Spark Machine Learning Project (House Sale Price Prediction)

Install java 11 using apt update and apt install openjdk 11, confirm with y, and verify java -version shows 11.0.27 for Apache Zeppelin.

Learn to set up the Java environment by editing /etc/profile with sudo nano, add the Java home, path, and JRE exports, and verify with echo $JAVA_HOME.

Install and run Apache Zeppelin on Ubuntu 0.12.0, download and untar the package, start the daemon, access localhost:8080, and create or import notebooks for basic Zeppelin activities.

Install Docker Desktop on Windows 10 or 11 by downloading the installer, running as administrator, and launching Docker to run containers, images, volumes, and build features in future sessions.

Learn how to run Apache Zeppelin on Docker Desktop for Windows, pull the image, run the container on port 8080, start and stop Zeppelin, and review logs to access notebooks.

Configure and connect Apache Spark with Zeppelin to run Spark locally; install Spark, start Zeppelin, set Spark master locally, edit configs, and execute a sample dataframe.

Import the house price prediction Zeppelin notebook into the Zeppelin environment, ensuring the correct file paths such as home/data engineer/downloads are set for successful data loading with train.csv.

Explore Apache Zeppelin, a web-based notebook for data ingestion, discovery, analytics, visualization, and collaboration using SQL and Scala, with built-in Spark integration and multi-language backends.

Discover how Apache Zeppelin enables multi-language analytics, real-time execution, and interactive dashboards for Spark and Hadoop, enhancing collaboration and automated ETL workflows.

Explore the Apache Zeppelin notebook user interface with notebooks, toolbar, paragraphs, interpreters, and output panels, and learn to run Spark, SQL, and Python code with dynamic forms and scheduling.

Explore markdown and text formatting in Apache Zeppelin to document, annotate, and structure notebooks with headers, lists, links, tables, and images for readable, collaborative, presentation-ready reports.

Create and run paragraphs in Apache Zeppelin to build modular data pipelines, using Spark, SQL, and Markdown blocks, and visualize results with integrated display and output.

Explore the Apache Zeppelin UI, create and run notebooks and paragraphs with Spark and Markdown interpreters, and learn paragraph-level execution, editing, and output controls.

Explore how Apache Zeppelin turns raw data into visual insights using tables, bar charts, line charts, pie charts, and scatter plots to analyze trends and correlations in house price data.

Explore the five default charts in Zeppelin—bar, pie, area, line, and scatter—through interactive examples, demonstrating how to visualize employee data and support business decisions.

Configure and use the Apache Spark interpreter in Apache Zeppelin to run Spark SQL, data frames, and RDDs, visualize results, and perform EDA on an employee attrition dataset.

Master RDDs and data frames in Apache Spark within Zeppelin to switch between fine-grained transformations and optimized SQL-like data manipulation, visualizing results interactively for the house sale price prediction project.

Learn to run Spark SQL queries on a data frame in Apache Zeppelin, register temp tables, and use in-memory caching to accelerate interactive queries and visualizations.

Visualize Spark outputs in Apache Zeppelin by turning Spark data into interactive tables and charts with temporary views and the percentage SQL interpreter, enabling clear insights.

Track Spark job performance and tune execution with Apache Zeppelin, using the Spark Web UI to analyze bottlenecks and apply caching, repartitioning, and broadcasting joins for production-grade pipelines.

Explore essential Spark ML imports for a house sale price prediction project, including Spark SQL functions, vector assembler, and linear regression to preprocess data and build a regression model.

Load a csv dataset into a spark data frame with infer schema and header options in scala, preview data, and print the schema to kick off the machine learning pipeline.

Transform raw data into a numeric feature vector for the Spark ML project by applying string indexer to categorical columns and using vector assembler to create features and a label.

Spark ML pipeline encodes a string category column into numeric indices for model training, typically followed by vector assembly to create feature vectors for training.

Define and build a Spark MLlib pipeline by chaining transformers and estimators, including string indexer and vector assembler, to preprocess data and train a model for house price prediction.

Split the data into training and testing sets (70/30) to train on 70% and test on 30%, evaluate on data to avoid overfitting, and rename test label to true label.

Combine numeric and categorical features into a single dense feature vector with vector assembler. Prepare training data by transforming with the assembler and naming sales price as label for regression.

Train a regression model in spark using linear regression to predict house prices. Configure label and feature columns, set max iterations and reg parameter, and fit on 70% of data.

Prepare the testing data by applying the same vector transformation as training with VectorAssembler, renaming sales price to true label and creating a test frame with feature vector.

Test the trained spark regression model on unseen test data, generate predictions, compare them with actual house prices, and prepare the data for evaluating accuracy with a matrix like RSM.

Evaluate a linear regression model in spark by visualizing predicted versus actual values with a scatter plot, using a temporary view, spark sql, pandas, and seaborn to assess accuracy.

measure model performance with rmse to quantify the average difference between true house prices and predictions, using spark ml's regression evaluator on label and prediction columns.

Learn SPARC machine learning for wholesale price prediction with Apache Spark on the Databricks community edition. Build and evaluate a linear regression model to predict housing prices.

Explore Apache Spark, a high-performance engine that distributes work across a cluster. Use dataframes for structured data, machine learning, graph processing, and streaming, via notebooks for practical predictive analytics.

Create a free Databricks account by navigating to the sign-up page, entering details, and confirming your email to access the platform.

Learn how to create a free Databricks account by visiting databricks.com, selecting community edition, receiving credentials by email, and logging in to practice for free.

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Provision a spark cluster by logging into the website, navigating to cluster, creating a new cluster named sparklers, and monitoring its status from pending to active.

Learn supervised and unsupervised machine learning, using features and labels to train predictive models with Spark and generate predictions from new data.

Create and name a notebook, then write and run code in runnable cells to see outputs like 1+1 equals 2; use magic commands for documentation and diverse content.

Explore dataframes in Spark for house price data: load data with a schema, select columns, filter rows where price > 2, create temporary in-memory views, and visualize results in notebooks.

Explore regression models as predictive tools that relate a continuous dependent variable to independent predictors for forecasting and causal analysis, focusing on linear regression.

Learn key spark ml terms for model building: 70/30 train-test splits, vector assembler, feature transformation, iterations, and regularization techniques to curb overfitting.

Learn to build a spark-based house price prediction model using regression, with data loading, feature engineering via vector assembler and string indexer, and 70/30 train-test split, then evaluate with RMSE.

Conclude the spark machine learning project for house sale price prediction with gratitude and best wishes for your future. The instructor thanks you for enrolling and encourages continued learning.