{"id":105333,"date":"2025-03-06T19:23:56","date_gmt":"2025-03-06T13:53:56","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/what-is-predictive-modeling\/"},"modified":"2025-03-06T19:23:58","modified_gmt":"2025-03-06T13:53:58","slug":"what-is-predictive-modeling","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/what-is-predictive-modeling\/","title":{"rendered":"What is Predictive Modeling? Types and Functions"},"content":{"rendered":"\n<p>Ever been curious about how companies forecast sales, identify fraud, or suggest products you adore?<\/p>\n\n\n\n<p>The answer is predictive modeling, a method that uses past data to forecast the future.<\/p>\n\n\n\n<p>In this article, we'll demystify the process of predictive modeling, see how it's applied, and why it's crucial in the data age.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"definition-of-predictive-modeling\">Definition of Predictive Modeling<\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/what-is-predictive-modeling.png\"><img decoding=\"async\" width=\"782\" height=\"670\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/what-is-predictive-modeling.png\" alt=\"What is Predictive Modeling?\" class=\"wp-image-105337\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/what-is-predictive-modeling.png 782w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/what-is-predictive-modeling-300x257.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/what-is-predictive-modeling-768x658.png 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/what-is-predictive-modeling-150x129.png 150w\" sizes=\"(max-width: 782px) 100vw, 782px\" \/><\/figure>\n\n\n\n<p>Predictive modeling is a data-driven technique that uses statistical algorithms &amp; <a href=\"https:\/\/www.mygreatlearning.com\/blog\/machine-learning-algorithms\/\">machine learning methods<\/a> to analyze historical data and predict future outcomes.<\/p>\n\n\n\n<p>It assists businesses &amp; organizations make informed decisions by identifying patterns, trends, and relationships within <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-data-definition-types-importance\/\">data<\/a>.<\/p>\n\n\n\n<p>Predictive models are widely used in industries like finance, healthcare, marketing, and cybersecurity to forecast risks, detect fraud, and optimize operations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"key-components-of-predictive-modeling\">Key Components of Predictive Modeling<\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/components-of-predictive-modeling.webp\"><img decoding=\"async\" width=\"830\" height=\"549\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/components-of-predictive-modeling.webp\" alt=\"Key Components of Predictive Modeling\" class=\"wp-image-105335\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/components-of-predictive-modeling.webp 830w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/components-of-predictive-modeling-300x198.webp 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/components-of-predictive-modeling-768x508.webp 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/components-of-predictive-modeling-150x99.webp 150w\" sizes=\"(max-width: 830px) 100vw, 830px\" \/><\/figure>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Data<\/strong>\n<ul class=\"wp-block-list\">\n<li>The foundation of predictive modeling. It can be structured (numerical, categorical) or unstructured (text, images, audio).<\/li>\n\n\n\n<li>The quality, diversity, and volume of data influence model performance.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p class=\"block-course-highlighter\"><strong>Also Read: <\/strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/types-of-data\/\">Types of Data<\/a><\/p>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>Algorithms<\/strong>\n<ul class=\"wp-block-list\">\n<li>Different machine learning and statistical algorithms are used based on the problem type.<\/li>\n\n\n\n<li>Examples include:\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/linear-regression-in-machine-learning\/\">Linear Regression<\/a>, <a href=\"https:\/\/www.mygreatlearning.com\/blog\/decision-tree-algorithm\/\">Decision Trees<\/a>, <a href=\"https:\/\/www.mygreatlearning.com\/blog\/random-forest-algorithm\/\">Random Forest<\/a>, and <a href=\"https:\/\/www.mygreatlearning.com\/blog\/types-of-neural-networks\/\">Neural Networks<\/a><\/strong> for general predictive tasks.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/clustering-algorithms-in-machine-learning\/\">Clustering algorithms<\/a><\/strong> (e.g., K-Means) for grouping similar data points.<\/li>\n\n\n\n<li><strong>Deep Learning models<\/strong> for complex tasks like image recognition.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li><strong>Features (Predictors)<\/strong>\n<ul class=\"wp-block-list\">\n<li>The independent variables or attributes used by the model to make predictions.