Python for Actuaries - Course Notebooks

Welcome to the Python for Actuaries course repository! This repository contains all the Jupyter Notebooks used throughout the course, covering Python fundamentals, data analysis, visualization, and machine learning with a focus on actuarial applications.

Course Structure

The course is divided into five key sections:

1. Python Basics:

   • Variables, control structures, and functions.
   • Working with basic data types such as numbers, strings, and booleans.
   • Understanding conditional statements and loops.

2. Data Structures & File Operations:

   • Lists, tuples, sets, and dictionaries.
   • Reading and writing files (e.g., .txt, .csv).
   • Practical exercises on manipulating files and data structures.

3. Data Analysis with Pandas:

   • Introduction to Pandas: Series and DataFrames.
   • Loading, filtering, and sorting data.
   • Data cleaning and manipulation techniques.

4. Data Visualization with Matplotlib:

   • Creating basic plots: line, bar, and pie charts.
   • Advanced visualizations such as scatterplots and histograms.
   • Practical examples using insurance-related data.

5. Introduction to Machine Learning:

   • Basics of machine learning with scikit-learn.
   • Building simple classification and regression models.
   • Applying machine learning models to actuarial datasets.

Completed Notebooks

This repository has a second branch, with the completed notebooks, e.g. all exercises solved and all missing code added. You can find it under: Completed Branch

Getting Started

To get started with the notebooks, you can either clone this repository locally or use one of the following platforms for running the notebooks in your browser without any local installation:

Option 1: Run with Google Colab

1. Open Google Colab.
2. Click on the GitHub tab.
3. Paste the URL of this repository and open any notebook to start working.

Option 2: Run with Kaggle

1. Go to Kaggle.
2. Navigate to "Notebooks" and create a new notebook.
3. Under "File," select "Import from GitHub" and paste the repository URL.

Option 3: Run with GitHub Codespaces

1. Click the green "Code" button in this repository.
2. Select "Open with Codespaces" to launch the environment directly in your browser.

Option 4: Local Installation

If you prefer to run the notebooks locally, follow these steps:

1. Clone this repository to your local machine:

   git clone https://github.com/your-username/your-repo-name.git

2. Install Python and Jupyter.
3. Install the necessary packages:

   pip install matplotlib numpy pandas scikit-learn seaborn

4. Run Jupyter notebooks:

   jupyter notebook

5. Open the desired .ipynb file from the course materials.

License

This project is licensed under the GNU General Public License v3.0. You can find the full license text in the LICENSE file.