Machine Learning Model Development Flowchart

Follow these steps:

1. Raw Data: Collect initial data.
2. Apply pre-processing to data.
3. Data Preprocessing Modules:
   • Clean data (remove missing values, outliers, etc.).
   • Normalize or standardize features.
   • Encode categorical variables (e.g., using one-hot encoding or label encoding).
4. Prepare data for training:
   • Split data into training and validation sets.
   • Feature selection/extraction.
5. Apply machine learning algorithms to data.
6. Candidate Model: Evaluate different algorithms and choose the best one.
7. Deploy Chosen Model for applications.

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Data Processing Pipeline

Our goal is to design a data processing pipeline where each component runs asynchronously, processing large amounts of data and outputting results to another data store.

Pipeline Components

The pipeline consists of several components that perform the following actions:

• Pull in large amounts of data
• Process the data
• Output the results to another data store

Training Supervision Types

We need to determine the appropriate training supervision for our model:

• Supervised: Training with labeled data
• Unsupervised: Training with unlabeled data to find similarities
• Semi-supervised: Some labeled data, some not (e.g., Google Photos example)
• Self-supervised: (description missing)
• Reinforcement Learning: Positive reinforcement for good behavior, negative for bad behavior
• Regression: Predicting a value
• Multiple Regression: Using multiple features to make a prediction
• Univariate Regression: Predicting a single value
• Multivariate: Predicting multiple values

Data Access and Automation

In typical data environments, data is stored in relational databases or other data stores across multiple tables, documents, or files. Accessing the data requires credentials, authorizations, and knowledge of the data schema. However, for this project, data is provided in a single compressed file, housing.tgz, containing a CSV file with all the data.

Instead of manually downloading and decompressing the data, it's more efficient to write a function that automates this process. This is particularly useful if the data updates frequently, as a script can fetch the latest data or a scheduled job can update it automatically at regular intervals. Automating data fetching is also beneficial when installing the dataset on multiple machines.