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https://github.com/Mindful-AI-Assistants/Top-AI-ML-Algos-Paper-Code-and-More

data sciency
https://github.com/Mindful-AI-Assistants/Top-AI-ML-Algos-Paper-Code-and-More

machine-learning-algorithms

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data sciency

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##  
  ✨ Machine Learning Top Models Overview and 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐔𝐬𝐞𝐝 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 ✨

 A comprehensive guide to essential machine learning models, each with a brief description, example use cases, and links to detailed Jupyter Notebook examples.



 




#### 
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## I - [Machine Learning Top Models Overview]()










 








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###  1️⃣ **Linear Regression**

   - 📈 **Description**: Used for predicting continuous values.

   - 🔗 **How It Works**: Models the relationship between dependent and independent variables by fitting a linear equation to the data.

   - 💼 **Use Cases**: 

     - Predicting house prices based on features like square footage, number of bedrooms, and neighborhood.

     - Forecasting sales revenue from marketing spend.

   - 📘 **Notebook Examples**:

      - [Machine Learning - Linear Regression - Nsive Bayes](https://github.com/Mindful-AI-Assistants/Top-ML-Algorithms/blob/560ed3af4be2ab8d6a08da4c506c349a48f57293/ML-Files/Linear%20Regression/Linear-Regression%20copy.ipynb)

    

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### 🟢 2️⃣ **Logistic Regression**

   - ✅ **Description**: Ideal for binary classification problems.

   - 📊 **How It Works**: Estimates the probability that an instance belongs to a particular class.

   - 💼 **Use Cases**: 

     - Determining if an email is spam or not.

     - Predicting if a customer will purchase based on their online behavior.

   - 📘 **Notebook Example**:

      - [Logistic Regression](https://github.com/Mindful-AI-Assistants/Top-ML-Algorithms/tree/7c55f48770c20417a17a9f347f2efeebd61ab415/ML-Files/Linear%20Regression/Logistic%20Regression)

    

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### 🟢 3️⃣ **Decision Trees**

   - 🌳 **Description**: Splits data into subsets based on the value of input features.

   - 👁️ **Advantage**: Easy to visualize and interpret, but can be prone to overfitting.

   - 💼 **Use Cases**: 

     - Customer segmentation based on purchasing behavior.

     - Predicting loan approval decisions based on applicant details.

   - 📘 **Notebook Example**:

      - [Decision Tree Classifier](https://github.com/Mindful-AI-Assistants/Top-ML-Algorithms/tree/08ac3e7e0b8a6f2be262762e610008b95b4cc119/ML-Files/Linear%20Regression/Decision%20Trees)

    

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### 🟢 4️⃣ **Random Forest**

   - 🌲 **Description**: An ensemble method using multiple decision trees.

   - 🎯 **Benefit**: Reduces overfitting and improves accuracy by averaging multiple trees.

   - 💼 **Use Cases**: 

     - Predicting customer churn by combining different decision tree predictions.

     - Assessing loan default risk by using various decision paths.

   - 📘 **Notebook Example**:

      - [Random Forest Classifier Example](https://github.com/Mindful-AI-Assistants/Top-ML-Algorithms/tree/dfd0da7f9f4d7aa942547278a8b48f2e8ec9904f/ML-Files/Linear%20Regression/Randon%20Forests)



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### 🟢 5️⃣ **Support Vector Machines (SVM)**

   - 🚀 **Description**: Finds the hyperplane that best separates different classes.

   - 📈 **Advantage**: Effective in high-dimensional spaces and well-suited for classification tasks.

   - 💼 **Use Cases**: 

     - Image classification, such as distinguishing between cats and dogs.

     - Identifying cancerous tumors based on medical imaging data.

   - 📘 **Notebook Example**:

      - [SVM Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/05_support_vector_machines.ipynb)

    

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### 🟢 6️⃣ **k-Nearest Neighbors (k-NN)**

   - 🤝 **Description**: Classifies data based on the majority class among the k-nearest neighbors.

   - 🧩 **Note**: Simple and intuitive, but can be computationally intensive.

   - 💼 **Use Cases**: 

     - Recommending products based on user similarity.

     - Identifying handwritten digits in image data.

   - 📘 **Notebook Example**:

      - [k-NN Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/03_knn.ipynb)



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### 🟢 7️⃣ **K-Means Clustering**

   - 🔍 **Description**: Partitions data into k clusters based on feature similarity.

   - 💡 **Applications**: Useful for market segmentation, image compression, and more.

   - 💼 **Use Cases**: 

     - Customer segmentation for targeted marketing.

     - Compression of large image files by clustering similar pixels.

   - 📘 **Notebook Example**:

      - [K-Means Clustering Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/06_kmeans_clustering.ipynb)

    

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### 🟢 8️⃣ **Naive Bayes**

   - 📧 **Description**: Based on Bayes' theorem with an assumption of independence among predictors.

   - 📬 **Common Uses**: Particularly useful for text classification and spam filtering.

