Data Processing for AI Algorithms

what is Data Processing?

 Data processing in machine learning refers to the steps taken to prepare, clean, and transform the data before it is used to train a machine learning model. It is a critical step in the machine learning pipeline as the quality of the data can greatly impact the performance of the model.

What are the step involve in Data Processing?

1. Data processing can include a variety of tasks such as:
2. Data cleaning: Removing missing or duplicate data, handling outliers, and dealing with inconsistent or inaccurate data.
3. Data integration: Combining data from multiple sources to create a single data set.
4. Data transformation: Scaling, normalizing, or encoding data to make it more consistent and suitable for the machine learning algorithm.
5. Data reduction: Selecting a subset of the data to use for training and testing, or reducing the dimensionality of the data.
6. Data splitting: Dividing the data into training and test set

EDA (Exploratory Data Analysis):

EDA (Exploratory Data Analysis) is an approach to analyzing and understanding the data before building a machine learning model. It is a crucial step in the machine learning pipeline as it helps to identify patterns, trends, and relationships in the data, and can inform the selection of features and the development of the model.

EDA can include tasks such as:

Visualizing the data: Creating charts, plots, and histograms to understand the distribution and relationships of the variables.

Summarizing the data: Calculating statistics such as mean, median, and standard deviation to understand the central tendency and spread of the data.

Identifying outliers: Detecting and handling extreme or unusual values in the data.

Checking for missing data: Identifying and handling missing values in the data.

Identifying correlations: Understanding the relationship between different variables in the data.

Dimensionality reduction: Identifying the most important features and variables in the data.

By performing EDA, a data scientist can gain a deeper understanding of the data, identify potential issues, and make informed decisions about the development of the machine learning model

EDA Playlist in You Tube

EDA for Machine Learning - Link 1

EDA for Machine Learning - Link 2

To follow the Playlist for Learning the EDA Methods for Machine Learning 

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