Apache Spark's Machine Learning (ML) library

 Apache Spark's Machine Learning (ML) library offers a diverse range of algorithms and tools to address various data processing and modeling challenges. Below is a curated list of example scripts available in the Spark repository, each accompanied by a brief description of its functionality and typical use cases:​

https://github.com/apache/spark/tree/master/examples/src/main/python/ml 

🎯 Overview of Apache Spark ML Examples

This collection of 70 machine learning examples in Apache Spark covers a wide range of applications, including:

✔ Regression & Classification (Logistic Regression, Random Forest, GBT, Decision Trees)
✔ Clustering & Anomaly Detection (K-Means, Gaussian Mixture, LSH, Bisecting K-Means)
✔ Dimensionality Reduction (PCA, Word2Vec, LDA)
✔ Feature Engineering & Preprocessing (VectorAssembler, StringIndexer, Tokenizer)
✔ Text Processing (Word2Vec, N-Gram, Stop Words Removal)
✔ Survival Analysis & Recommendation Systems (AFT Regression, ALS)

Apache Spark’s MLlib provides a powerful and scalable machine learning solution, ideal for big data applications in industries such as fintech, healthcare, marketing, and e-commerce. 🚀

1. aft_survival_regression.py Implements Accelerated Failure Time (AFT) survival regression. This model is used for survival analysis, predicting the time until an event of interest (e.g., equipment failure, customer churn) occurs.
2. als_example.py Demonstrates Alternating Least Squares (ALS) for collaborative filtering. Commonly applied in recommendation systems, ALS helps in predicting user preferences for products or services.
3. binarizer_example.py Showcases the use of the Binarizer feature transformer. This tool converts continuous numerical values into binary (0 or 1) based on a specified threshold, aiding in feature engineering for classification tasks.
4. bisecting_k_means_example.py Implements the Bisecting K-Means clustering algorithm. This hierarchical clustering method is useful for grouping similar data points, especially in large datasets.
5. bucketed_random_projection_lsh_example.py Demonstrates Locality-Sensitive Hashing (LSH) using bucketed random projection. LSH is effective for approximate nearest neighbor searches in high-dimensional data, commonly used in duplicate detection and clustering.
6. chisq_selector_example.py Illustrates feature selection using the Chi-Squared test. This method selects features that have the strongest relationship with the outcome variable, enhancing model performance.
7. count_vectorizer_example.py Demonstrates the CountVectorizer for text processing. It converts a collection of text documents into vectors of token counts, a foundational step in text mining and natural language processing.
8. decision_tree_classification_example.py Implements Decision Tree classification. Decision Trees are intuitive models used for both classification and regression tasks, providing clear decision rules.
9. decision_tree_regression_example.py Showcases Decision Tree regression. This model predicts continuous values by learning decision rules from features.
10. dct_example.py Illustrates the Discrete Cosine Transform (DCT). DCT is used in signal processing to convert data into a sum of cosine functions, aiding in data compression and feature extraction.
11. elementwise_product_example.py Demonstrates the ElementwiseProduct transformer. It scales each input vector by a provided weight vector, useful in customizing feature transformations.
12. fm_classifier_example.py Implements Factorization Machines for classification. These models capture interactions between variables in high-dimensional sparse datasets, often used in recommendation systems.
13. fm_regressor_example.py Showcases Factorization Machines for regression tasks. They model pairwise interactions among features, suitable for predictive tasks with sparse data.
14. gaussian_mixture_example.py Demonstrates Gaussian Mixture Models (GMM) for clustering. GMM assumes data is generated from a mixture of several Gaussian distributions, useful in clustering and density estimation.
15. generalized_linear_regression_example.py Implements Generalized Linear Models (GLM) for regression. GLMs extend linear models to accommodate various types of response variables, including binary and count data.
16. gradient_boosted_tree_classifier_example.py Showcases Gradient-Boosted Tree (GBT) classification. GBT is an ensemble technique that builds multiple decision trees sequentially to improve predictive performance.
17. gradient_boosted_tree_regressor_example.py Demonstrates Gradient-Boosted Tree regression. This approach enhances regression models by combining the predictions of several weaker models.
18. index_to_string_example.py Illustrates converting indexed labels back to original strings. Useful in post-processing stages to interpret model predictions in their original categorical form.
19. kmeans_example.py Implements K-Means clustering. A popular algorithm for partitioning data into K distinct, non-overlapping subsets (clusters).
20. lda_example.py Demonstrates Latent Dirichlet Allocation (LDA) for topic modeling. LDA discovers topics within a collection of documents, aiding in understanding large text corpora.
