Dimensionality Reduction
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
Survival Analysis
Analyze time-to-event data, calculate survival probabilities, and compare groups using Kaplan-Meier and Cox proportional hazards models
Time Series Analysis
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Clustering Analysis
Identify groups and patterns in data using k-means, hierarchical clustering, and DBSCAN for cluster discovery, customer segmentation, and unsupervised learning
Statistical Hypothesis Testing
Conduct statistical tests including t-tests, chi-square, ANOVA, and p-value analysis for statistical significance, hypothesis validation, and A/B testing
Funnel Analysis
Analyze user conversion funnels, identify drop-off points, and optimize conversion rates for conversion optimization and user flow analysis
Feature Engineering
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
ML Model Explanation
Interpret machine learning models using SHAP, LIME, feature importance, partial dependence, and attention visualization for explainability
log-analysis
Analyze application and system logs to identify errors, patterns, and root causes. Use log aggregation tools and structured logging for effective debugging.
ML Model Training
Build and train machine learning models using scikit-learn, PyTorch, and TensorFlow for classification, regression, and clustering tasks
Model Hyperparameter Tuning
Optimize hyperparameters using grid search, random search, Bayesian optimization, and automated ML frameworks like Optuna and Hyperopt
Regression Modeling
Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification
Cohort Analysis
Track and analyze user cohorts over time, calculate retention rates, and identify behavioral patterns for customer lifecycle and retention analysis
Correlation Analysis
Measure relationships between variables using correlation coefficients, correlation matrices, and association tests for correlation measurement, relationship analysis, and multicollinearity detection
Exploratory Data Analysis
Discover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, data profiling, and initial insights
Recommendation System
Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement
Network Analysis
Analyze network structures, identify communities, measure centrality, and visualize relationships for social networks and organizational structures
Recommendation Engine
Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches
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