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Introduction to the Course
Welcome to the Course
What You Will Learn
Why Spark MLlib for Machine Learning Projects
Course Workflow & Project Overview
Tools We’ll Use: Apache Spark, Spark ML, Apache Zeppelin
Overview of Telecom Dataset
Setting Up the Environment
Requirements
(Hands On) Installing JAVA
Steps for Installing JAVA
(Hands On) Setting JAVA environments
Steps for Setting JAVA environments
(Hands On) Apache Zeppelin Installation Steps on Ubuntu machine
Steps for Installing Apache Zeppelin on Ubuntu machine
(Hands On) Installing Docker Desktop on Windows 10/11
Steps for Installing Docker on Windows
(Hands On) Running Apache Zeppelin on Docker (Windows)
Steps for Running Apache Zeppelin on Docker
(Hands On) Configure and Connect to Spark interpreter
Steps for Configure and Connect to Spark Interpreter
Download Resources
Download Resources
TelcoCustomerChurn
Telecom Customer Churn Prediction
Importing Zeppelin file in Zeppelin Environment
Zeppelin Basics
What is Zeppelin
Features & Benefits
Notebook UI Overview
Markdown and text formatting
Creating and running paragraphs
Hands on Creating and Running paragraphs
Visualization Options (Tables, Bar chart, Pie chart, etc.)
Hands On - Types of Default Chart in Zeppelin
Zeppelin with Apache Spark
Spark interpreter details
Working with RDDs and DataFrames
Spark SQL queries and caching
Visualizing Spark outputs
Job tracking and performance tuning basics
Machine Learning Project
Understanding Customer Churn: Concepts, Types, and Business Impact
Understanding the Telecom Dataset
Loading Telecom Dataset in Apache Spark using Case Class and DataFrames
Exploratory Data Analysis (EDA) Using Spark SQL for Churn Insights
EDA on Customer Relationships and Service Usage Using Spark SQL
Exploratory Data Analysis (EDA): Service Features vs Customer Churn
Exploratory Data Analysis (EDA): Support & Backup Services Impact on Churn
Exploratory Data Analysis (EDA): Streaming Services & Contract Impact on Churn
EDA: Deriving Tenure from Charges & Its Impact on Churn
Building a Classification Model with Logistic Regression in Apache Spark
Handling Categorical Data with StringIndexer and Building ML Pipelines in Spark
Splitting Data and Preparing Feature Vectors for Machine Learning in Spark
Training a Logistic Regression Model and Preparing Test Data in Spark ML
Testing the Model and Evaluating Performance using Confusion Matrix in Spark ML
Understanding Prediction Probability and Evaluating Model Performance using ROC
Building a Naive Bayes Classification Model in Apache Spark
Building a One-vs-Rest (One-vs-All) Classification Model in Apache Spark
Introduction
Master Customer Churn Prediction with Machine Learning
Introduction
Download Resources
Download Source Code (2023) for Telecom Project
Download Data for Telecom Project
Download Source Code for Telecom Project
Project Begins
Introduction to Spark
(Old) Free Account creation in Databricks
(New) Free Account creation in Databricks
Provisioning a Spark Cluster
Introduction to Machine Learning
Basics about notebooks
Dataframes
Project Explaination Part 1
Project Explaination Part 2
Project Explaination Part 3
Project Explaination Part 4
Project Explaination Part 5