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Predictive Modeling & Statistical Analysis: Leveraging Data Science to Make Better Decisions

Predictive Modeling & Statistical Analysis: Leveraging Data Science to Make Better Decisions


Our growing reliance on digital tools has led to an explosion of data. Between 2013 and 2023, the amount of data created, captured, and consumed worldwide every day grew from 9 zettabytes to 120 zettabytes–a staggering increase of 1,200%.1

This rapid growth means businesses have more data than ever at their fingertips. Information like client demographics and sales trends can help companies understand customers and predict the future to gain a competitive advantage. But, many businesses don’t know how to leverage this data successfully.

This article explores how professionals can use predictive modeling and statistical analysis to inform data driven-decision making processes and add value wherever they work.

Understanding Predictive Modeling

Predictive modeling uses software, statistical analysis, and other tools to estimate the likelihood of a future event or outcome. This approach leverages historical data to predict future outcomes and behavior, enabling organizations to make more informed decisions.2

There are many types of predictive models, including:3

  • Time Series Data Models: Use temporal data to predict future trends, such as weather patterns
  • Regression Models: Establish clear relationships between variables, such as connections between current economic trends and future home sales
  • Decision Tree Models: Graphically represent decisions and outcomes as a series of branching, interlinked nodes

Data Collection and Preprocessing

You can use qualitative or quantitative data to create predictive models. The questions you want to ask determine your data collection methods.4 For instance, if you want to predict new software sales, you can collect data on customer purchase behavior and sales of similar products.

Next, you’ll need to clean and prepare data for predictive modeling. This step involves:4

  • Removing duplicate, corrupt, and inaccurate data
  • Correcting mistakes in the dataset
  • Formatting data uniformly
  • Tracking down missing data

Exploratory Data Analysis

Start evaluating your data by conducting an exploratory data analysis (EDA). This process identifies general patterns in the dataset instead of answering specific questions or making assumptions.5

Predictive analytics software can perform EDA and present the results in data visualizations. For example, a histogram is a graph that uses bars to represent the distribution of variables. Histograms allow you to quickly spot patterns and outliers.5

Statistical Analysis Techniques

Data scientists use numerous statistical analysis methods to assess historical data and make predictions. Here are two popular techniques:

  • Descriptive Statistics: Summarize the key characteristics of data using mean, median, standard deviations, and other statistical measurements5
  • Inferential Statistics: Draw insights by comparing the model’s predictions to observations made while gathering samples6

Introduction to Machine Learning Algorithms

Data scientists can use machine learning algorithms to analyze data and build predictive models. For instance, hospitals can train machine learning algorithms to analyze cancer patients’ medical records and predict the most effective treatment.7

Like humans, machine learning algorithms improve their performance by analyzing data. These models can learn in one of two ways:7

  • Supervised Learning: Professionals use labeled input data to train algorithms to detect patterns and predict outputs
  • Unsupervised Learning: The algorithms find patterns and draw inferences from vast quantities of unlabeled data

Model Building and Training

Building and training machine learning algorithms can be a complex process. Data scientists typically start by dividing historical data into training and testing sets. The training data has known inputs and corresponding outputs, which teach the algorithms to make accurate predictions. Testing data allows professionals to evaluate the model’s performance.7

Evaluating Model Performance

Data scientists can assess the performance of predictive models and machine learning algorithms by comparing their projections to actual events or data. Metrics often used to evaluate accuracy include:7

  • Root Mean Squared Error: This calculation compares the model’s predicted values to their actual values
  • Mean Absolute Error: This formula calculates the difference between a set of predicted values and the actual values
  • Variance Ratio Criterion: This metric calculates the separation between data points within and between clusters to understand how the model identifies patterns

Feature Selection and Engineering

Feature selection is the process of identifying relevant variables or traits in a dataset. Irrelevant or redundant features get excluded from the dataset. This step improves the accuracy of predictive analytics by ensuring the model gets trained on the most important features.8

Overfitting and Regularization

Overfitting occurs when the predictive model misinterprets random changes in data as meaningful trends. Data scientists can use feature selection when training models to prevent this problem. Additionally, regularization methods like ranking the importance of features to improve the model’s precision.7

Interpretability and Explainability

Data scientists must know how to interpret and explain the predictive model’s results to assist decision-making. Common techniques for analyzing a model’s predictions include:9

  • Input-Response Analysis: Give the model different samples to gain insights into how it makes predictions
  • Data Visualization: Represent the model’s predictions in charts and graphs to understand how it makes connections between data

Predictive Modeling in Business Applications

Predictive analytics has many practical applications in business settings, such as:10

  • Identifying fraud
  • Predicting customer churn
  • Optimizing supply chains
  • Forecasting sales and demand

Ethical Considerations in Predictive Modeling

Predictive modeling is a valuable tool for analyzing big data, but it can raise ethical dilemmas. For instance, Target sparked controversy after allegedly using predictive analytics to identify a pregnant woman and sending her coupons for baby supplies.11

Businesses can apply predictive analytics ethically by following these principles:12

  • Accountability
  • Human centricity
  • Inclusivity
  • Transparency

Successful Predictive Modeling Applications

Here are two case studies of effective predictive modeling applications:

  • Efficiency Vermont uses predictive modeling to help businesses analyze energy consumption patterns and reduce their carbon footprint13
  • FedEx collects real-time data to create more accurate models that forecast the demand for package delivery14

Harness the Power of Predictive Modeling

Use data science to lead change in your organization and industry. An Online Master's in Data Science from New York Tech can help you develop the skills you need to thrive in this rapidly growing field. You’ll strengthen your data science capabilities by taking classes like Data Visualization, Machine Learning, and Statistics for Data Science. You can also gain hands-on experience by collaborating with faculty on research projects for national organizations.

Contact an admissions outreach advisor today for more information.

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