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relataly.com

  • AI
    • Simple Regression
    • Classification: Two Class
    • Classification: Multi-Class
    • Clustering
    • Time Series Forecasting
    • Anomaly Detection
    • Natural Language
    • Recommender Systems
    • Reinforcement Learning
    • Responsible AI
  • Use Cases
    • Stock Market Forecasting
    • Algorithmic Trading
    • Sentiment Analysis
    • Churn Prediction
    • Fraud Detection
    • Predictive Maintenance
    • Marketing Automation
    • Customer Segmentation
    • Sales Forecasting
    • ChatBots
    • Fighting Crime
    • Risk Management
    • Image Recognition
  • Algorithms
    • CNNs
    • RNNs (LSTM)
    • Decision Trees
    • Random Decision Forests
    • Random Isolation Forest
    • Local Outlier Factor
    • Gradient Boosting
    • Collaborative Filtering
    • Content-based Filtering
    • K-Nearest Neighbors
    • K-Means
    • Affinity Propagation
    • Agglomerative Clustering
    • Logistic Regression
    • Naive Bayes
    • ARIMA
  • Data Science
    • Exploratory Data Analysis
    • Feature Engineering
    • Hyperparameter Tuning
    • Dimensionality Reduction
    • Model Interpretation
    • Data Visualization
    • Correlation
    • Measuring Performance
    • Cross-Validation
    • Vector Databases
    • SQLite
    • Data Science Environments
      • Anaconda
      • Azure Machine Learning
    • Python Libraries
      • Scikit-Learn
      • Tensorflow
      • Keras
      • Pytorch
      • PySpark
      • Chainer
      • OpenAI Gym
      • Seaborn
      • Fairlearn
      • Facebook Prophet
      • GeoPandas
  • Data & APIs
    • OpenAI API
    • REST APIs
    • NewsAPI
    • Coinmarketcap API
    • Coinbase API
    • Gate.io API
    • Yahoo Finance API
    • Statworx COVID-19 API
    • Twitter API
    • Reddit API
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    • Synthetic Data
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Requesting Data via REST APIs

Data scientists use APIs to request data from web-based servers. To do this, the data scientist would make a request using a specific URL and a set of parameters that specify the type of data being requested and any other relevant information. The server would then respond to the request by providing the data in a specified format, such as JSON. The data scientist can then use this data in their analysis, either by directly working with the data in its raw format, or by importing it into a data science tool or framework for further processing.

pandas datareader python tutorial machine learning

Using Pandas DataReader to Access Online Data Sources in Python

March 2, 2023October 15, 2022

Pandas DataReader is a library that allows data scientists to easily read data from a variety of sources into a … Read more

twitter social media relataly machine learning api streaming data midjourney-min

Streaming Tweets and Images via the Twitter API in Python

March 28, 2023January 3, 2021

Twitter is a rich source of data that can be used to understand current and future trends. Because tweets often … Read more

rest apis python tutorial relataly machine learning-min

Accessing Remote Data Sources via REST APIs in Python

March 28, 2023March 1, 2020

REST APIs provide straightforward access to remote data sources. Data scientists should learn about REST APIs because APIs (Application Programming … Read more

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