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Join me in this hands-on data science video as I walk through the solution to a forecasting competition. Learn how to approach data science projects and competitions with practical tips and techniques to succeed in the field.
📖CHAPTERS
00:00 Introduction
01:29 Competition Context
04:18 The Starter Notebook
34:06 Data Preparation
57:11 Isolated Series Approach
01:25:20 Analysis and Feature Engineering
02:02:34 Clustering Series Approach
02:18:37 My Final Solution
02:44:30 The Winner's Solution
02:56:45 Outro and Thanks!
🔗LINKS AND CHAPTER REFERENCES
My Video on The Competition Journey: • Can I WIN a Data Science Competition?🏆
My Solution Repo: github.com/trentpark8800/zindi-hydropower-challeng…
Competition Context:
Competition Link: zindi.africa/competitions/ibm-skillsbuild-hydropow…
The Starter Notebook:
ARIMA Explanation: • ARIMA Model Explained | Time Series F...
Time Series vs ML: • Why Are Time Series Special? : Time S...
Stationarity: • Time Series Talk : Stationarity
Data Preparation
DuckDB: duckdb.org/
DuckDB Tutorial: realpython.com/python-duckdb/
Isolated Series Approach
Darts Quikstart: unit8co.github.io/darts/quickstart/00-quickstart.h…
Clustering Series Approach
My Video on KMeans Clustering: • Hands On Data Science Project: Unders...
tsfresh: tsfresh.readthedocs.io/en/latest/
My Final Solution
My Video on Linear Regression: • Linear Regression is More Important T...
My Video on Optuna: • The Best Way to Tune Hyperparameters?...
The Winner's Solution
Winner's Repo: github.com/bakarys01/hydropower-kalam-winner
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