Making data more meaningful and informative is the effort of changing it from a given form to one that is considerably more useable and desired. This entire process can be automated using Machine Learning algorithms, mathematical modeling, and statistical expertise. Depending on the task we are conducting and the needs of the machine, the output of this entire process can be in any desired form, including graphs, videos, charts, tables, photos, and many more.

Steps to perform Data Processing

1. Collection

2. Preparation

3. Input

4. Processing

5. Output

6. Storage

Collection

The most important step in beginning with ML is to acquire accurate and high-quality data. Any verified source, such as data.gov.in, Kaggle, or the UCI dataset repository, can be used to acquire data. The learning process of the model will be facilitated and improved by high-quality and accurate data, and the model will produce cutting-edge outcomes when tested. The process of gathering data uses up a tremendous amount of money, time, and resources. The type of data required to carry out tasks or conduct research must be determined by organizations or researchers. For instance, multiple photos of people in a range of human expressions are required when working on the Facial Expression Recognizer. The validity and dependability of the model's conclusions are ensured by good data.

Preparations

The gathered information can be in a raw state that cannot be supplied to the machine right away. This procedure involves gathering datasets from many sources, analyzing those datasets, and then creating a new dataset for additional processing and investigation. You have the option of performing this preparation manually or automatically. Data can be prepared in numerical forms as well, which would hasten the learning process for the model. A matrix of N X N dimensions can be created from an image, with each cell's value representing a single picture pixel.

Input

Now that the data has been prepared, it may not yet be machine-readable, so certain conversion methods are required to change this data into a readable form. A high level of computation and accuracy are required to complete this activity. Examples of sources from which data can be gathered are MNIST Digit data (pictures), Twitter comments, audio recordings, and video clips

Processing

At this point, algorithms and ML approaches are needed to accurately and efficiently process the instructions given over a massive volume of data.

Output

At this stage, the machine obtains results that are meaningful and simple for the user to deduce. Reports, graphs, films, and other products are examples of output.

Storage

The output that has been collected, the data model data, and all other helpful information are preserved for use in the following step.

Data Cleaning

One of the crucial components of machine learning is data cleaning. It is crucial to the process of creating a model. There are no hidden twists or secrets to discover, but it's also not the fanciest aspect of machine learning. However, effective data cleaning determines a project's success or failure. Since better data "beats fancier algorithms," professional data scientists typically devote a significant amount of their time to this step. If the dataset is thoroughly cleaned, there is a potential that we can get decent results using straightforward techniques as well. This can be quite helpful at times, especially when it comes to computing when the dataset size is enormous.

Various data kinds will require various methods of cleansing. But this methodical approach is always an excellent place to start.

Steps in Data Cleaning

1. Removal of Unwanted Information: This includes eliminating variables from your dataset that are redundant, duplicate, or irrelevant. The most frequent instances of duplicate observations occur while gathering data, and irrelevant observations do not genuinely apply to the particular issue you're trying to solve. The efficiency is significantly changed by redundant observations since the data repeats and may add to the right or wrong side, generating unreliable results. Any type of data that is irrelevant to us and can be immediately eliminated is referred to as an irrelevant observation.

2. Fixing Structural errors: Structural errors are errors that occur during measurement, data transfer, or other related processes. Typographical errors in feature names, using a different name for the same property, mislabeling classes (different classes that should be the same), and uneven capitalization is examples of structural faults. The model might treat America and America as distinct classes or values even if they both stand for the same value, or it might treat red, yellow, and red-yellow as distinct classes or attributes even though one class can be a member of the other two classes. These structural flaws render our model ineffective and produce results of low quality.

3. Controlling undesirable outliers: With some models, outliers can lead to issues. For instance, decision tree models are more resistant to outliers than linear regression models. In general, we shouldn't get rid of outliers until we have a good cause too. Removing them can sometimes enhance performance, but not always. To delete the outlier, one must have a valid explanation, such as suspicious measurements that are unlikely to be a part of actual data. Outliers are those data points that are significantly different from the rest of the dataset.

4. Handling missing data: A deceptively challenging problem in machine learning is missing data. We are unable to simply disregard or delete the missing observation. They need to be addressed carefully because they can be a sign of something significant. Dropping observations with missing values is one of the two most typical methods for handling missing data.

   The absence of the value itself might be instructive. Additionally, even when some elements are absent, you frequently need to make predictions based on new facts in the actual world! utilizing previous observations to estimate the missing values. Once more, "missingness" is usually informative in and of itself, therefore you should alert your algorithm if a value is missing. Even if you create a model to infer your values, you won't be adding any new knowledge. You’re just reinforcing the patterns already provided by other features.

   Some data cleansing tools

   • Openrefine

   • IBM Infosphere Quality Stage

   • TIBCO Clarity

   • Cloudingo

   • Trifacta Wrangler

     Thank you for reading, Next, we will cover Feature Scaling and other topics.