sadjad6

data_quality_scorer

Score overall data quality

data_sampler

Create representative data samples

schema_validator

Validate data against expected schema

sql_loader

Load data from SQL databases

type_converter

Convert column data types

clustering_analysis

Group data into natural clusters

cohort_analysis

Analyze behavior across user cohorts

dimensionality_reduction

Reduce feature space using PCA or t-SNE

hypothesis_testing

Formulate and test research hypotheses

precision_recall

Compute precision and recall metrics

regression_metrics

Compute regression evaluation metrics

config_validator

Validate configuration files

data_versioner

Version control datasets

dependency_manager

Manage project dependencies

docker_builder

Build Docker images for deployment

env_setup

Set up Python environments

svm_trainer

Train support vector machine models

target_encoder

Encode categorical targets for ML

executive_summary

Write executive summaries from analysis

paper_searcher

Search for relevant academic papers

heatmap

Generate heatmap visualizations

interactive_plot

Generate interactive Plotly visualizations

scatter_plot

Generate scatter plot visualizations

cross_validator

Perform k-fold cross-validation

abstract_writer

Generate research abstracts

accuracy_evaluator

Compute classification accuracy metrics

anomaly_detection

Detect anomalous observations in data

auto_ml

Automated model selection and tuning

bar_chart

Generate bar chart visualizations

box_plot

Generate box plot visualizations

cache_manager

Manage caching of computed results

citation_manager

Manage and format research citations

confusion_matrix

Generate confusion matrices

csv_loader

Load CSV files into DataFrames

dashboard_generator

Generate multi-panel dashboards

data_cleaning

Clean and standardize raw data

data_splitter

Split data into train/test/validation sets

data_storyteller

Create compelling data narratives

data_summarizer

Generate natural-language data summaries

eda_generator

Generate comprehensive exploratory data analysis

experiment_comparator

Compare multiple experiments side-by-side

fairness_checker

Check model fairness across groups

feature_engineer

Create new features from existing data

finding_validator

Validate research findings

health_checker

Check system and service health

histogram

Generate histogram visualizations

image_feature_extractor

Extract features from images for ML

insight_extractor

Extract key insights from analysis results

json_loader

Load JSON files into DataFrames

knn_trainer

Train K-nearest neighbors models

line_chart

Generate line chart visualizations

literature_reviewer

Conduct systematic literature reviews

log_aggregator

Aggregate and search through logs

logistic_regression

Train logistic regression classifiers

methodology_writer

Write methodology sections for reports

model_explainer

Generate SHAP-based model explanations

neural_network

Train neural network models

outlier_detector

Detect outliers using statistical methods

parquet_loader

Load Parquet files into DataFrames

performance_profiler

Profile model inference performance

pie_chart

Generate pie chart visualizations

random_forest

Train random forest ensemble models

recommendation_engine

Generate actionable recommendations

report_writer

Generate structured research reports

research_planner

Plan research investigations

roc_auc

Compute ROC curves and AUC scores

seasonality_detection

Detect seasonal patterns in time series

statistical_testing

Perform hypothesis tests (t-test, chi-square, etc.)

summary_generator

Generate executive summaries

text_classifier

Classify text documents into categories

trend_analysis

Detect trends in temporal or sequential data

xgboost_trainer

Train XGBoost models

data_deduplicator

Find and remove duplicate records

gradient_boosting

Train gradient boosting models

linear_regression

Train linear regression models

job_scheduler

Schedule and manage background jobs

ensemble_builder

Build model ensembles (voting, stacking)

missing_value_handler

Detect and handle missing values

excel_loader

Load Excel files into DataFrames

correlation_analysis

Compute and visualize correlation matrices

feature_importance

Rank features by predictive importance

time_series_decomposition

Decompose time series into trend, seasonality, residual

hyperparameter_tuner

Optimize model hyperparameters

model_comparator

Compare models side-by-side on metrics

model_selector

Compare and select the best model

prediction_service

Generate predictions from trained models

knowledge_synthesizer

Synthesize knowledge from multiple sources

results_formatter

Format analysis results for presentation

sentiment_analyzer

Analyze sentiment of text data

report_chart

Generate publication-quality charts

dataset_profiler

Generate comprehensive dataset profiles

resource_monitor

Monitor CPU, memory, and disk usage

lightgbm_trainer

Train LightGBM models

model_serializer

Save and load trained models

pipeline_builder

Build sklearn preprocessing pipelines

model_drift_detector

Detect data or concept drift

cross_val_scorer

Score models using cross-validation

ab_test_analyzer

Analyze results of A/B experiments

distribution_analysis

Analyze the statistical distribution of features