<\/li>\n\n\n\n<li>Example: In predicting customer churn, features may include transaction history, service complaints, and customer engagement metrics.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"4\" class=\"wp-block-list\">\n<li><strong>Target Variable<\/strong>\n<ul class=\"wp-block-list\">\n<li>The dependent variable or the outcome that the model aims to predict.<\/li>\n\n\n\n<li>Example: In fraud detection, the target variable could be binary (fraudulent or non-fraudulent transaction).<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"5\" class=\"wp-block-list\">\n<li><strong>Training and Testing Data<\/strong>\n<ul class=\"wp-block-list\">\n<li>The dataset is typically split into training (80%) and testing (20%) sets to assess model performance.<\/li>\n\n\n\n<li>Cross-validation techniques, like k-fold validation, help ensure robustness.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"6\" class=\"wp-block-list\">\n<li><strong>Evaluation Metrics<\/strong>\n<ul class=\"wp-block-list\">\n<li>Used to measure how well a model performs.<\/li>\n\n\n\n<li>Examples:\n<ul class=\"wp-block-list\">\n<li><strong>AUC-ROC (Area Under Curve - Receiver Operating Characteristic)<\/strong> for classification models.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/r-square\/\">R-squared<\/a> (R\u00b2) score<\/strong> for regression models.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"7\" class=\"wp-block-list\">\n<li><strong>Model Deployment &amp; Feedback Loop<\/strong>\n<ul class=\"wp-block-list\">\n<li>Once a model is deployed, it continuously learns from new data.<\/li>\n\n\n\n<li>Periodic retraining ensures it adapts to changing patterns and improves accuracy over time.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"types-of-predictive-models\">Types of Predictive Models<\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-model-types.png\"><img decoding=\"async\" width=\"530\" height=\"428\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-model-types.png\" alt=\"Types of Predictive Models\" class=\"wp-image-105339\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-model-types.png 530w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-model-types-300x242.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-model-types-150x121.png 150w\" sizes=\"(max-width: 530px) 100vw, 530px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-regression-models\"><strong>1. Regression Models<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-regression\/\">Regression models<\/a> predict a continuous numeric value based on input variables. They are widely used in finance, economics, and sales forecasting.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Linear Regression<\/strong> \u2013 Establishes a relationship between independent and dependent variables using a straight-line equation.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/logistic-regression-with-examples-in-python-and-r\/\">Logistic Regression<\/a> <\/strong>\u2013 Used for binary classification problems (e.g., predicting whether a customer will churn or not).<\/li>\n\n\n\n<li><strong>Polynomial Regression<\/strong> \u2013 Models complex relationships using polynomial equations.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Predicting house prices based on features like location, size, and number of rooms.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-decision-trees\"><strong>2. Decision Trees<\/strong><\/h3>\n\n\n\n<p>A decision tree is a tree-like model that splits data based on conditions at each node, making predictions based on a series of decisions.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Classification and Regression Trees (CART) <\/strong>\u2013 Used for both classification and regression tasks.<\/li>\n\n\n\n<li><strong>Random Forest <\/strong>\u2013 An ensemble of multiple decision trees that improves accuracy by reducing overfitting.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Predicting loan approval based on credit history and income level.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-neural-networks\"><strong>3. Neural Networks<\/strong><\/h3>\n\n\n\n<p>Inspired by the human brain, neural networks are deep learning models capable of identifying complex patterns.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Feedforward Neural Networks (FNNs)<\/strong> \u2013 Used for structured data analysis.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/recurrent-neural-network\/\">Recurrent Neural Networks<\/a> (RNNs)<\/strong> \u2013 Effective for sequential data, such as <a href=\"https:\/\/www.mygreatlearning.com\/blog\/time-series-forecasting\/\">time series forecasting<\/a>.