Data Collection Strategies and APIs
High-quality data is the foundation of every successful ML model — no algorithm can compensate for garbage input. This topic covers practical strategies for acquiring structured datasets from APIs, public repositories, and web sources.
Collecting Data from REST APIs
Most modern data sources expose REST APIs that return JSON or CSV. Python's requests library is the standard tool for querying them, and pagination handling is critical for large datasets.
Making API Requests with requests
<pre><code class="language-python">import requests
import pandas as pd
url = "https://api.example.com/data"
params = {"start_date": "2024-01-01", "limit": 1000}
response = requests.get(url, params=params)
response.raise_for_status()
df = pd.DataFrame(response.json()["results"])
print(df.head())</pre>
Handling Pagination
APIs often return data in pages. Loop through pages until no more data is returned, accumulating records into a list before converting to a DataFrame for efficiency.
<pre><code class="language-python">all_records = [] page = 1 while True: r = requests.get(url, params={"page": page, "limit": 100}) data = r.json()["results"] if not data: break all_records.extend(data) page += 1 df = pd.DataFrame(all_records)</pre>Public Datasets and Open Repositories
For many ML tasks, curated public datasets exist and are preferable to building a scraper from scratch. Knowing where to look saves enormous time.
Key Dataset Sources
- Kaggle — competition datasets with baselines (
kaggle datasets download) - UCI ML Repository — classic benchmark datasets
- Hugging Face Datasets — NLP and vision datasets via
datasetslibrary - Google Dataset Search — meta-search across repositories