This repository contains the implementation of various data mining algorithms using Python. The algorithms included in this project are logistic regression, decision trees, random forest,, and naive Bayes. These algorithms are widely used in data mining and machine learning for classification tasks.
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Logistic Regression: Logistic regression is a popular classification algorithm used to model the relationship between input features and a binary or categorical target variable. It estimates the probability of an instance belonging to a particular class.
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Decision Trees: Decision trees are versatile and interpretable classification algorithms that partition the feature space based on a set of rules inferred from the training data. Each internal node represents a feature test, and each leaf node represents a class label.
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Naive Bayes: Naive Bayes is a probabilistic classifier based on Bayes' theorem. It assumes that the features are conditionally independent given the class label. Naive Bayes is computationally efficient and often used for text classification and spam filtering.
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Clone the repository using the following command: git clone https://github.com/Parsabzh/Data-mining-algrorithm.git
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Navigate to the specific algorithm folder (e.g.,
logistic_regression,decision_trees,naive_bayes) and run the corresponding Python script. -
Adjust the input data and parameters as required, or use the provided sample datasets.
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Execute the script to train the algorithm on the data and generate the classification results or other relevant outputs.
This project includes sample datasets that can be used for testing and experimentation. Feel free to explore and modify the datasets or use your own datasets for training and evaluation.
Contributions to the Data Mining Algorithms project are welcome! If you find any bugs, have suggestions for improvements, or want to add more algorithms, please feel free to open an issue or submit a pull request.
This Data Mining Algorithms project is developed by Parsabzh and contributors.