Tools for Data Preprocessing and Model Development

Explore commonly used tools in machine learning for data preprocessing and model development tasks.

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Machine learning is a robust field that converts raw data into usable information. The process of transformation takes place in two stages:

  • Data preprocessing

  • Model development

Let’s look at the tools and techniques used in these two important stages.

Data preprocessing

Data preprocessing is an important stage in the machine learning pipeline that transforms, cleans, and organizes raw data to make it useful for training machine learning models. This phase is important because the quality of the data we include in our models directly affects their performance.

Here’s a list of common tools for data preprocessing in machine learning:

  • Jupyter Notebook: This interactive computing environment facilitates code execution, data visualization, and exploration.

  • NumPy: This basic Python library for numerical computations is frequently used for array operations and data manipulation.

  • SQL: This language was developed for handling and querying structured data.

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