🦋˖𓂃🌸˖ ִֶָ🦩˖·🎀˳⋆ ִֶָ🌺 ִֶ˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖˳·🦩˖🎀˳⋆ ִֶָ🌺 ִֶ ˖˳ ZΞΝ˖˖⋆ 🌷𓍢 ִֶָ🍄⋆˳·🌸˖ ִֶָ🌷𓍢˖·🌹˖˳·🦩˳ ִֶ˖⋆˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖·🌸˖🍄⋆˳·🌸˖ ִֶָ 🌷 ִֶָ🌺˳·🌹

🍄⋆˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖˳˳··🎀˳⋆ ִֶָ🌺 ִֶ˖·⋆˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖˳·🦩 ˖˳ ZΞΝ˖˖ ִֶָ🍄⋆˳·🌸˖ ִֶָ🦩˖·🎀˳⋆ ִֶָ🌺 ִֶ˖·⋆˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖˳· ִֶ˖⋆˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖˳·🦩˳˖𓂃🦋 ִֶָ

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

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

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

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

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

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

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.

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

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

• Simple Linear Regression Notebook
• Python Data Science Handbook - Linear Regression
• Machine Learning with Python and Spark - Linear Regression
• Logistic Regression Example
• Decision Tree Classifier Example
• Random Forest Classifier Example
• SVM Example
• k-NN Example
• K-Means Clustering Example
• Naive Bayes Classifier Example
• Neural Networks with Keras Example
• Gradient Boosting Example

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