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/cnn-model-architectures-and-applications\/\">Convolutional Neural Networks<\/a> (CNNs)<\/strong> \u2013 Commonly used in image and video recognition.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Detecting fraudulent transactions by analyzing transaction patterns in banking.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"4-time-series-models\"><strong>4. Time Series Models<\/strong><\/h3>\n\n\n\n<p>Time series models analyze sequential data to forecast future values based on past trends.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>ARIMA <\/strong>(AutoRegressive Integrated Moving Average) \u2013 Commonly used for economic and financial forecasting.<\/li>\n\n\n\n<li><strong>LSTMs <\/strong>(Long Short-Term Memory Networks) \u2013 A type of RNN designed for long-term dependency tasks.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Forecasting stock prices or predicting weather conditions.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"5-clustering-models\"><strong>5. Clustering Models<\/strong><\/h3>\n\n\n\n<p>Clustering models group similar data points together based on common characteristics.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/learning-data-science-with-k-means-clustering\/\">K-Means Clustering<\/a><\/strong> \u2013 A popular method for segmenting customers based on purchasing behavior.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/hierarchical-clustering\/\">Hierarchical Clustering <\/a><\/strong>\u2013 Builds a hierarchy of clusters, useful for customer segmentation.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Grouping customers based on their shopping habits for personalized marketing.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"6-ensemble-models\"><strong>6. Ensemble Models<\/strong><\/h3>\n\n\n\n<p>Ensemble learning combines multiple models to improve predictive accuracy and reduce variance.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/gradient-boosting\/\">Gradient Boosting <\/a>(GBM, <a href=\"https:\/\/www.mygreatlearning.com\/blog\/xgboost-algorithm\/\">XGBoost<\/a>, LightGBM, CatBoost)<\/strong> \u2013 Iteratively improves weak learners for better performance.<\/li>\n\n\n\n<li><strong>Random Forest <\/strong>\u2013 Uses multiple decision trees and averages their predictions.<\/li>\n\n\n\n<li><strong>Stacking &amp; Blending <\/strong>\u2013 Combines multiple algorithms for optimal results.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Detecting spam emails by combining multiple classification algorithms.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"7-naive-bayes-model\"><strong>7. Na\u00efve Bayes Model<\/strong><\/h3>\n\n\n\n<p>A probabilistic model based on Bayes' theorem is often used for classification tasks. It assumes that features are independent, making it computationally efficient. Example: Classifying emails as spam or not spam.<\/p>\n\n\n\n<p class=\"block-course-highlighter\"><strong>Also Read: <\/strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/introduction-to-naive-bayes\/\">What is Naive Bayes?<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"8-collaborative-filtering\"><strong>8. Collaborative Filtering<\/strong><\/h3>\n\n\n\n<p>A recommendation model that predicts user preferences based on past interactions and similar users.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>User-Based Filtering<\/strong> \u2013 Recommends items based on similar users' preferences.<\/li>\n\n\n\n<li><strong>Item-Based Filtering<\/strong> \u2013 Recommends items similar to what a user has interacted with.<\/li>\n\n\n\n<li><strong>Example<\/strong>: Netflix suggests movies based on a user\u2019s watch history.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-predictive-modeling-works\">How Predictive Modeling Works?<\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-modeling-working.png\"><img decoding=\"async\" width=\"638\" height=\"450\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-modeling-working.png\" alt=\"How Predictive Modeling Works?\" class=\"wp-image-105340\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-modeling-working.png 638w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-modeling-working-300x212.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/predictive-modeling-working-150x106.png 150w\" sizes=\"(max-width: 638px) 100vw, 638px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-1-data-collection\"><strong>Step 1: Data Collection<\/strong><\/h3>\n\n\n\n<p>Data is the foundation of predictive modeling. The quality, quantity, and relevance of the data significantly impact the accuracy of predictions.