Wisam Mohammed Abed Alqaraghuli Data preparation is a crucial step in machine learning that involves transforming raw data into a format suitable for analysis and model training. It encompasses a series of tasks aimed at improving the quality, consistency, and usability of the data. The process typically includes the following steps:

It is important to note that the specific data preparation steps may vary depending on the nature of the data, the problem domain, and the requirements of the machine learning task at hand.

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"Data preparation is the process of preparing raw data so that it is suitable for further processing and analysis. Key steps include collecting, cleaning, and labeling raw data into a form suitable for machine learning (ML) algorithms and then exploring and visualizing the data. Data preparation can take up to 80% of the time spent on an ML project. Using specialized data preparation tools is important to optimize this process.

Data preparation follows a series of steps that starts with collecting the right data, followed by cleaning, labeling, and then validation and visualization.

Collect data

Collecting data is the process of assembling all the data you need for ML. Data collection can be tedious because data resides in many data sources, including on laptops, in data warehouses, in the cloud, inside applications, and on devices. Finding ways to connect to different data sources can be challenging. Data volumes are also increasing exponentially, so there is a lot of data to search through. Additionally, data has vastly different formats and types depending on the source. For example, video data and tabular data are not easy to use together.

Clean data

Cleaning data corrects errors and fills in missing data as a step to ensure data quality. After you have clean data, you will need to transform it into a consistent, readable format. This process can include changing field formats like dates and currency, modifying naming conventions, and correcting values and units of measure so they are consistent.

Label data

Data labeling is the process of identifying raw data (images, text files, videos, and so on) and adding one or more meaningful and informative labels to provide context so an ML model can learn from it. For example, labels might indicate if a photo contains a bird or car, which words were mentioned in an audio recording, or if an X-ray discovered an irregularity. Data labeling is required for various use cases, including computer vision, natural language processing, and speech recognition.

Validate and visualize

After data is cleaned and labeled, ML teams often explore the data to make sure it is correct and ready for ML. Visualizations like histograms, scatter plots, box and whisker plots, line plots, and bar charts are all useful tools to confirm data is correct. Additionally, visualizations also help data science teams complete exploratory data analysis. This process uses visualizations to discover patterns, spot anomalies, test a hypothesis, or check assumptions. Exploratory data analysis does not require formal modeling; instead, data science teams can use visualizations to decipher the data."

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Dr.Sundus Fadhil Hantoosh

data preparation is the one type of process:

data is available in different formats such as structured ,semi structured and unstructured ,depending up on type of data we have to apply different data processing tools or techniques then process it to prepare data efficiently.which is suitable to apply different algorithms on it and get more accurate results.