21. linear_regression_with_elastic_net_example.py Showcases Linear Regression with Elastic Net regularization. Elastic Net combines L1 and L2 regularization to enhance model generalization, especially when dealing with correlated features.
22. linear_svc_example.py Implements Linear Support Vector Classification (SVC). SVC is effective for high-dimensional spaces and is commonly used in text classification.
23. logistic_regression_summary_example.py Demonstrates Logistic Regression and extracting model summaries. Logistic Regression is used for binary classification problems, and model summaries provide insights into model performance.
24. min_hash_lsh_example.py Illustrates MinHash for Locality-Sensitive Hashing. MinHash is effective for estimating the similarity between large sets, commonly used in clustering and duplicate detection.
25. min_hash_lsh_example.py Illustrates MinHash for Locality-Sensitive Hashing (LSH). MinHash is used to estimate the similarity between large sets efficiently. It is commonly applied in clustering, duplicate detection, and large-scale document comparison (e.g., near-duplicate web pages, plagiarism detection, and recommendation systems).
26. multilayer_perceptron_classification.py Implements a Multilayer Perceptron (MLP) classifier. MLPs are a class of feedforward artificial neural networks used for modeling complex non-linear relationships in data.
27. one_hot_encoder_example.py Demonstrates One-Hot Encoding for categorical features. This technique converts categorical variables into a binary vector representation, which is essential for algorithms that require numerical input.
28. pca_example.py Illustrates Principal Component Analysis (PCA). PCA is a dimensionality reduction technique that transforms features into a lower-dimensional space, preserving as much variance as possible.
29. pipeline_example.py Shows how to create an ML pipeline. Pipelines streamline the process of building machine learning workflows by chaining together multiple stages, such as data preprocessing, feature extraction, and model training.
30. polynomial_expansion_example.py Demonstrates Polynomial Expansion. This technique generates polynomial features from the original features, allowing linear models to fit non-linear data.
31. quantile_discretizer_example.py Implements Quantile Discretization. This method transforms continuous features into categorical ones by dividing the range into intervals with approximately equal frequencies.
32. random_forest_classifier_example.py Showcases Random Forest classification. Random Forests are ensemble learning methods that operate by constructing multiple decision trees, improving predictive accuracy and controlling overfitting.
33. random_forest_regressor_example.py Demonstrates Random Forest regression. Similar to its classification counterpart, this model is used for predicting continuous outcomes by averaging the predictions of multiple decision trees.
34. r_formula_example.py Illustrates the use of R formula for specifying ML models. R formulas provide a concise way to define relationships between features and labels, simplifying the model creation process.
35. standard_scaler_example.py Demonstrates feature scaling using StandardScaler. This technique standardizes features by removing the mean and scaling to unit variance, which is crucial for algorithms sensitive to feature scaling.
36. string_indexer_example.py Implements StringIndexer for encoding categorical features. This estimator maps a string column of labels to an ML-friendly column of label indices.
37. tokenizer_example.py Showcases text tokenization. Tokenization is the process of splitting text into individual words or tokens, which is a fundamental step in text preprocessing.
38. vector_assembler_example.py Demonstrates VectorAssembler. This transformer combines multiple columns into a single vector column, which is useful for assembling feature vectors.
39. vector_indexer_example.py Illustrates VectorIndexer. This tool identifies categorical features and indexes them, preparing datasets with categorical features for ML algorithms.
40. word2vec_example.py Implements Word2Vec for learning word embeddings. Word2Vec learns vector representations of words, capturing semantic meaning and relationships, which is useful in natural language processing tasks.
41. chi_square_test_example.py Performs Chi-Square hypothesis testing. This statistical test determines if there is a significant association between categorical variables.
42. correlation_example.py Calculates correlation matrices. Correlation measures the statistical relationship between two variables, indicating how changes in one variable are associated with changes in another.
43. hypothesis_testing_example.py Demonstrates hypothesis testing. This process involves making inferences about populations based on sample data, commonly used to test assumptions or claims.
44. kolmogorov_smirnov_test_example.py Implements the Kolmogorov-Smirnov test. This non-parametric test compares a sample with a reference probability distribution or compares two samples, assessing the goodness of fit.
45. sample_weighted_logistic_regression_example.py Showcases logistic regression with sample weights. Incorporating sample weights allows the model to handle imbalanced datasets by giving different importance to samples.
