What are some data visualization projects relevant for an ML job?

Data visualization is crucial in machine learning (ML) for conveying insights from complex datasets. Relevant projects include creating interactive dashboards to explore model performance metrics, such as accuracy and precision-recall curves. Visualizing feature importance helps in understanding which variables contribute most to model predictions. Time series forecasting projects benefit from graphical representations of historical trends and forecasted values. Additionally, creating heatmaps to analyze correlations between variables aids in feature selection and model optimization. Such projects showcase proficiency in both ML techniques and effective communication of results through visualization.

Some relevant data visualization projects for an ML job include creating interactive dashboards to explore model predictions and performance metrics. Visualizing feature distributions and relationships can help understand data patterns and inform feature engineering decisions. Plotting decision boundaries of machine learning models can provide insights into their behavior and performance across different classes. Additionally, visualizing model interpretability techniques, such as SHAP values or LIME explanations, can enhance understanding and trust in model predictions.

In my career as a machine learning engineer, emphasizing the importance of data visualization has been a cornerstone for effectively communicating complex models and findings to both technical and non-technical stakeholders. Creating an interactive dashboard to visualize the exploratory data analysis (EDA) of a dataset we were using to train a predictive model. This dashboard provided insights into distributions, correlations, and patterns within the data, making it easier for the team to make informed decisions about feature engineering and model selection. These projects, especially when crafted with tools like Tableau or Power BI showcase one's technical ability to create and interpret complex machine learning models.

For an ML job, data visualization projects that demonstrate data storytelling skills are highly relevant. Projects could include: 1. Exploratory Data Analysis (EDA): Visualizing distributions, correlations, and patterns in datasets to uncover insights and inform model development. 2. Model Performance Dashboards: Creating interactive dashboards to present model accuracy, precision, recall, and other metrics over time or across parameters. 3. Feature Importance Visualizations: Highlighting key features that impact model predictions using bar charts, heatmaps, or tree-based visualizations. 4. Predictive Results Visualization: Comparing actual vs. predicted outcomes through plots to narrate the effectiveness of models.

Effectively communicating the intricacies of models is crucial. I've engaged in several projects that centered around ML model visualization, which have been instrumental in understanding and improving model performance. A project revolved around using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for creating feature importance plots and explaining individual predictions. Visualizations helped demystify the "black box" nature of complex models, such as random forests and neural networks, by highlighting which features were most influential in the model's predictions. This also fostered trust among stakeholders by providing a clearer understanding of how models arrived at their conclusions.

Participating in these challenges, hosted on platforms like Kaggle, Makeover Monday, or Viz for Social Good, offer a wide array of datasets, themes, and problems that push the boundaries of conventional data visualization techniques. One challenge was related to public health, focusing on the spread of a contagious disease across different regions. The goal creating an interactive dashboard that could help public health officials make informed decisions. This project tested my ability to synthesize complex datasets into a coherent and interactive visualization, using tools such Tableau and Google Data Studio. The dashboard included various filters and sliders, allowing users to explore the data across different dimensions and time frames.

For my portfolio, I chose GitHub to host the code and visualizations, ensuring transparency and ease of access for anyone interested in the technical details behind the projects. I utilized Medium to publish articles that accompany each project, providing context, explaining the methodology, and discussing the insights derived from the data. This dual-platform approach allowed me to cater to both technical audiences interested in the code and non-technical audiences more focused on the story and insights. An engaging introduction at the beginning of the portfolio provides a snapshot of my background, and my approach to data visualization. A personal touch helps set the tone and connect with the audience on a more personal level.

I work in the drug discovery domain; exploring the vast chemical space of potential drug-like compounds is a crucial task in drug discovery. With the estimated number of drug-like chemicals being more than 10^63, visualizing this space becomes essential for guiding compound selection and prioritization for experimental screening. This can also be a great ML data visualization project for portfolio building especially in the specialized domains like biopharma and crop sciences.