Скачать или смотреть Applied Machine Learning | Session 22 | Time Series Forecasting & Model Evaluation

Join our class for a practical walkthrough of common ML workflows and advanced techniques:

📅 Loading and exploring time-series and cross-sectional datasets

🔧 Feature engineering and data preprocessing for real-world data

📈 Building and evaluating time series forecast models (like ARIMA or Prophet)

🔍 Comparing performance using train/test splits, cross-validation, and visual evaluation

🧠 Tips for detecting overfitting and ensuring robust predictions

🛠️ Tools & Libraries Used:
Python • pandas • NumPy • scikit-learn • statsmodels/prophet • matplotlib • seaborn

🔗 Access the notebook here:
https://github.com/GenEd-Tech/Applied...

Ideal for anyone ready to level up—from data prep to building reliable forecasting models with clear, hands-on Python demonstrations.

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