In this liveProject, you’ll explore a pre-made dataset of meteorological records. You’ll learn the shape, size and type of data at hand, and discover factors that affect rainfall. You use scikit-learn and logistics regression to make initial predictions about future rainfall, evaluate their accuracy, and visualize emerging patterns using Seaborn and Matplotlib.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for Python data scientists who want to expand their capabilities in preparing data for machine learning. To begin this liveProject you will need to be familiar with:
- Basic Python
- Basic pandas
- Basic NumPy
- Basic Matplotlib
- Basic seaborn
- Basic scikit-learn
- Basic Jupyter Notebook
- Basics of machine learning
- Basics of exploratory analysis
you will learn
In this liveProject, you’ll learn skills for cleaning and exploring data in preparation for training a machine learning model.
- Manipulating data and exploratory analysis
- Imputing missing values, engineering outliers, and one-hot encoding
- Evaluating predictions, accuracy, and other metrics
- Visualizing patterns with correlation pair plots and heatmaps