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Messy data is the tax every analyst pays. Might as well pay it in 60 seconds. ๐งน
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Follow @TimeToLearnAI for daily Python โ AI/ML progress, code examples, and honest reflections.
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Day 40/60โUnderstanding Train & Test Split ๐
Today I worked on splitting my dataset into training and testing sets, and the difference between the two scores reminded me why proper validation matters in data science.
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Day 39 / 60 โ Python for Data Science ๐
This chart compares the actual number of deaths with what the model predicted. The closer the points fall along an upward line, the better the modelโs predictions.
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Feature Engineering Best Practices A Guide for Data Scientists
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Day 38 / 60 โ Python for Data Science ๐๐
Today I practiced model validation using `train_test_split`.
The model returned an Rยฒ score of 0.708, which means it explains about 70% of the relationship between cases and deaths.
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If you're also learning, building, or working in tech, Iโd be glad to connect.
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Day 37 / 60 โ Python for Data Science ๐
Today I focused on feature engineering.
After retraining the model, the Rยฒ score remained around 0.80, showing consistent performance even after introducing a new feature (total cases).
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Mastering Python is the first step toward a successful Data Science career
Follow these 5 smart tips to build strong logic, write efficient code, and work on real-world projects.
Consistency + practice = growth
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Day 36/60โPython for Data Science๐
Built a multiple linear regression model today.
Score: 0.80โmeaning the model explains about 80% of the changes in deaths.
Growth continues. ๐
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Python isnโt just a language - itโs a career toolkit ๐
From data analysis and machine learning to web apps and game development, Python powers it all.
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