By the end of this project learners will be able to perform data ingestion using Pandas and Numpy, preprocess and visualize the data using Matplotlib and Seaborn. Learners will also build a binary classification model using Scikit-Learn. Additionally, learners will be introduced to other data science concepts and techniques such as standardization and up- and down-sampling of biased data. It is important to manipulate your data such that it can then be used to build a predictive model.

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In this Guided Project, you will:

- Ingest a dataset into a Jupyter Notebook environment using Pandas and create visualizations using the Matplotlib and Seaborn libraries.

- Preprocess data through standardization, resampling using Scikit-Learn.

- Build, train and save an accurate binary classification model.

Learn step-by-step

- Data ingestion and visualization

- Data preprocessing (standardization and scaling)

- Data preparation and Principal Component Analysis intro

- Build and train a binary classification model

- Versioning

Binary Classification