Data Collection

Data is the fuel for AI models. Before you can train any model, you need quality data. Here's how to collect it from various sources.

Remember: Garbage in, garbage out. The quality of your data determines the quality of your model.

Data Sources

🌐

APIs

Structured data from web services

🗄️

Databases

SQL/NoSQL data stores

🕷️

Web Scraping

Extract data from websites

1. Using APIs

APIs (Application Programming Interfaces) provide structured access to data. Most modern services offer REST APIs.

python

Output:

Click "Run Code" to see output

Popular Data APIs

• Twitter API (social data)

• Google Maps API (location)

• OpenWeather API (weather)

• Alpha Vantage (finance)

• News API (articles)

• Spotify API (music)

2. Database Access

Connect to SQL or NoSQL databases to extract data for analysis.

python

Output:

Click "Run Code" to see output

3. Web Scraping

Extract data from websites when no API is available. Always check robots.txt and terms of service!

python

Output:

Click "Run Code" to see output

⚠️ Legal Note: Always respect robots.txt, rate limits, and terms of service. Some websites prohibit scraping. Use APIs when available.

Data Formats

CSV

Comma-separated values. Simple and widely used.

name,age,city
Alice,25,NYC
Bob,30,LA

JSON

JavaScript Object Notation. Hierarchical data.

{"name": "Alice",
"age": 25,
"city": "NYC"}

XML

Extensible Markup Language. Structured documents.

<person>
<name>Alice</name>
<age>25</age>
</person>

Best Practices

✓Document your data sources and collection methods

✓Implement error handling and retry logic

✓Respect rate limits to avoid being blocked

✓Store raw data before processing

✓Add timestamps to track when data was collected

✓Validate data quality during collection