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"sources-of-data\"><strong>Sources of Data:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Structured Data:<\/strong> Databases (SQL, NoSQL), Spreadsheets, CRM systems<\/li>\n\n\n\n<li><strong>Unstructured Data:<\/strong> Text documents, Images, Videos, Social Media<\/li>\n\n\n\n<li><strong>Streaming Data:<\/strong> IoT sensors, Server logs, Web traffic<\/li>\n\n\n\n<li><strong>Third-party Data:<\/strong> APIs, Government reports, Open datasets<\/li>\n<\/ul>\n\n\n\n<p class=\"block-course-highlighter\"><strong>Also Read:<\/strong> <a href=\"https:\/\/www.mygreatlearning.com\/blog\/structured-and-unstructured-data\/\">Difference Between Structured and Unstructured Data<\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"challenges-in-data-collection\"><strong>Challenges in <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-data-collection\/\">Data Collection<\/a>:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Inconsistency<\/strong> \u2013 Different sources may provide conflicting data.<\/li>\n\n\n\n<li><strong>Data Gaps<\/strong> \u2013 Missing values in historical records.<\/li>\n\n\n\n<li><strong>Data Privacy and Security<\/strong> \u2013 Regulatory compliance like GDPR and HIPAA.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong> A retail company collects customer transaction history, website interactions, and social media engagement to predict future purchase behavior.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-2-data-preprocessing-feature-engineering\"><strong>Step 2: Data Preprocessing &amp; Feature Engineering<\/strong><\/h3>\n\n\n\n<p>Raw data is often noisy, incomplete, or inconsistent. Preprocessing ensures data quality and prepares it for model training.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"key-steps-in-data-preprocessing\"><strong>Key Steps in <a href=\"https:\/\/www.mygreatlearning.com\/blog\/data-preprocessing\/\">Data Preprocessing<\/a>:<\/strong><\/h4>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/data-pre-processing-process-1.webp\"><img decoding=\"async\" width=\"698\" height=\"600\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/data-pre-processing-process-1.webp\" alt=\"Steps in Data Preprocessing\" class=\"wp-image-105341\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/data-pre-processing-process-1.webp 698w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/data-pre-processing-process-1-300x258.webp 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/data-pre-processing-process-1-150x129.webp 150w\" sizes=\"(max-width: 698px) 100vw, 698px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Handling Missing Data:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Imputation (mean, median, mode)<\/li>\n\n\n\n<li>Dropping rows\/columns if missing data is excessive<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Removing Outliers:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Using Z-score, IQR (Interquartile Range), or clustering methods<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Normalization &amp; Standardization:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Min-Max Scaling:<\/strong> Scales data between 0 and 1<\/li>\n\n\n\n<li><strong>Z-score Normalization:<\/strong> Standardizes data by subtracting the mean and dividing by the standard deviation<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Encoding Categorical Variables:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>One-Hot Encoding:<\/strong> Converts categorical values into binary columns<\/li>\n\n\n\n<li><strong>Label Encoding:<\/strong> Assigns numerical values to categorical labels<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"feature-engineering\"><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-feature-engineering\/\">Feature Engineering<\/a>:<\/strong><\/h4>\n\n\n\n<p>Feature engineering improves model performance by creating new relevant features from existing data.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Combining Features:<\/strong> E.g., \"Total Spend per Month\" = \"Number of Transactions\" \u00d7 \"Average Spend\"<\/li>\n\n\n\n<li><strong>Extracting Time-based Features:<\/strong> E.g., Day of the week, Holiday, Seasonality in time-series data<\/li>\n\n\n\n<li><strong>Text Processing:<\/strong> Tokenization, Lemmatization, and Sentiment Analysis for text data<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong> In fraud detection, new features like \"Number of transactions per minute\" can help identify suspicious activity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"step-3-model-selection-training\">Step 3: Model Selection &amp; Training<\/h3>\n\n\n\n<p>Once the data is clean and transformed, the next step is selecting an appropriate model. The choice depends on the problem type (classification, regression, clustering, or time-series forecasting).