👋 Hi there, it’s Lina …

… and this is where I write about data science

Illustration by Icons 8 from Ouch!

About me

I’m a freelance data scientist based in France 🇫🇷🥖 who loves diving into the nitty-gritty of data science projects.

Through my blog posts, I explore various data science topics, providing simple yet detailed explanations. I bring these concepts to life by applying them to real-world use cases, describing the results, and sharing the code on Github.

💬 If you’re interested in collaborating or have any data science project ideas you’d like to discuss, feel free to contact me on Linkedin. I’m always looking for exciting opportunities to put my skills to work.

👉 If you’re looking to level up your data science skills and stay on top of the latest trends, subscribe to my Medium page here for only $5/month (you’ll also get access to all the articles published on Medium).

Why Do I Write Articles?

Writing technical blog posts that include real-world use cases can be time-consuming. Plus, the rapid progress of AI can make the effort seem futile. So why write in this case?

I write technical blog posts for several reasons.

1. Firstly, I’m an avid data science enthusiast. I always feel the need to go beyond passive reading and get my hands dirty by implementing new concepts and techniques.
2. But it’s not just about learning for myself. Through my work as a consultant with many companies, I noticed a gap between research and practice. While many research papers showcase great models and approaches, there is often a lack of implementation and real-world examples. I aim to bridge this gap by taking these cutting-edge concepts and applying them to real-world use cases, then sharing my results and code on Github for others to use.
3. Lastly, I enjoy sharing my knowledge with others. Through my blog posts, I hope to inspire others to explore data science further and empower them to implement these techniques in their own work.

Read Me

Uplift Modeling

Beyond Predictions: Uplift Modeling & the Science of Influence (Part I)

Deep Learning & Recommender Systems

Building Powerful Recommender Systems with Deep Learning

Survival Analysis

Survival Analysis: Predict Time-To-Event With Machine Learning (Part I)

Survival Analysis: Leveraging Deep Learning for Time-to-Event Forecasting

Graph Theory

Graph Neural Networks: Merging Deep Learning With Graphs (Part I)

Graph Neural Networks: Link Prediction (Part II)

Graph Neural Networks: Graph Classification (Part III)

Fraud Detection with Graph Analytics

Deep Learning & Time Series

Deep Learning for Time Series Forecasting: Is It Worth It? (Part I)

Can Deep Learning Change the Game for Time Series Forecasting? (Part II)

Machine Learning

Evolutionary Decision Trees: When Machine Learning draws its Inspiration from Biology

How can Machine Learning algorithms include better Causality?

Understanding Happiness Dynamics with Data (Part 1)

Understanding Happiness Dynamics with Machine Learning (Part 2)

Reinforcement Learning

Deep Q Network: Combining Deep & Reinforcement Learning

AI-Driven Tax Policy: What It Would Look Like?

Operational Research

A Game Theoretical Approach for Adversarial Machine Learning