머신 러닝 알고리즘이 비지니스에 활용되는 사례의 검토를 통해, 좀 더 거시적인 측면에서 머신러닝을 이해해 보자.
최신의 신경망( Neural Network )이외의 머신러닝 알고리즘도 다양한 분야에 활용될 수 있다.
머신러닝에 알고리즘 자체에 대한 것은 논하지 않는다.( 전공자라면, 이름과 특성은 익히 또는 종종 들었을 것이다.)
1. Linear Regression
- Sales Forecasting: Predicting future sales based on historical data.
- Risk Management: Estimating financial risk by analyzing market trends.
- Pricing Strategies: Determining optimal pricing based on various factors such as demand and competition.
2. Logistic Regression
- Prediction of Churn: Customers likely to leave a service ( leave or stay )
- Fraud Detection: Fraudulent vs. Legitimate transactions.
- Effectiveness of Marketing Campaign: Likelihood of customer response to the campaign
3. Decision Tree
- Customer Segmentation: Division of customers into several different groups based on their behavior.
- C redit Scoring: Deciding the creditworthiness of loan applications.
- Supply Chain Optimization: Making decisions on inventory levels and logistics.
4. Random Forest
- Product Recommendation—Prediction of products a customer is likely to buy
- Stock Market Prediction—Prediction of stock price at a given time in the future, given the history
- Quality Control—Detection of defects in the process of manufacturing
5. Supoort Vector Machines(SVM)
- Text Classification: Categorizing emails as spam or not spam.
- Image Recognition: Identifying objects in images.
- Customer Sentiment Analysis: Classifying customer reviews as positive or negative.
6. K-Nearest Neighbors(KNN)
- Customer Behavior Analysis: Grouping similar customers for targeted marketing.
- Recommendation Systems: Suggesting products based on similar user preferences.
- Anomaly Detection: Identifying outliers in financial transactions.
7. K-Means Clustering
- M arket Segmentation: Grouping customers based on purchasing behavior.
- Image Compression: Reducing the number of colors in an image.
- Document Clustering: Organizing documents into topic-based clusters.
8. Pricipal Component Analysis(PCA)
- Data Visualization: Simplifying complex datasets for visualization.
- Feature Extraction: Identifying key features for model building.
- Noise Reduction: Removing noise from data for better model performance.
9. Neural Networks
- Image Recognition: Identifying objects and faces in images.
- Natural Language Processing(NLP): Understanding and generating human language.
- Fraud Detection: Identifying fraudulent activities in financial transactions.
10. Ensemble Learning ( such as Bagging, Boosting, Stacking )
- Fraud Detection: Combining different models to detect fraudulent transactions more accurately.
- Customer Segmentation: Improving segmentation accuracy by combining various algorithms.
- Credit Scoring: Enhancing the precision of credit risk assessments.
원본 자료는
https://www.linkedin.com/pulse/essential-machine-learning-algorithms-business-analytics-dictc/
Essential Machine Learning Algorithms in Business Analytics
In the rapidly evolving landscape of business analytics, machine learning algorithms have become indispensable tools for extracting insights, making predictions, and automating decision-making processes. Businesses across various sectors leverage these alg
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