PySpark Mastery: From Beginner to Advanced Data Processing

Delve into intermediate PySpark concepts, covering classifications, regressions, and clustering with logistic, decision tree, random forest, and gradient boosted models, after reviewing Spark basics.

Learn to perform linear regression with a PySpark pipeline using advertising data, convert features and labels into vectors, and handle categorical data with indexers and encoders.

Build a PySpark pipeline with a one hot encoder, indexer, output column, and vector assembler. Fit and transform the data, then split 40% for training and testing.

Split the data into 60% training and 40% test, then build a pipeline with feature indexing and linear regression. Examine model summary to review intercept, coefficients, standard errors, and p-values.

Explore PySpark prediction workflows and evaluate performance with mean squared error, root mean squared error, and R2. Generate predictions on test data and use a regression evaluator.

Create a spark session, load and prepare data for generalized linear regression. Train, evaluate (mse and r2), and generate predictions with a spark ml pipeline.

Develop and compare random forest regression and gradient boosting tree regression models in PySpark, loading data, creating features, and splitting training and test sets with root mean squared error evaluation.

Explore binomial logistic regression through a Spark session with bank.csv. Load data, inspect schema, and build a preprocessing pipeline using string indexers, one-hot encoders, and a vector assembler.

Build a PySpark pipeline with indexers, encoders, and an assembler for binomial logistic regression. Transform data using get_dummy category calls and num calls, then perform a train-test split.

Fit a logistic regression model in spark.ml, using indexers and a label converter within a pipeline to train, predict, and evaluate accuracy, including test error, area under ROC, and F-measure.

Plot a confusion matrix for binomial logistic regression, with and without normalization, using matplotlib. Include class labels and color maps, and introduce multi normal logistic regression.

Learn how to run multinomial logistic regression on Spark, set up Spark context and session, load CSV data, preprocess features, encode categories into dense vectors, and evaluate via a pipeline.

Explore PySpark multiclass classification with logistic regression, using label and feature indexers, transforming data, performing train‑test split, making predictions, and evaluating accuracy via a confusion matrix and optional normalization.

Create a spark context and session, load the dataset, and configure a decision tree classifier in a pipeline. Evaluate accuracy with a multi-class evaluator, view predictions, and report test error.

Create and evaluate a random forest classifier in PySpark, using pipelines with label and feature indexers, and plot and assess performance with normalized confusion matrices and accuracy.

Explore clustering with a k-means model in Spark, loading data, converting to features, handling categorical and continuous columns, and evaluating the optimal number of clusters using a cost metric.

Explore PySpark advanced topics, including RFM analysis with recency, frequency, and monetary value, text mining, and Monte Carlo simulation, plus prerequisites from introductory and intermediate PySpark courses.

Master rfm analysis with py spark by loading the online retailer data, cleaning nulls, computing total price, and deriving recency, frequency, and monetary value.

Learn to compute recency, frequency, and monetary values in PySpark, then perform RFM segmentation using quantile cutting points and user-defined functions with group-by aggregates.

Build a dense feature matrix from rfm data and apply k-means clustering in spark ml to segment customers. Use elbow analysis to select the optimal cluster count and visualize costs.

Compare elbow and silhouette analyses to select the optimal k for k means in PySpark. Build a pipeline and evaluate clustering with a clustering evaluator.

Explore text mining with PySpark as you convert images to text using a practical img_to_text function, processing image files, extracting text, and writing results with timing.

define a pdf_to_text function that takes a pdf directory and an image directory, converts pdfs to images, extracts text from images, and writes it to a file.

Explore Monte Carlo simulation to estimate fixed parameters by generating random numbers and visualize risk and uncertainty in financial and forecasting models.

Learn to implement Monte Carlo simulation steps for normal and log-normal setups, compute mu_n and theta estimates from sampled data, and visualize results with matplotlib plots.