💡[FEATURE]: Cancer Prediction using Decision Tree and Random Forest

[Lingamuneni Santhosh Siddhardha]

Is your feature request related to a problem? Please describe.
Current cancer prediction models often lack accuracy and interpretability, making it challenging for healthcare professionals to make informed decisions.

Describe the solution you'd like
Implement cancer prediction using Decision Tree and Random Forest algorithms to improve accuracy and interpretability. Decision Trees provide a clear decision-making structure, while Random Forests enhance accuracy and robustness by combining multiple trees.

Describe alternatives you've considered

1. Logistic Regression: Simple and interpretable, but less effective in capturing complex relationships.
2. Support Vector Machines (SVM): Suitable for high-dimensional data, but less interpretable and computationally intensive.
3. Neural Networks: High accuracy but lack interpretability and require significant data and computational resources.

Additional context
Include visualizations of decision trees and feature importance from Random Forests. Compare performance metrics (accuracy, precision, recall, F1-score) of Decision Tree and Random Forest models with other methods to highlight strengths and weaknesses.

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