46. sampling_example.py Illustrates data sampling techniques. Sampling is the process of selecting a subset of data from a larger dataset, which is useful for reducing computational load or creating training/testing datasets.
47. vector_slicer_example.py Demonstrates VectorSlicer. This transformer selects a subset of features from a vector column, which is useful for feature selection.
48. stop_words_remover_example.py Implements StopWordsRemover. This tool removes common stop words from text, which are words that are often filtered out in text processing because they carry less meaningful information.
49. n_gram_example.py Showcases NGram generation. NGrams are sequences of 'n' consecutive words, used in text analysis to capture context and improve language models.
50. dct_example.py Demonstrates Discrete Cosine Transform (DCT). DCT is used in signal processing to convert data into a sum of cosine functions, aiding in data compression and feature extraction.
51. elementwise_product_example.py Illustrates ElementwiseProduct. This transformer scales each input vector by a provided weight vector, useful in customizing feature transformations.
52. max_abs_scaler_example.py Implements MaxAbsScaler. This scaler transforms each feature individually by dividing by the maximum absolute value, preserving sparsity in data.
53. min_max_scaler_example.py Demonstrates MinMaxScaler. This scaler transforms features by scaling each feature to a given range, typically [0, 1], which is useful for algorithms sensitive to the scale of data.
54. normalizer_example.py Showcases Normalizer. This transformer normalizes each feature vector to have unit norm, which is useful when the direction of the vector matters more than its magnitude.
55. pca_example.py Illustrates Principal Component Analysis (PCA). PCA is a dimensionality reduction technique that transforms features into a lower-dimensional space, preserving as much variance as possible.
56. polynomial_expansion_example.py Demonstrates PolynomialExpansion. This transformer expands features into polynomial space, which can capture non-linear interactions between features.
57. quantile_discretizer_example.py Implements QuantileDiscretizer. This transformer discretizes continuous features into equal-frequency bins, which can convert continuous variables into categorical ones
58. r_formula_example.py Implements R-style formulas for ML models. R formulas provide an easy way to specify relationships between variables, simplifying data preparation for machine learning models.
59. robust_scaler_example.py Demonstrates RobustScaler for feature scaling. This scaler removes the median and scales features using the interquartile range (IQR), making it more robust to outliers compared to standard scaling methods.
60. standard_scaler_example.py Implements StandardScaler for normalizing data. StandardScaler transforms features by removing the mean and scaling to unit variance, ensuring that all features contribute equally to machine learning models.
61. stop_words_remover_example.py Removes common stop words from text data. Stop words (e.g., "the", "is", "and") are often filtered out in Natural Language Processing (NLP) tasks to improve model efficiency and reduce noise.
62. string_indexer_example.py Converts categorical features into indexed numerical values. StringIndexer assigns numerical indices to string-based categorical variables, making them suitable for machine learning models that require numerical input.
63. tokenizer_example.py Splits text into individual words or tokens. Tokenization is an essential step in text preprocessing, widely used in NLP models such as text classification and sentiment analysis.
64. vector_assembler_example.py Combines multiple feature columns into a single vector column. This transformer is commonly used in data preprocessing, where multiple numerical features are assembled into a single input vector for ML algorithms.
65. vector_indexer_example.py Identifies categorical features in a dataset and indexes them automatically. This is useful for machine learning models that need categorical variables encoded as indexed values.
66. vector_slicer_example.py Extracts a subset of features from a vector column. VectorSlicer is helpful when you want to select specific features from a dataset for better model performance.
67. word2vec_example.py Implements Word2Vec for learning word embeddings. Word2Vec converts words into dense vector representations, capturing semantic relationships between words. It is widely used in NLP tasks like document similarity and word analogy predictions.
68. n_gram_example.py Generates N-Grams from text data. N-Grams capture contextual word sequences (e.g., "big data", "machine learning"), improving text models in NLP applications.
69. pca_example.py Performs Principal Component Analysis (PCA) for dimensionality reduction. PCA reduces the number of features while preserving the most important variations in the dataset, improving computational efficiency in large-scale ML problems.
70. polynomial_expansion_example.py Creates polynomial features from input variables. Polynomial expansion allows linear models to capture non-linear relationships by generating higher-order terms from the input features.