Data Preparation
Data Cleaning
Before proceeding with analysis, it’s crucial to ensure the integrity and quality of the data. In this section, we’ll perform data cleaning operations to remove any null or irrelevant values from the sensor data.
Data Transformation
Data transformation involves reshaping and laos whatsapp number data structuring the data into a format suitable for analysis. Here, we’ll transform the raw sensor readings into a more structured format, aggregating them at an hourly level.
Data Aggregation
Aggregating the data allows us to summarize and condense information, making it easier to analyze trends and patterns. In this snippet, we aggregate sensor data by machine ID and hourly timestamp, calculating the average sensor value for each interval.
And aggregation functionalities enhance the comprehensiveness of our analysis and ensure that we’re working with high-quality, structured data for predictive maintenance modeling.
Model Training
With the preprocessed data in hand, we can proceed to train a predictive maintenance model. Let’s say we choose to use a logistic regression model for this task.