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STASTUCStatistical & Machine Learning Workspace
STATISTICAL & MACHINE LEARNING

Train and evaluate models directly from your dataset.

Load a tabular dataset, configure preprocessing and validation, train models, evaluate results, and export reproducible reports.

Ready

Upload dataset

CSV, TSV, TXT, DAT, XLS, XLSX, or XLSM. The first row must contain column names.

Machine-learning task

Choose how the dataset will be modeled.

Feature columns

Numeric columns are selected automatically. You can exclude columns before training.

Numeric feature policy

This version trains on numeric predictors. Categorical target columns are supported for classification; non-numeric predictor columns are excluded to avoid hidden encoding and data leakage.

Missing values

Choose the rule used inside every training fold.

Transform & scaler

Transformation is applied first, then scaling is fitted using training data only.

Dataset
Mean imputation
No transform
StandardScaler
ML

Principal Component Analysis (PCA)

Unsupervised projection of the selected feature matrix before machine learning.

PCA has not been calculated.

Linear Discriminant Analysis (LDA)

Supervised projection for classification. Class labels are used only in this LDA analysis.

LDA has not been calculated.
Analysis before machine learning

PCA and LDA are diagnostic visualizations. Machine-learning training continues to use the selected original features unless you explicitly change the feature selection.

Model

Select an algorithm and its main parameters.

Validation

The test set stays independent from cross-validation.

Preprocessing parameters are fitted separately inside each training fold to reduce leakage.

Hyperparameter Tuning

Optional model selection using cross-validation on the training partition only.

Ready to train

Review the pipeline then train the model. Hyperparameter tuning is optional.

No model trained yet.Complete steps 1–4 and train a model.
MODEL RUNTIME

Load Model & Prediksi

Muat model STASTUC yang telah disimpan, masukkan data baru, lalu jalankan prediksi tanpa melakukan training ulang.

1. Load Model

Gunakan file model JSON yang diekspor dari STASTUC.

STASTUC Pilih model STASTUC JSON model • drag & drop atau pilih file
Belum ada model yang dimuat.

2. Input Data

Masukkan satu sampel secara manual atau unggah dataset untuk prediksi batch.

Load model terlebih dahulu. Kolom input akan dibuat otomatis sesuai fitur model.
Dataset prediksiCSV, TSV, TXT, DAT, XLS, XLSX, atau XLSM. Nama kolom fitur harus sesuai model.

3. Hasil Prediksi

Hasil menggunakan preprocessing dan urutan fitur yang tersimpan pada model.

Hasil prediksi akan muncul di sini.