TildAlice – Tech Blog for Developers
Python, AI, Deep Learning, Data Analysis, Computer Vision, Reinforcement Learning โ tips and tutorials
Latest Posts
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Building a Credit Risk Scoring Model with Machine Learning
Build a credit scoring model with XGBoost: 0.92 AUC-ROC achieved. SHAP explainability, feature engineering, and production deployment guide.
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Advanced Feature Engineering for Financial Time-Series
Engineer 15 financial features for time-series ML: RSI, MACD, Bollinger Bands, lag returns. Code templates for pandas and feature validation.
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Exploratory Data Analysis (EDA) for Stock Price Prediction
Run EDA on stock data: detect non-stationarity, compute volatility metrics, and find correlation traps before modeling. Jupyter notebook included.
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Navigating the Landscape of Financial Datasets on Kaggle
Stop wasting hours hunting Kaggle datasets. These 5 financial datasets include stock data, fraud cases, and credit riskโready to analyze today.
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Physics-Informed Neural Nets for Bearing RUL Prediction
Physics-Informed NNs cut bearing RUL error 30% vs pure deep learning. How Paris' law constraints work when you only have 20 training samples.
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Deploying RUL Models: Optimization and PHM Integration
Deploy RUL models without crashing production: quantization cuts inference from 45ms to 8ms. Real metrics, optimization code, and PHM integration.
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RUL Prediction in Python: Linear Regression to LSTM
Build bearing RUL prediction in Python: linear regression baseline to LSTM with NASA C-MAPSS data. Full code, degradation features, performance.
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PHM Intro: What Is RUL Prediction? Concepts to Data Prep
RUL prediction 101: formulate the problem, engineer degradation features from sensor data, and prepare NASA C-MAPSS dataset for model training.