DATA PROCESSING || DATA CLEANING USING PANDAS IN PYTHON
Data processing in machine learning refers to the manipulation and transformation of raw data into a format suitable for analysis and modeling. It involves several steps aimed at preparing the data to be fed into machine learning algorithms, ensuring that the algorithms can effectively learn from the data and make accurate predictions or classifications.
Here are some common steps involved in data processing for machine learning:
Data Collection: This is the initial step where raw data is gathered from various sources such as databases, files, sensors, APIs, or web scraping.
Data Cleaning: Raw data often contains errors, missing values, outliers, or inconsistencies. Data cleaning involves tasks like removing duplicates, handling missing values, correcting errors, and dealing with outliers to ensure data quality.
credits: starting template - VEED.io
Python: https://www.python.org/
Anaconda: https://www.anaconda.com/download/
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