   - 💼 **Use Cases**: 

     - Email spam detection.

     - Sentiment analysis on customer reviews.

   - 📘 **Notebook Example**:

      - [Naive Bayes Classifier Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/04_naive_bayes.ipynb)



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### 🟢 9️⃣ **Neural Networks**

   - 🧠 **Description**: Mimic the human brain to identify patterns in data.

   - 🌐 **Applications**: Power deep learning applications, from image recognition to natural language processing.

   - 💼 **Use Cases**: 

     - Object detection in images (e.g., autonomous driving).

     - Language translation (e.g., English to Spanish translation).

   - 📘 **Notebook Example**:

      - [Neural Networks with Keras Example](https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/4.3-using-a-pre-trained-model.ipynb)

    

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### 🟢 🔟 **Gradient Boosting Machines (GBM)**

   - 🔥 **Description**: Combines weak learners to create a strong predictive model.

   - 🏆 **Applications**: Used in various applications like ranking, classification, and regression.

   - 💼 **Use Cases**: 

     - Predicting customer propensity to buy in e-commerce.

     - Ranking relevant search results based on past behavior.

   - 📘 **Notebook Example**:

      - [Gradient Boosting Example](https://github.com/dmlc/xgboost/blob/master/demo/jupyter/xgboost_demo.ipynb)



### 📘 Each of these models has its strengths and ideal applications. Choosing the right model depends on the data and task requirements!








## II- [𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐔𝐬𝐞𝐝 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬]()










 

 






### 1️⃣ 𝐏𝐚𝐧𝐝𝐚𝐬: 



This library is essential for data manipulation and exploration. It provides efficient data structures and functions to work with structured data.



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## 2️⃣ 𝐍𝐮𝐦𝐏𝐲:



Widely used for numerical computing, NumPy facilitates operations on large arrays and matrices, offering essential mathematical functions.



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## 3️⃣ 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛 & 𝐒𝐞𝐚𝐛𝐨𝐫𝐧:



These libraries are fundamental for data visualization. They allow users to create various types of plots and graphs to represent data visually.



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## 4️⃣ 𝐒𝐜𝐢𝐤𝐢𝐭-𝐥𝐞𝐚𝐫𝐧:



Ideal for machine learning tasks, Scikit-learn offers a range of algorithms and tools for data modeling, classification, regression, and clustering.



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##5️⃣ 𝐓𝐞𝐧𝐬𝐨𝐫𝐅𝐥𝐨𝐰 & 𝐏𝐲𝐓𝐨𝐫𝐜𝐡: 



These frameworks are essential for deep learning applications. They provide tools for building and training neural networks, enabling advanced machine learning tasks.



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## 6️⃣ 𝐒𝐭𝐚𝐭𝐬𝐦𝐨𝐝𝐞𝐥𝐬: 



This library is invaluable for statistical modeling and analysis. It offers a wide range of statistical tests and models for hypothesis testing and regression analysis.



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## 7️⃣ 𝐃𝐚𝐬𝐤:



Useful for parallel computing and handling large datasets, Dask enables users to work with data that exceeds the memory capacity of their systems.



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## 8️⃣ 𝐁𝐨𝐤𝐞𝐡 & 𝐏𝐥𝐨𝐭𝐥𝐲: 



These libraries are crucial for creating interactive visualizations and dashboards, and enhancing data exploration and presentation.








## 🔗 **References**



- [Simple Linear Regression Notebook](https://github.com/philipphundertmark/simple-linear-regression-notebook)

- [Python Data Science Handbook - Linear Regression](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.06-Linear-Regression.ipynb)

- [Machine Learning with Python and Spark - Linear Regression](https://github.com/ruslanmv/Machine-Learning-with-Python-and-Spark/blob/master/Linear-Regression/Linear-Regression.ipynb)

- [Logistic Regression Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/04_logistic_regression.ipynb)

- [Decision Tree Classifier Example](https://github.com/krishnaik06/Decision-Tree-Classifier/blob/master/Decision%20Tree%20Classifier.ipynb)

- [Random Forest Classifier Example](https://github.com/krishnaik06/Random-Forest-Classifier/blob/master/Random%20Forest%20Classifier.ipynb)

- [SVM Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/05_support_vector_machines.ipynb)

- [k-NN Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/03_knn.ipynb)

- [K-Means Clustering Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/06_kmeans_clustering.ipynb)

- [Naive Bayes Classifier Example](https://github.com/justmarkham/DAT8/blob/master/notebooks/04_naive_bayes.ipynb)

- [Neural Networks with Keras Example](https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/4.3-using-a-pre-trained-model.ipynb)

- [Gradient Boosting Example](https://github.com/dmlc/xgboost/blob/master/demo/jupyter/xgboost_demo.ipynb)



 



 

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Copyright 2024 Mindful-AI-Assistants. Code released under the  [Creative Commons License.](https://github.com/Mindful-AI-Assistants/AI-DataSciency-ML/blob/ea04c03ca9997d0caa870ab185a353637cdec6db/LICENSE)