create a code #456
Description
Create a simple professional google colab code including: Cell 1 — Data Collection & Understanding
Load dataset
Understand features (data dictionary, types, purpose)
Initial descriptive statistics
Initial visual exploration (distributions, correlations). Cell 2 — Data Preprocessing
Includes:
Handle Missing Values
Outlier Detection & Treatment
Feature Scaling
Categorical Encoding (One-Hot + Dummy Encoding)
Data Type Optimization (use less expensive types)
Filter Out Unused Dimension Values
Define Encoding Hint
Reduce Precision where possible
Include charts + performance metrics. Cell 3 — Feature Engineering
Create new features
Feature Selection / Feature Extraction
Feature Importance visualizations
Professional drawers explaining new features. Cell 4 — Pre-Diabetes Section (Clustering)
Apply clustering (K-Means or similar)
Assign groups: Healthy / Pre-Diabetic / Diabetic
Validate clustering visually
Pie + bar + column charts showing groups. Cell 5 — Train/Test Split & Cross-Validation Setup
Create training and testing sets
Choose cross-validation strategy
Draw workflow diagram for data splitting. Cell 6 - Data Augmentation: check for class imbalance, smote to balance the dataset. MODEL SECTION — 5 MODELS, EACH IN ITS OWN CELL
All 5 models will be trained for:
Healthy
Pre-Diabetes
Diabetes
Each model has its own cell with:
Training
Hyperparameter tuning
Evaluation (classification report, confusion matrix, ROC, feature importance)
Charts + drawers
📌 Cell 6 — Model 1: Random Forest
Train
Hyperparameter tuning
Cross-validation
Feature importance
Evaluation metrics
Visual charts
📌 Cell 7 — Model 2: CatBoost
Train
Hyperparameter tuning
Cross-validation
Feature importance
Evaluation metrics
Visual charts
📌 Cell 8 — Model 3: LightGBM
Train
Hyperparameter tuning
Cross-validation
Feature importance
Evaluation metrics
Visual charts
📌 Cell 9 — Model 4: XGBoost
Train
Hyperparameter tuning
Cross-validation
Feature importance
Evaluation metrics
Visual charts
📌 Cell 10 — Model 5: Neural Networks
Design neural architecture
Apply regularization
Early stopping
Training
Evaluation metrics
Visual charts
OVERFITTING PREVENTION SECTION
📌 Cell 11 — Overfitting Prevention
Includes:
Regularization
Early Stopping
Data Augmentation (if tabular DA used)
Comparison of impact on accuracy
Charts and results included.
MODEL OPTIMIZATION SECTION
📌 Cell 12 — Optimization & Improving Accuracy
Optimizing the data model
Reduce precision
Use less expensive data types
Dimensionality reduction
Encoding optimization
Memory optimization
Processing-time optimization
Charts for performance before vs after optimization
COMPARISON SECTION
📌 Cell 13 — Comparison of All 5 Models
Accuracy comparison table
Bar charts for models
ROC curves comparison
Confusion matrices comparison
Feature importance comparison
Choose the best model
Professional diagrams summarizing the entire pipeline
FINAL SECTION
📌 Cell 14 — Final Validation
Test dataset final results
Full classification report
Performance summary
Final bar/pie/column charts
Export results
Final professional workflow diagram. Each step is placed in its own cell, and every cell must contain:
Bar chart
Pie chart
Column chart
Title
Classification report (Accuracy, Precision, Recall, F1-Score, AUC Score)
Confusion Matrix
ROC Curve
Feature Importance
Professional diagrams/drawers with different colors
One-Hot Encoding (where needed)
Dummy Encoding (where needed)
Understanding the feature set