5A. Recursive Forecasting with SQLite + Feature Engineering + Linear Regression Pipeline

#timeseries #machinelearning #pipeline #sqlite3 #weatherforecasting #regressionanalysis #linearregression Welcome to Learning Iterations, where every project is an opportunity to learn, build, evaluate, and improve. In this video, I’ll present my GitHub project, Chennai Weather Forecasting: A Regression Problem. Part 1: Project Introduction, API Request call to Open Meteo then the Data Ingestion.    • Weather Forecasting Project - A Regression...   Part 2: Feature Engineering - Lag Features, Cyclical Encoding, Rolling Statisics    • 2. Feature Engineering - Weather Forecasti...   Part 3: Train Test Split and Time Series Split    • 3. TrainTestSplit TimeSeriesSplit & Matplo...   Part 4: Standardization and HeatMap - Weather Forecasting Project - A Regression Problem    • 4 Standardization and HeatMap - Weather Fo...   Part 5A - Focuses on creating SQLite DB to store the raw data, Create the Linear Regression Model by employing a pipeline for preprocessing. Entire preprocessing + model saved together in one .pkl file. As a continuation of Linear Regression maximum temperature prediction, Part 5B - Will focus on Model evaluation Part 5C - Will focus on Model Visualization Part 5D - Will focus on Forecasting Data Loading, Save and Load Models, Evaluation metrics, Visualization and making inference will be reused for every other desired models in our list. And every model will have their model specifics. utils/helpers.py makes this reusability easier.