In this final module, will show machine learning (ML). At a high-level, that has three general categories of ML: supervised, unsupervised, and reinforcement learning. We'll employ a type of supervised learning, simple linear regression, to train a model and use the resulting model (a "best-fit" straight line) to make predictions.
. Build a model . Make predictions . Visualize the model . Publish your insights
Environment Setup and How to Install and Run the Project
datafun-07-applied.ipynb
git clone https://github.com/hanenia/datafun-07-applied/tree/main
source .venv/bin/activate
python3 -m venv .venv
py -m pip install requests py -m pip freeze > requirements.txt Git Ignore Add a useful .gitignore to the root project folder.
1.1 Start the Project Open datafun-07-applied Open a terminal in your root project repository folder and rin geit pull Creat new notebook name :hanna_ml.ipynb add Python cell and import pip intall jupyterlab pip install pandas pip install pyarrow pip install matplotlib pip intasll seaborn pip install scipy pip install stats
.Complete the steps on page 414
Section 1 - Data Acquisition Section 2 - Data Inspection Section 3 - Data Cleaning Section 4 - Descriptive Statistics Section 5 - Build the Model Section 6 - Predict Section 7 - Visualizations
Section 1 - Data Acquisition Section 2 - Data Inspection Section 3 - Data Cleaning Section 4 - Descriptive Statistics Section 5 - Build the Model Section 6 - Predict Section 7 - Visualizations
Complete Official Course Evaluation