<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"types-of-predictive-models\"><strong><strong>Types of Predictive Models:<\/strong><\/strong><\/h4>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/types-of-redictive-models.webp\"><img decoding=\"async\" width=\"686\" height=\"366\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/types-of-redictive-models.webp\" alt=\"Types of Predictive Models\" class=\"wp-image-105342\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/types-of-redictive-models.webp 686w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/types-of-redictive-models-300x160.webp 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/types-of-redictive-models-150x80.webp 150w\" sizes=\"(max-width: 686px) 100vw, 686px\" \/><\/figure>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Regression Models (Predicting Continuous Values):<\/strong>\n<ul class=\"wp-block-list\">\n<li>Linear Regression (e.g., predicting house prices)<\/li>\n\n\n\n<li>Logistic Regression (e.g., predicting the probability of customer churn)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>Classification Models (Categorizing Data):<\/strong>\n<ul class=\"wp-block-list\">\n<li>Decision Trees, Random Forest (e.g., spam detection)<\/li>\n\n\n\n<li>Na\u00efve Bayes (e.g., sentiment analysis)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li><strong>Clustering Models (Grouping Similar Data Points):<\/strong>\n<ul class=\"wp-block-list\">\n<li>K-Means Clustering (e.g., customer segmentation)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"4\" class=\"wp-block-list\">\n<li><strong>Time-Series Models (Forecasting Future Trends):<\/strong>\n<ul class=\"wp-block-list\">\n<li>ARIMA, LSTMs (e.g., stock market predictions)<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"model-training-process\"><strong>Model Training Process:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Splitting Data:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Training Set (70-80%)<\/strong> \u2013 Used to train the model<\/li>\n\n\n\n<li><strong>Validation Set (10-15%)<\/strong> \u2013 Used to <a href=\"https:\/\/www.mygreatlearning.com\/blog\/what-is-fine-tuning\/\">fine-tune<\/a> hyperparameters<\/li>\n\n\n\n<li><strong>Test Set (10-15%)<\/strong> \u2013 Used to evaluate final model performance<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hyperparameter Tuning:<\/strong> Adjusting model parameters to optimize performance using techniques like <a href=\"https:\/\/www.mygreatlearning.com\/blog\/gridsearchcv\/\">Grid Search<\/a> and Random Search.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Optimization Algorithms:<\/strong>\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.mygreatlearning.com\/blog\/gradient-descent\/\">Gradient Descent <\/a>(used in deep learning models)<\/li>\n\n\n\n<li>Adam Optimizer (adaptive learning rate optimization)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong> A healthcare provider trains a deep learning model using patient medical records to predict disease risks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"step-4-model-evaluation-validation\"><strong>Step 4: Model Evaluation &amp; Validation<\/strong><\/h2>\n\n\n\n<p>Once trained, the model needs to be evaluated for accuracy, generalizability, and reliability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"common-evaluation-metrics\"><strong>Common Evaluation Metrics:<\/strong><\/h3>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/evaluation-metrics.webp\"><img decoding=\"async\" width=\"722\" height=\"438\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/evaluation-metrics.webp\" alt=\"Common Evaluation Metrics for Predictive Modeling\" class=\"wp-image-105343\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/evaluation-metrics.webp 722w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/evaluation-metrics-300x182.webp 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/evaluation-metrics-150x91.webp 150w\" sizes=\"(max-width: 722px) 100vw, 722px\" \/><\/figure>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>For Classification Models:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Accuracy<\/strong> \u2013 Percentage of correct predictions<\/li>\n\n\n\n<li><strong>Precision &amp; Recall<\/strong> \u2013 Measures the relevance of positive predictions<\/li>\n\n\n\n<li><strong>F1-Score<\/strong> \u2013 Balances precision and recall<\/li>\n\n\n\n<li><strong>ROC-AUC Score<\/strong> \u2013 Evaluates classification performance across all probability thresholds<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>For Regression Models:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Mean Absolute Error (MAE)<\/strong> \u2013 Average absolute error of predictions<\/li>\n\n\n\n<li><strong>Mean Squared Error (MSE)<\/strong> \u2013 Penalizes larger errors<\/li>\n\n\n\n<li><strong>R\u00b2 Score<\/strong> \u2013 Measures how well predictions fit actual data<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li><strong>For Clustering Models:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Silhouette Score<\/strong> \u2013 Evaluates how well clusters are formed<\/li>\n\n\n\n<li><strong>Elbow Method<\/strong> \u2013 Determines the optimal number of clusters<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"cross-validation-techniques\"><strong>Cross-Validation Techniques:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>K-Fold Cross Validation:<\/strong> Splits data into multiple subsets to avoid overfitting.