Follow @TimeToLearnAI for daily Python → AI/ML progress, code examples, and honest reflections. What was your biggest win this week? Let’s celebrate small wins together! 🚀 #100DaysOfML #PythonForDataScience

Day 40/60—Understanding Train & Test Split 📊 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. #PythonForDataScience #MachineLearning #PublicHealth

Day 39 / 60 — Python for Data Science 📊 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 model’s predictions. #MachineLearning #PythonForDataScience #DataAnalytics #HealthcareAnalytics

Day 38 / 60 — Python for Data Science 📊🐍 Today I practiced model validation using `train_test_split`. The model returned an R² score of 0.708, which means it explains about 70% of the relationship between cases and deaths. #DiAnalyst #PythonForDataScience #machinelearning

Day 37 / 60 — Python for Data Science 📊 Today I focused on feature engineering. After retraining the model, the R² score remained around 0.80, showing consistent performance even after introducing a new feature (total cases). #DiAnalyst #PythonForDataScience #DataAnalytics

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Day 36/60—Python for Data Science📊 Built a multiple linear regression model today. Score: 0.80—meaning the model explains about 80% of the changes in deaths. Growth continues. 🚀 #DataScience #PythonForDataScience #DataAnalytics #MachineLearning #LearningInPublic #TechInAfrica

Day 33/60—Python for Data Science 📊 Been working with a real epidemiological dataset, analyzed weekly case trends, visualizing outbreak patterns, and running a hypothesis test to compare suspected vs confirmed cases. #DiAnalyst #DataAnalysis #PythonForDataScience #Healthcare

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Master the core Python tools used in data science and machine learning. → NumPy for numerical computing → Pandas for data analysis → Matplotlib & Seaborn for visualization → Scikit-Learn for machine learning -- #PythonDataScience #PythonForDataScience #MachineLearningPython

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Day 1 was exciting, insightful, and motivating. Ready to dive into Day 2 tomorrow! 🚀 #Day1of100 #DataAnalysis #PythonForDataScience #100DaysOfCode #TechJourney #LearningInPublic

And to check the shape of a variable: ```python print(mat["varname"].shape) ``` ✨ Why This Matters If you're transitioning from MATLAB to Python or working with shared research data, this trick saves time and keeps your workflow smooth. #PythonForDataScience #MATLAB #SciPy

Exciting news! I'm starting my Python journey for data science! Looking forward to learning and growing with the community. #PythonForDataScience #DataScience #LearningInPublic

#Python Tips to be expert😌 #pythonlearning #PythonForDataScience #pythonframeworks #100DaysOfCode #100daysofdevops

#Python Tips to be expert😌 #pythonlearning #PythonForDataScience #pythonframeworks #100DaysOfCode #100daysofdevops

#Python Tips to be expert😌 #pythonlearning #PythonForDataScience #pythonframeworks #100DaysOfCode #100daysofdevops

When I'm down, I write python. I get fine 😊 #python #pythonfordatascience #pythonprogramming

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