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Step by Step Predictive Analysis – Machine Learning

  • Last Updated : 22 Apr, 2020

Predictive analytics involves certain manipulations on data from existing data sets with the goal of identifying some new trends and patterns. These trends and patterns are then used to predict future outcomes and trends. By performing predictive analysis, we can predict future trends and performance. It is also defined as the prognostic analysis, the word prognostic means prediction. Predictive analytics uses the data, statistical algorithms and machine learning techniques to identify the probability of future outcomes based on historical data.

Why is predictive analysis important?
In predictive analysis, we use historical data to predict future outcomes. Thus predictive analysis plays a vital role in various fields. It improves decision making and helps to increase the profit rates of business and reduces risk by identifying them at the early stage.

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Predictive analysis is used in various fields like:



  • Online Retail
  • Healthcare
  • Education
  • Reduces Risks
  • Fraud Detection
  • Improvised market campaigning
  • weather forecasting
  • Social Media Analysis
  • cyber security
  • Recommendation and search engines
  • Government Sector etc.

Steps To Perform Predictive Analysis:
Some basic steps should be performed in order to perform predictive analysis.

  1. Define Problem Statement:
    Define the project outcomes, the scope of the effort, objectives, identify the data sets that are going to be used.
  2. Data Collection:
    Data collection involves gathering the necessary details required for the analysis. It involves the historical or past data from an authorized source over which predictive analysis is to be performed.
  3. Data Cleaning:
    Data Cleaning is the process in which we refine our data sets. In the process of data cleaning, we remove un-necessary and erroneous data. It involves removing the redundant data and duplicate data from our data sets.
  4. Data Analysis:
    It involves the exploration of data. We explore the data and analyze it thoroughly in order to identify some patterns or new outcomes from the data set. In this stage, we discover useful information and conclude by identifying some patterns or trends.
  5. Build Predictive Model:
    In this stage of predictive analysis, we use various algorithms to build predictive models based on the patterns observed. It requires knowledge of python, R, Statistics and MATLAB and so on. We also test our hypothesis using standard statistic models.
  6. Validation:
    It is a very important step in predictive analysis. In this step, we check the efficiency of our model by performing various tests. Here we provide sample input sets to check the validity of our model. The model needs to be evaluated for its accuracy in this stage.
  7. Deployment:
    In deployment we make our model work in a real environment and it helps in everyday discussion making and make it available to use.
  8. Model Monitoring:
    Regularly monitor your models to check performance and ensure that we have proper results. It is seeing how model predictions are performing against actual data sets.
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