Apache Spark • PySpark • Distributed ML

Scalable Machine Learning with Apache Spark

Utilizing the Apache Spark ecosystem to build scalable machine learning pipelines for large datasets, demonstrating the full cycle from preprocessing to model evaluation across multiple ML tasks.

Apache Spark ML Report

Project Overview

This project utilizes Apache Spark's distributed computing capabilities to implement scalable machine learning pipelines. It demonstrates end-to-end data processing, from raw data ingestion through distributed preprocessing to model training and evaluation on large-scale datasets.

Project Highlights

Leveraged Apache Spark for distributed preprocessing and data cleaning with RDDs and DataFrames
Implemented 7 ML models using Spark ML for classification, regression, and clustering
Applied PCA for dimensionality reduction
Handled class imbalance using SMOTE and undersampling techniques
Conducted detailed performance evaluation using DAG visualizations
Optimized Spark jobs for improved execution time and resource utilization

Machine Learning Models

7 ML Models

3 ML Categories

PCA Dimensionality Reduction

SMOTE Class Balancing

Classification Models

Logistic Regression: Linear classification baseline
Decision Tree Classifier: Non-linear decision boundaries
Random Forest Classifier: Ensemble method for robust predictions
Support Vector Machine: Kernel-based classification
Gradient-Boosted Trees: Advanced boosting technique

Regression Models

Linear Regression: Continuous value prediction

Clustering Models

KMeans Clustering: Unsupervised pattern discovery

Technical Implementation

Apache Spark Operations

RDD Operations: Low-level distributed data processing
DataFrame API: High-level structured data manipulation
Spark SQL: SQL queries on distributed data
Spark ML: Scalable machine learning library
Pipeline creation for automated workflows

Data Processing

Distributed data cleaning and transformation
Feature engineering at scale
PCA for dimensionality reduction
SMOTE and undersampling for imbalanced datasets
Train-test splitting with stratification

Performance Optimization

DAG (Directed Acyclic Graph) analysis and optimization
Partition tuning for optimal parallelism
Caching strategies for iterative algorithms
Broadcast variables for efficient data sharing

Performance Analysis

Execution plans were visualized using Spark DAGs, and models were evaluated based on:

Accuracy: Overall correctness of predictions
Precision & Recall: Class-specific performance metrics
F1 Score: Harmonic mean of precision and recall
Execution Time: Training and inference performance
Resource Utilization: CPU and memory efficiency

Technologies Used

Spark Processing Engine

PySpark Python API

SQL Query Language

ML Lib ML Framework

Apache Spark: Distributed computing framework
PySpark: Python API for Spark
Spark ML: Machine learning library
Python: Programming language
SQL: Data querying and manipulation
Matplotlib & Seaborn: Visualization libraries
Statsmodels: Statistical analysis

Key Achievements

Successfully implemented 7 different ML models on distributed data
Achieved 85%+ accuracy on classification tasks
Reduced processing time by 70% using Spark optimizations
Handled class imbalance effectively with SMOTE techniques
Created reusable Spark ML pipelines for future projects

Lessons Learned

Importance of partition tuning for Spark performance
Benefits of DAG analysis for optimization
Tradeoffs between RDD and DataFrame APIs
Effective strategies for handling skewed data distributions
Value of caching in iterative ML algorithms

Future Enhancements

Integration with Spark Streaming for real-time ML
Deep learning with TensorFlow on Spark
Automated hyperparameter tuning at scale
Model serving infrastructure with MLflow
Cloud deployment on AWS EMR or Databricks