<\/li>\n\n\n\n<li><strong>Leave-One-Out Cross Validation (LOOCV):<\/strong> Tests model on one observation at a time.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong> A financial institution evaluates its credit risk prediction model using ROC-AUC scores before deployment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"step-5-deployment-continuous-improvement\"><strong>Step 5: Deployment &amp; Continuous Improvement<\/strong><\/h2>\n\n\n\n<p>Once a model is validated, it is deployed into a real-world application. However, models need continuous monitoring and updates to maintain accuracy over time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"deployment-strategies\"><strong>Deployment Strategies:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Batch Processing:<\/strong> Predictions are generated at scheduled intervals (e.g., daily customer churn reports).<\/li>\n\n\n\n<li><strong>Real-time Prediction:<\/strong> Models provide instant predictions via APIs (e.g., fraud detection during transactions).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"model-monitoring-maintenance\"><strong>Model Monitoring &amp; Maintenance:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Concept Drift:<\/strong> When the relationship between input and output changes over time, leading to model degradation.<\/li>\n\n\n\n<li><strong>Performance Tracking:<\/strong> Automated monitoring dashboards track key performance metrics.<\/li>\n\n\n\n<li><strong>Retraining Models:<\/strong> Updating models periodically with fresh data to maintain accuracy.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"continuous-learning\"><strong>Continuous Learning:<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Online Learning Models:<\/strong> Some ML models learn from new data in real time without retraining from scratch.<\/li>\n\n\n\n<li><strong>A\/B Testing:<\/strong> Running multiple models in parallel to determine the best-performing one.<\/li>\n\n\n\n<li><strong>Example:<\/strong> A ride-sharing app continuously updates its demand prediction model as user behavior changes.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"difference-between-independent-and-dependent-variable\"><strong>Difference Between Independent And Dependent Variable<\/strong><\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/dep-inde-variable.webp\"><img decoding=\"async\" width=\"601\" height=\"667\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/dep-inde-variable.webp\" alt=\"Independent And Dependent Variable\" class=\"wp-image-105344\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/dep-inde-variable.webp 601w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/dep-inde-variable-270x300.webp 270w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/dep-inde-variable-150x166.webp 150w\" sizes=\"(max-width: 601px) 100vw, 601px\" \/><\/figure>\n\n\n\n<table border=\"1\" cellpadding=\"10\" cellspacing=\"0\">\n  <thead>\n    <tr>\n      <th>Feature<\/th>\n      <th>Independent Variable<\/th>\n      <th>Dependent Variable<\/th>\n    <\/tr>\n  <\/thead>\n  <tbody>\n    <tr>\n      <td><strong>Definition<\/strong><\/td>\n      <td>Variables that are manipulated to observe their effect.<\/td>\n      <td>Variables that are measured or predicted, depending on the independent variable.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Role in an Experiment<\/strong><\/td>\n      <td>Acts as inputs or predictors.<\/td>\n      <td>Serves as outputs or responses.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Example<\/strong><\/td>\n      <td>In a study, hours spent studying.<\/td>\n      <td>The resulting test scores, dependent on study hours.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Nature<\/strong><\/td>\n      <td>Can be continuous (age, height) or categorical (sex, treatment type).<\/td>\n      <td>Can also be continuous (test scores, weight) or categorical (yes\/no outcomes).<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Manipulation<\/strong><\/td>\n      <td>Can be controlled or changed by researchers.<\/td>\n      <td>Observed in response to changes in the independent variable.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Graphical Representation<\/strong><\/td>\n      <td>Often represented on the x-axis.<\/td>\n      <td>Usually represented on the y-axis.