Data Processing and Machine Learning – Why are They Important?

Machine learning algorithm requires massive datasets and data processing systems for self-learning. The model organizes data and learns through patterns. Let’s take the example of Amazon as they are one of the leading tech companies. Every day, they collect massive data for customer satisfaction and inventory management. Amazon uses advanced machine learning systems to manage data. Since they use trained algorithms, they have optimized inventory. That is why they can ship almost 1.6 million packages each day.

Importance of Data Processing in Machine Learning

Many businesses use a tremendous amount of data to solve complicated problems such as understanding customer behavior. Since machine learning handles critical tasks, you need efficient data processing system. Here is some importance of data processing in machine learning:

• Increase productivity and yield better results
• Simplifying reporting
• Generate accurate data in high speed

Data Preparation for Machine Learning

Whether it is machine learning or artificial intelligence, the model requires a significant amount of data. Every business collects data in different ways. They should clean and prepare the data for accurate results. The machine learning algorithm can help classify data into different groups. Here are some important steps for data preparation in machine learning.

1. Data Collection

The amount of data required for a project depends on the problem and its complexity. Data can be in different forms and nature. For instance, open-source data includes traffic data and weather data. Business requires:

• Numeric data such as customer retention rate and loan amount
• Categorical data such as color, gender, and property type
• Time-stamped data such as the number of products sold in a specific time-frame
•  Free text such as doctor’s notes or emails

2. Data Transformation

While collecting data from a different source, it should come in different formats. Therefore, it is essential to clean the data before adding it to the model. In some cases, you might also need to add missing values.

The technique to manage data is through data segmentation. Data scientists should create a different set of data according to their relationships. For instance, the sales team can split their data into different categories such as say, month, and year. This will give them clear patterns.

3. Data Training

Now data scientists should select the right model and algorithm according to their tasks. They should consider the 20:80 percent rule to split the data into evaluation and training sets. 20:80 percent rule means that analysts should spend 80% of their time to find, cleanse, and manage data. Meanwhile, they should spend the remaining time performing analysis.

4. Parameter Tuning

Analysts use different parameters and evaluation sets to test the model. They will fine-tune the data by including many training steps and leering rates. Parameter tuning and model evaluation is important to create effective machine learning models.

Conclusion

Data processing has a wide spectrum of advantages such as improving customer experience, processing insurance claims, and validating actions and changes in transactions. It is also helpful in government departments, especially for surveys.