Introduction to Predictive Analytics

Meaning, scope, and importance of predictive analytics in business; types of analytics (descriptive, diagnostic, predictive, prescriptive); data-driven decision-making; overview of data mining concepts; structured and unstructured data; applications in marketing, finance, HR, and operations.

Data collection and cleaning; handling missing values and outliers; data transformation and normalization; exploratory data analysis (EDA); correlation and pattern identification; data visualization for insights; introduction to business datasets used in predictive analytics.

Measures of central tendency and dispersion; probability concepts; correlation and regression analysis; simple linear regression; introduction to multiple regression; hypothesis building; interpretation of statistical outputs; basics of forecasting techniques.

Concept of predictive modelling; classification and regression models; decision tree basics; clustering overview; model training and testing; overfitting and underfitting; model evaluation techniques (accuracy, precision, recall).

Applications of predictive analytics in business: customer behaviour prediction, demand forecasting, financial risk prediction, HR attrition analysis, and supply chain forecasting; Introduction to machine learning: meaning and scope; relationship between predictive analytics, statistics, and machine learning; supervised and unsupervised learning (basic concepts); simple machine learning workflow (data → model → prediction → evaluation). Business applications of machine learning in marketing, finance, HR, and operations; ethical issues including bias, fairness, transparency, and data privacy; future trends in AI- driven predictive systems.