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This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.
Classification Analysis with supervised algorithms KNN, RandomForest and SVM. Use of PCA and K-means to recognize the importance of features.
Analysis classification rules of decision tree
Customer Churn Predictions Using Classification Analysis
The repository for standard machine learning model developments and related tasks
During my Data Science internship at Encryptix, I tackled three distinct tasks: predicting Titanic survival rates, classifying Iris flowers, and detecting credit card fraud. Each project provided valuable hands-on experience in diverse aspects of data analysis and machine learning.
A solution that will predict whether a customer will churn
Credit Card Fraudulent Detection