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.
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.
- 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
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
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
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
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
- 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
- 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
- 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