When you want to learn about data and its uses, there is a lot of terminology floating around the internet that relates to data science. Predictive analysis is a concept at the very core of data science, but what does it comprise of and what stages are involved in the overall process? The following is a breakdown and explanation of the key stages of predictive analysis.

Data Exploration

This is the process of understanding and clarifying the nature of the data that has been uploaded to your application. This means acknowledging the data types involved, how that data is delivered, what type of files the data is contained in and how the the data is stored in those files. The list could technically go on, but these are the key factors that are considered in the data exploration stage.

Data Cleaning

Cleaning or wrangling data is very important in the early stages of predictive analysis where rogue data is detected and corrected. Rogue data can be data in the dataset that is incorrect, improperly formatted, incomplete or even corrupt. Deduplicating is also involved here, which is the process of either removing or merging duplicate data points. Finding redundancies in the dataset is important so that the data that could even be ethically removed from the dataset without impacting the analysis process in a negative way. Clean data is crucial for starting off the data analysis process in the best way, as the cleanliness of the dataset will directly impact the quality and reliability of predictive analysis results.

Data Modelling

Predictive modelling of data can take different forms. The model used will be selected based on the given dataset. Forecasting models are the most popular models for predicting future values based on historical data. There are other models such as the classification model (widely utilised in machine learning) and the outliers model (for detecting anomalies). Depending on the particular use case, an appropriate model is available.

Performance Analysis

The model is built and analysed for its efficiency in predicting future values. The accuracy of the model needs to be above a particular level in order to be considered useful. This is only possible where the previous stages of data exploration and cleaning have been executed to a high standard so that the model can be built with high quality data that makes sense.

In conclusion, the predictive analysis process proves to consist of several stages at which the utmost care and attention to detail should be considered as the quality of the preparation of a dataset for modelling and analysis will directly impact the quality and usefulness of the insights gained.