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https://github.com/fayzi-dev/machin_learning

Machin Learning Full Algorithm (Linear Regression, Decision tree, Random forest, Neural network ,Logistic regression ,Support vector machine ,Naive Bayes ,Clustering, XGBoost,DBscan,KMeans)
https://github.com/fayzi-dev/machin_learning

algorithms artificial-neural-networks logistic-regression machine-learning matplotlib matplotlib-pyplot naive-bayes-classifier numpy pandas python python3 seaborn seaborn-plots sklearn sklearn-knn sklearn-library sklearn-linear-model sklearn-linear-regression sklearn-metrics sklearn-svm

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Machin Learning Full Algorithm (Linear Regression, Decision tree, Random forest, Neural network ,Logistic regression ,Support vector machine ,Naive Bayes ,Clustering, XGBoost,DBscan,KMeans)

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README 

        
# Machin learning



    1. Numpy library 

    2. Pandas Library 

    3. Linear Regression

    4. Multivariable Linear Regression

    5. Logistic Regression

    6. Data Preprocessiong 

    7. KNN (k Nearest neighbor) Algorithm

    8. Naive Bayes

    9. Support Vector Machin (SVM)

    10. Decision Tree 

    11. Random Forest 

    12. XGBoost Model

    13. K-Means Cluster Model (unsupervised learning)

    14. DBScan Cluster Model(unsupervised learning)

    15. PCA (Principal Component Analysis)

    16. Polynomial Regression



Machine learning is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed for each specific task. Instead, machine learning systems learn from data, identifying patterns and making predictions or decisions based on that data.



### Key Concepts



1. **Data**: Machine learning relies heavily on data. The quality and quantity of the data used can significantly impact the performance of the model.



2. **Algorithms**: Various algorithms are used to create machine learning models, including:

   - **Supervised Learning**: The model is trained on labeled data (input-output pairs). Examples include linear regression and decision trees.

   - **Unsupervised Learning**: The model is trained on unlabeled data and tries to identify patterns. Examples include clustering and dimensionality reduction.

   - **Reinforcement Learning**: The model learns through trial and error, receiving rewards or penalties based on its actions.



3. **Training and Testing**: The data is usually split into training and testing sets. The model learns from the training set and is evaluated on the testing set to assess its performance.



4. **Features and Labels**: In supervised learning, features are the input variables used for prediction, while labels are the output variables.



5. **Evaluation Metrics**: Different metrics are used to evaluate model performance, such as accuracy, precision, recall, and F1 score for classification tasks, and mean squared error for regression tasks.



### Applications

Machine learning has a wide range of applications, including:

- **Natural Language Processing (NLP)**: For tasks like sentiment analysis, translation, and chatbots.

- **Computer Vision**: For image recognition, object detection, and facial recognition.

- **Recommendation Systems**: Used by platforms like Netflix and Amazon to suggest products or content.

- **Healthcare**: For predictive analytics and personalized medicine.

- **Finance**: For fraud detection and algorithmic trading.



### Conclusion

Machine learning enables computers to learn from data, improving their performance over time without human intervention. It is a powerful tool with applications across various fields, making it a significant area of research and development in technology today.