# Hello, hello!

Hello, and welcome!

Here’s a list of my articles, grouped into broad topics. So grab your favourite drink, get comfortable and dive in.

# Helpful hints and tips

Articles about useful tips and tricks, from data analysis to making presentation-ready charts and exhibits.

- Create presentation-ready DataFrames: Comprehensive guide to formatting pandas DataFrames | Towards Data Science
- How to create stunning visualisations: Create presentation-ready line and bar charts in matplotlib | Towards Data Science
- Essential tools for your data manipulation tool box: Turbocharge your data manipulation skills | Towards Data Science
- Learn how to exploit Pandas’
`agg`

function:Speed up data analysis using pandas agg | Towards Data Science - Think Excel is the only place to find Pivot Tables? Think again: A how-to guide to pivot tables in pandas | Towards Data Science

# Data science

Articles demonstrating various data science concepts, with real world data and applications.

- Reinsurance Pricing, the Data Science Way | by Bradley Stephen Shaw | Towards Data Science is a high-level explanation of alternative approachs to reinsurance pricing.
- Gaps in your knowledge of imputation techniques? Brdige them with Filling in the Gaps: Imputation 3 Ways | by Bradley Stephen Shaw | Towards Data Science.
- Seeing Numbers: Bayesian Optimisation of a LightGBM Model | by Bradley Stephen Shaw | Towards Data Science explores the tuning of a LightGBM model using Bayesian optimisation.
- Predicting Pulsar Stars: An Imbalanced Classification Task Comparing Bootstrap Resampling to SMOTE | by Bradley Stephen Shaw | Towards Data Science covers a typical data science workflow, summarising and comparing two popular approaches to dealing with class imbalance.
- The ins-and-outs of feature engineering for tabular data: Let’s Do: Feature Engineering. A brief exhibition of the power of… | by Bradley Stephen Shaw | Towards Data Science
- Let’s Do: Spatial Clustering with DBSCAN | by Bradley Stephen Shaw | Towards Data Science. A demonstration of a clustering tas using real-world data. Includes Bayesian optimisation of the DBSCAN clustering algorithm.
- Things all getting a little too serious for your liking? Take a break as we ask the question: Can data science find Bigfoot? | Towards Data Science

# Neural networks

A documented journey of a dummy learning about neural networks, culminating in the building of a custom neural network to predict insurance claim frequency.

- Let’s Learn: Neural Nets #1. A step-by-step chronicle of me learning… | by Bradley Stephen Shaw | Medium
- Let’s Learn: Neural Nets #2 — Nodes and Neurons | Towards AI
- Let’s Learn: Neural Nets #3 — Activation Functions | Towards AI
- Let’s Learn: Neural Nets #4 — Weights and Biases | Python in Plain English
- Let’s Learn: Neural Nets #5 — Layers | by Bradley Stephen Shaw | Medium
- Let’s Learn: Neural Nets #6 — Backpropagation (or how Neural Networks learn) | by Bradley Stephen Shaw | Medium
- A neural network to predict insurance claim frequency | Towards Data Science: putting lessons 1–7 to the test, as I build a neural network to predict how often a customer will make an insurance claim.

# Time series analysis and forecasting

Articles relating to all things time series.

- A comprehensive guide to time series decomposition | Towards AI breaks down how a time series can be… well, broken down.
- Familiar with “normal” cross-validation? Not quite sure if it’s appropriate for a time series problem? Let’s Do: Time Series Cross-Validation | Medium summarises why it isn’t and what you should do instead.
- False Prophet: Feature Engineering for Time Series | Towards Data Science. Taking inspiration from Meta’s Prophet, I explore how feature engineering can help us build a powerful time series model.
- False Prophet: a Time Series Regression Model | Towards Data Science combines inventive time series features with a practical model choice to build a powerful time series model. Inspired by Meta’s Prophet package, we explore how sometimes simple is better.
- Built a model following Prophet’s approach? Interested in how it stacks up to the real deal? I was — Comparing a Regression Model to Meta’s Prophet | Towards Data Science (medium.com)
- Sometimes it takes more than one tool to get the job done — see how that plays out in a time series context with Time Series: Mixed Model Time Series Regression | Towards Data Science
- Interested in how you might incorporate a new data source into a time series regression model? I go into some detail in External Data in Time Series Regression | Towards Data Science, using real-world weather in the prediction of road traffic incidents.
- How do you know that your time series model captures all of the signal in your target? Take a look at A comprehensive guide to time series residuals | Python in Plain English (medium.com), where I cover how looking at leftovers can help you build better models.

Happy reading!