How param302/100daysofcode-python works
This repository is a self-paced, day-by-day Python data-science and machine-learning learning log. The principal workflow is a learner studying notebook lessons, progressing from Python data tools through analysis and visualization into model building and competition projects. README-listed topics support the broad curriculum branches; Day-27/pipeline.py is the sampled executable example of preprocessing, fitting, and prediction. Most notebooks were not sampled, so links between their datasets and lessons are not inferred.
Main entry points: pipeline.py Model pipeline · train.csv Training data · test.csv Test data · Numpy-tutorial.ipynb NumPy basics
Components 19
Data fundamentals
- NumPy basics
Day-0/Numpy-tutorial.ipynb - Pandas lessons
Day-1/Pandas_tutorial_part_1.ipynb - Linear algebra
Day-6/Data_analysis_project_2.ipynb
Analysis and visualization
- Data analysis
Day-2/Data_analysis_project_1.ipynb - Matplotlib lessons
Day-7/matplotlib_tutorial_part_1.ipynb - Titanic analysis
Day-10/titanic_data_analysis.ipynb - Seaborn lessons
Day-16/L1 - Hello, Seaborn.ipynb - Visualization projects
Day-18/Data_analysis_project_4.ipynb
Machine learning
- ML foundations
Day-19/L3 - Your_first_machine_learning_model.ipynb - Intermediate ML
Day-20/L1 - Introduction.ipynb - Feature engineering
Day-25/L2 - Mutual_information.ipynb - Model pipeline
Day-27/pipeline.py
Applied projects
- House prediction
Day-19/House_price_prediction-v1.ipynb - Titanic competition
Day-30/Titanic_survival_competition_1.ipynb - Data science project
Day-32/GHW_data_science_project.ipynb - Regression studies
Day-46/readme.md
Other components
- Learner
- Training data
Day-27/data/train.csv - Test data
Day-27/data/test.csv
Connections 17
- Learner → NumPy basics: studies
- Learner → Pandas lessons: studies
- Learner → Linear algebra: studies
- Learner → Data analysis: studies
- Learner → Matplotlib lessons: studies
- Learner → Titanic analysis: studies
- Learner → Seaborn lessons: studies
- Learner → Visualization projects: studies
- Learner → ML foundations: studies
- Learner → Intermediate ML: studies
- Learner → Feature engineering: studies
- Learner → House prediction: studies
- Learner → Titanic competition: studies
- Learner → Data science project: studies
- Learner → Regression studies: studies
- Training data → Model pipeline: reads
- Test data → Model pipeline: reads