Data can come in all sorts of forms, such as numerical data from finance sheets, to images and audio clips. In all of these cases, the data needs to be processed to capture important information within the data. Data processing is a method for converting this raw data into something meaningful to get more information from the data.

Data processing tasks can be fully automated using machine learning algorithms and statistical data. Data processing task is a structured process that is completed as follows

- Data Collection
- Data Preprocessing
- Data Transformation
- Data Output
- Data Storage

This is the first step in data processing whose major task is to collect data from all the available and trusty resources. The main criteria in collecting data is the quality, Quality of the collected data must be good and accurate. This is a huge effort and time needed task in data processing. There may be some errors in the collected data that includes

- Missing data
- Inaccurate data
- Data imbalance
- Biased data

There are several techniques available to clear the problem that includes

- Take only clean and accurate free datasets
- Create your own private data
- Crowdsource the data

In this step, we need to make the data accurate and we have to address some issues in the collected data. Finally, we need to make the data into some specified format so that the algorithm will use that data which includes,

Data we will collect from different sources may have different formats and file formats, we need to convert these data into a small number of formats so that the machine learning algorithm can work more accurately with the data.

Real data is messy. Missing information is a part of real datasets. While there are no perfect solutions to address this problem, there are ways to use data with missing values without removing the entire observation.

**Ignore it**While this solution isn’t perfect, if you happen to know that individual observations are randomly being dropped, we can choose algorithms that handle missing values.

One example is random forests. It automatically detects missing values in most implementations and works with them to get a pretty decent model.

**Delete it**Deleting data, either by rows (samples) or columns (by features), is another way to handle missing data. This is especially useful if you suspect that:

A. Specific data points were unreliably acquired (row-wise deletion) or

B. If most of the features were not measured (column-wise deletion).Precautions must be taken to ensure we are not biasing the dataset and that we have enough data. Both of these issues will impact model performance.

**Impute it**Finally, we can impute data based on some systematic approach. This is ideal when we have small datasets where we can’t delete data, and we can’t simply ignore the missing values.

Common approaches to data imputation include filling missing values using the mean, median, or mode of the data. K-nearest neighbors or linear regression can also be used to impute missing values. In both cases, we make assumptions about the data that may not hold true and run the risk of biasing our data.

In some cases, we can’t able to take the whole dataset because of the errors in such a situation, we can take sample data from the huge collection of data and these sample data can be used to train the machine learning models.

**Steps involved in Sampling**

- Identify and define Target data
- Select sampling frame
- Choose sampling methods
- Determine Sample Size
- Collect the required data

**Types of Sampling Methods**

**Probability sampling**:In probability sampling, each component of the data has an equivalent possibility of being chosen. Probability sampling allows us the best opportunity to make an example that is a genuine representation of the data**Non-Probability Sampling**: In non-probability sampling, all components don't have an equivalent possibility of being chosen.

Now you already selected the machine learning algorithm for working with the dataset you have uploaded to the library. Now we have to look at the process of transformation of processed data. Many transformation methods include.

**Scaling **data is a method to standardize the range of values in our dataset without changing the underlying data distribution.

One widely applied scaling method is **min-max scaling**, where we transform the values in the range of 0 to 1.

**Normalizing **data changes the underlying data distribution so that it follows a Normal distribution.

One common way is to compute the **Z-score**. This method centers the data using the mean and sets the standard deviation to 1.

In this process, we use the decomposition algorithm to transform the heterogeneous data into triple model data. Here the data is grouped into structured, semi-structured, and unstructured data. And we will choose one for our machine learning algorithm.

**Label encoding** is the process of converting labels into a numerical format that a classification algorithm can use. Labels can be categorical data or any kind of text-based data.

Traditional label encoding assigns a unique value to each class. While this is a simple method, the model can inappropriately weigh the resulting numeric values.

A solution is to **one-hot encode** the data. We construct a new column for each category, and we assign the value to be 1 if it is a specific class or 0 if it’s not. This removes the possibility of different weights being factored into the algorithm.

Here we will get the output as some graphical representations like graph, video or image or something, which are meaningful and worthy which involves the steps

In this step, we are decoding the data to some understandable format like a graph, video, or image that can be accessed by the user any time, which is encoded earlier for use in the machine-learning algorithm.

This is the last step of data processing where we store the data or output in some devices for future use.

Data processing is converting the raw data into some meaningful format. That includes different steps, which are data collection where we collect data from various sources. Then we preprocess the data to remove the missing data and clear the errors in data. Then we transform the data for use in the algorithm. The fourth step includes decoding and receive the output and the final step is to save that output.