1. Linear Regression
A powerful yet interpretable algorithm for predicting continuous numerical values. Common applications include price forecasting, demand estimation, and trend analysis.
- Logistic Regression
Widely used in binary classification contexts like spam detection and fraud prediction. It estimates class probabilities using the logistic (sigmoid) function.
- Decision Trees
These intuitive, tree-structured models perform both classification and regression. They’re easy to interpret and can handle mixed data types, though they may overfit if not carefully controlled.
- Random Forest
An ensemble of decision trees that aggregates predictions to improve accuracy and control overfitting. Ideal for robust performance in medical diagnostics, finance, and more.
- Support Vector Machines (SVM)
Excellent for classification in high-dimensional spaces, SVMs identify the optimal boundary between classes and excel in tasks like image recognition and text classification.
- K‑Nearest Neighbors (KNN)
A non‑parametric method that classifies data based on proximity to labeled examples. Best suited for smaller datasets or pattern recognition tasks such as handwriting or recommendation systems.
- Naive Bayes
A probabilistic classifier based on Bayes’ theorem, assuming feature independence. Despite its simplicity, it performs exceptionally well in domains like spam filtering and sentiment analysis.
- K‑Means Clustering
An unsupervised learning algorithm that groups similar data points into clusters. Popular for applications like market segmentation and exploratory data analysis.
Gradient Boosting Machines (GBM) / XGBoost
Boosting techniques like XGBoost sequentially build models that correct earlier errors, delivering high accuracy and model flexibility—widely used in competitive predictive modeling tasks.
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Real‑World Applications
These algorithms power a wide range of AI applications, including:
- Predictive analytics & decision support: Demand forecasting, credit risk scoring, customer churn modeling.
- Cybersecurity & threat detection: Anomaly detection, intrusion prediction, malware classification.
- IoT & smart city systems: Traffic forecasting, energy usage modeling, public safety monitoring.
- Healthcare: Medical imaging, disease diagnosis, epidemic spreading predictions.
- E‑commerce & recommendations: Personalized suggestions, inventory forecasts, behavior analysis.
- NLP & text mining: Sentiment analysis, email classification, chatbots.
- Computer vision & pattern analysis: Image and speech recognition, face detection, industrial inspection.
- Agritech & agribusiness: Crop yield prediction, livestock health monitoring, soil and weather modeling.
- Context-aware mobile solutions: Smart notifications, personalization, adaptive user experiences.
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