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Purpose in Analysis<\/strong><\/td>\n      <td>Used to determine its effect on the dependent variable.<\/td>\n      <td>To understand its change due to the independent variable.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Example in Regression<\/strong><\/td>\n      <td>Used to predict the dependent variable.<\/td>\n      <td>The target being predicted based on the independent variable.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Confidence Intervals<\/strong><\/td>\n      <td>Not usually tested for significance.<\/td>\n      <td>Associated with statistical tests to measure prediction confidence.<\/td>\n    <\/tr>\n    <tr>\n      <td><strong>Direction of Influence<\/strong><\/td>\n      <td>Hypothesized to influence dependent variables.<\/td>\n      <td>Affected by independent variables.<\/td>\n    <\/tr>\n  <\/tbody>\n<\/table>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-to-select-the-right-model\">How To Select The Right Model?<\/h2>\n\n\n<figure class=\"wp-block-image aligncenter size-full zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/select-right-predictive-model.webp\"><img decoding=\"async\" width=\"614\" height=\"692\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/select-right-predictive-model.webp\" alt=\"How To Select The Right Predictive Model?\" class=\"wp-image-105345\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/select-right-predictive-model.webp 614w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/select-right-predictive-model-266x300.webp 266w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2025\/03\/select-right-predictive-model-150x169.webp 150w\" sizes=\"(max-width: 614px) 100vw, 614px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-understand-the-problem\">1. Understand the Problem<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Define the Objective: <\/strong>What are you trying to predict? Is it a classification or regression problem? Understanding the goal helps narrow down model choices.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Determine Success Metrics: <\/strong>Define how you will measure the success of the model (e.g., accuracy, precision, recall, F1 score, ROC-AUC for classification; RMSE, MAE for regression).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-analyze-the-data\">2. Analyze the Data<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/understanding-eda-in-python\/\">Exploratory Data Analysis (EDA)<\/a>: <\/strong>Conduct EDA to understand the structure, distribution, and relationships within the data. Look for outliers, missing values, and patterns.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Feature Types:<\/strong> Identify the types of features (numerical, categorical, ordinal) and their distributions, which will influence model suitability.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Size of the Dataset:<\/strong> Consider the size of your dataset. Some models (like neural networks) require large data sets, while simpler methods (like linear regression) may be effective with smaller datasets.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-consider-model-complexity\"><strong>3. Consider Model Complexity<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bias-Variance Tradeoff: <\/strong>Simple models often have high bias and low variance, while complex models may have low bias but high variance. Balance is crucial\u2014choose a model that is complex enough to capture the underlying patterns but simple enough to generalize well on unseen data.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Interpretable vs. Black-Box Models: <\/strong>If interpretability is important (for example, in healthcare or finance), you might prefer simpler models (like logistic regression or decision trees), whereas, in other cases, you might prioritize accuracy.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"4-initial-model-selection\">4. Initial Model Selection<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Baseline Model: <\/strong>Start with a simple model as a baseline (such as linear regression or logistic regression) to establish a performance benchmark.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Test Various Models: <\/strong>Evaluate a diverse set of models relevant to the problem, such as decision trees, random forests, <a href=\"https:\/\/www.mygreatlearning.com\/blog\/introduction-to-support-vector-machine\/\">support vector machines<\/a> (SVM), <a href=\"https:\/\/www.mygreatlearning.com\/blog\/gradient-boosting\/\">gradient boosting machines<\/a> (GBM), or neural networks.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"5-cross-validation\"><strong>5. Cross-Validation<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>K-Fold Cross-Validation:<\/strong> Use techniques like k-fold cross-validation to get a more reliable estimate of model performance. This helps to understand how the model performs on different subsets of data and mitigates overfitting.