The 10 steps for ML-powered Data analytics help the analysts build models.  

In data science, data analysts make reports by extracting the data, analysing it, and using ML-powered data analytics to make predictions from the data. Data analysts implement the data analytics process.

The 10 steps for ML-powered data analytics help any data analyst make the best prediction about the business or sales in an organization.  On the other hand, if the user builds the machine learning model using the steps for ML-powered data analytics it results in customer sentiment analysis and improvement of accuracy and efficiency.

1. Collecting data for machine learning:

The First step is to ensure there is enough data to perform the machine learning execution. Many companies have lots of data and some companies are providing the data sets on some open-source platforms. Some datasets are refined, and some datasets will completely contain raw data.

The user needs to ensure the naming of the data set is ML- friendly. Some datasets ending with .xlsx and .csv may contain raw data, these files should be cleaned and manipulated into the required dataset.

2. Research On ML process and Tools:

Before performing any Machine learning process on any dataset, a user should make sure to have a strong knowledge and understanding of the trending machine learning process and the tools to be used. This helps the user to select which machine learning process will be used for which type of prediction.

3. Determine the value of machine learning:

Using Machine learning will let you know how to improve the business. business strategy and many others can be calculated or estimated by using the machine learning model, but the user need to know the value of machine learning and where and how to use it to get the desire result.

4. Check your data quality:

Before working with data, user should ensure that it is tangible, sufficient for analysis, error free, can be accessed without getting any interruptions. The data should be adequate to perform analysis and machine learning models.

5. Format data to make it consistent:

Every available dataset will be the raw data. To perform any ML-powered data analytics, we need to analyse the data with the necessary parameters. So, the user should extract the data, remove the unwanted or null entries, and manipulate the raw dataset.

6. Reduce data:

Many datasets have lots of raw data. While working or focusing on a particular task, dealing with a large amount of data can't give the appropriate result, so it is better to reduce the data. Data can be reduced by using methods like clustering, data cube aggregation, histograms, and data compression.

7. Complete data cleaning:

Data cleaning is the first step in working with the dataset. In the raw, there will be many missing values, null values, duplicates, and many more. To get the necessary data file, the user needs to fix and remove all the incorrect, missing, and formatted values within the dataset using the data cleaning techniques.

8. Collect and incorporate diverse, quality data:

The output of any analysis or machine learning model is completely based on the dataset. If the dataset is inadequate, biased, or errored, the prediction output based on the analysis will also be inadequate, biased, or errored.

To achieve good results, users must make the effort to collect the entire set of data required to work with machine learning models

9. Develop and Test A Demo:

The user should calculate the benefits of machine learning. Set up a demo test on the data using ML, and then compare the traditional data analytics to your demo result. To learn about the potential benefits, increase the scale of the demo. The user should understand the pros and cons.

10. Setup ML models:

Basic machine learning models can be implemented without waiting for data. To improve the business, ML models like increasing retention, reducing risk and fraud, and increasing profitability can be implemented. The outcomes of implementation and business improvement should be documented.