Data Science - Handling Large Dataset

Efficiently handling large datasets in machine learning requires overcoming memory limitations, computational bottlenecks, and time-consuming processing

Basic Handling:

Python has various libraries (Pandas, NumPy, Scikit-learn) for data manipulation, analysis, and model building.

Understanding how to handle large datasets efficiently within this framework provides maximum flexibility and control over the entire machine learning workflow.

Advanced Handling:

Many advanced techniques for handling large datasets (like distributed computing with Dask or Spark) build upon core Python concepts.

No matter what !! If we want to handle larger dataset then please do follow one of the below strategies as and when needed.

1. Data Subsampling Techniques

Random Sampling: Select a random subset of the data. While simple, it might not be representative if the data has inherent biases or distributions.

Stratified Sampling: Divide the data into subgroups (strata) based on a relevant feature (e.g., class labels). Then, sample proportionally from each stratum to maintain the original class distribution.

K-Fold Cross-Validation: Divide the data into k folds. Train on k-1 folds and evaluate on the remaining fold. Repeat k times, using each fold as the validation set once.

2. Data Partitioning and Streaming

Data Sharding: Divide the dataset into smaller, independent chunks (shards). Process each shard individually, either sequentially or in parallel.

Data Streaming: Process data as it arrives, without storing the entire dataset in memory. This is crucial for real-time applications and continuously growing datasets.

3. Efficient Data Storage and Retrieval

Cloud Storage: Utilize cloud platforms like AWS S3, Google Cloud Storage, or Azure Blob Storage for cost-effective and scalable storage.

Data Lakes: Centralized repositories for storing large volumes of raw and processed data in various formats.

Data Warehouses: Optimized for analytical queries and reporting, suitable for structured data.

4. Distributed Computing Frameworks

Apache Spark: A powerful framework for large-scale data processing and machine learning. It supports distributed data processing and offers libraries like MLlib for machine learning algorithms.

TensorFlow: A popular open-source machine learning library with strong support for distributed training across multiple GPUs and machines.

PyTorch: Another popular deep learning framework with good support for distributed training and dynamic computational graphs.