']]  # Features

y = df['Label']  # Labels

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create the Decision Tree Classifier

classifier = DecisionTreeClassifier(max_depth=3)

# Train the model using the training data

classifier.fit(X_train, y_train)

# Make predictions on the test set

y_pred = classifier.predict(X_test)

# Evaluate the model's accuracy

accuracy = accuracy_score(y_test, y_pred)

print(f'Model Accuracy: {accuracy * 100:.2f}%')

# Visualize the Decision Tree

plt.figure(figsize=(12, 8))  # Adjust the plot size for readability

plot_tree(

    classifier, 

    feature_names=['Feature1', 'Feature2'], 

    class_names=['Class 0', 'Class 1'], 

    filled=True, 

    fontsize=10, 

    rounded=True

)

plt.show()