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Adding Decision Tree Classification README.md #106

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10 changes: 10 additions & 0 deletions 4_Classification/Decision_Tree_Classification/README.md
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## Decision Tree Classification

Decision Tree Classification is a machine learning algorithm used for solving classification problems. It builds a tree-like model of decisions and their possible consequences. The algorithm makes predictions by traversing the tree from the root node to the leaf nodes, where each node represents a feature or attribute and the branches represent the possible outcomes or decisions.


**How does it work?**

- Data pre-processing: In this step, we perform data cleaning and data transformation on the initial dataset. This includes handling missing values, encoding categorical data, splitting the dataset into training and test set, etc.
- Fitting Decision Tree Classification: The training data is used to build the Decision Tree Classification model.
- Prediction: Once the model is built, it can be used to predict the class of unknown cases.