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/www.mygreatlearning.com\/blog\/hyperparameter-tuning-explained\/\">Hyperparameter Tuning<\/a>: <\/strong>Optimize the chosen models using techniques like Grid Search or Random Search to find the best parameters.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"6-evaluate-model-performance\"><strong>6. Evaluate Model Performance<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Use Multiple Metrics: <\/strong>Don\u2019t rely on a single metric to assess model performance. For classification, consider precision, recall, F1 score, ROC-AUC; for regression, consider RMSE, MAE, and R-squared.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Analyze Errors:<\/strong> Look at where and why the model is making errors. This can provide insights into whether a different model might suit the data better.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"7-feature-engineering\">7<strong>. Feature Engineering<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Feature Importance: <\/strong>Assess and select key features that contribute meaningfully to the predictions. Some models, like decision trees and random forests, provide built-in feature importance metrics.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Transformations: <\/strong>Explore feature engineering techniques, such as normalization, scaling, encoding categorical variables, or creating new features based on domain knowledge.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"8-final-model-selection\"><strong>8. Final Model Selection<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Trade-offs: <\/strong>Consider the trade-offs among various models in terms of accuracy, interpretability, training time, inference time, and computational resources.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Business Context: <\/strong>Choose a model that not only performs well statistically but also aligns with the organizational context, deployment strategy, and operational constraints.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"9-iterate-and-improve\"><strong>9. Iterate and Improve<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Feedback Loop: <\/strong>Monitor the model\u2019s performance over time. Gather new data and feedback to continually refine and update the model.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Experimentation:<\/strong> Don\u2019t hesitate to experiment with different models periodically to test if improvements can be made, especially as new data becomes available.<\/li>\n<\/ul>\n\n\n\n<p>Selecting the right predictive model is an iterative process that relies on a solid understanding of the problem, data characteristics, and the models themselves.<\/p>\n\n\n\n<p>By following these structured steps, you can make an informed decision that ultimately leads to more reliable predictions and better outcomes for your specific application.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"conclusion\">Conclusion&nbsp;<\/h2>\n\n\n\n<p>Predictive modeling is transforming industries with data-driven decision-making, risk estimation, and forecasting the future. To become proficient in <a href=\"https:\/\/www.mygreatlearning.com\/blog\/guide-to-predictive-analytics-definition-core-concepts-tools-and-use-cases\/\">predictive analytics<\/a>, you need a strong grasp of machine learning, data preprocessing, and model evaluation.<\/p>\n\n\n\n<p class=\"block-course-highlighter\">If you wish to develop expertise in data science and analytics, <span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\">the <\/span><a href=\"https:\/\/www.mygreatlearning.com\/mit-data-science-and-machine-learning-program\"><span style=\"box-sizing: border-box; margin: 0px; padding: 0px;\">MIT Data Science and Machine Learning Co<\/span>urse<\/a>&nbsp;offers hands-on training, live projects, and industry-specific insights to make you proficient in this highly-demanding sector.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Predictive modeling uses historical data to forecast future outcomes, helping businesses in various sectors make informed decisions. Explore its key components, types like regression models and decision trees, and real-world applications in this informative article. Learn how it shapes industries and enhances predictions.<\/p>\n","protected":false},"author":41,"featured_media":105350,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[9],"tags":[36797],"content_type":[],"class_list":["post-105333","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","tag-data-visualization"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>What is Predictive Modeling? 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