What kind of AI projects to include in your Data Science portfolio?
Because AI is the hottest flex in 2025 😉
AI engineering has become an integral part of the Data Science job. Every regular Data Scientists is expected to know AI, since it’s become the default way we solve problems.
So in order to land your next Data Science job, you must include some AI projects in your portfolio.
For Data Science portfolio projects, I like to think about it in 4 broad categories: (1) core ML projects, (2) deep learning, (3) deployable applications, and (4) business-driven AI projects. Do steps 1–3 in order so to allow your skills to develop iteratively — but Step 4 (business-driven AI projects) is fair game.. you can start building those projects at anytime.
1. Core ML Projects (Start Here)
This is your foundation. Your goal: prove you can take a messy dataset, turn it into insights, and build a simple but solid model.
There are no hard-and-fast rules, but simple ML models generally mean Linear Regression, Logistic Regression, Decision Trees, KNN and K-means Clustering.
Components to include in your core ML project
Data Cleaning → Handle missing values, drop duplicates etc
Feature Engineering → Convert categorical variables to dummies, create new variables that make business sense
Exploratory Data Analysis (EDA) → Visualize key patterns, look for correlations (remove highly-correlated variables), and check class balance
Modeling → Train at least two models (e.g., Linear/Logistic Regression + a Tree-based model) and compare performance
Evaluation → Use appropriate metrics for each model and show results visually explaining what they mean
Interpretation → Highlight which features matter most and explain why the results make sense
Documentation → Write a clear README or report explaining the problem, approach, results, and next steps
2. Deep Learning Projects
Once you’ve mastered the basics, it’s time to tackle unstructured data — images, text, and sequences. The goal here is to show you can handle larger datasets, build neural networks, and explain what’s happening under the hood.
Don’t get intimidated by the phrase Deep Learning. Deep learning projects don’t have to be overly complex — start small, that’s totally fine!
What to include in your deep learning project
Data Preprocessing → Normalize inputs, tokenize text, and set up train/test splits carefully
Model Architecture → Start with simple architectures (e.g., feedforward network, CNN, LSTM) before moving to pre-trained models or transformers
Training → Use a validation set, tune hyperparameters (batch size, learning rate), and avoid overfitting with dropout/regularization
Evaluation → Track accuracy, loss curves, or other task-specific metrics; show how the model improved over time
Interpretation → Visualize activations, use SHAP/LIME for feature importance, or attention maps for interpretability
Documentation → As before, make sure you’re documenting your process
3. Deployable ML
This is where you separate yourself from the pack.. and show that you can actually do the job of a Data Scientist.
Your goal: prove you can turn a model into a production. It can be small (since it’s a portfolio project after all) — a small product, an API, or a simple app.
Ideas on what you want to show off with this project:
Pipeline Setup → Automate data cleaning, feature engineering, and model training so it’s reproducible
Model Serving → Wrap your trained model in a simple REST API
Frontend or Interface → Build a minimal interface — a Streamlit dashboard is a good start
Deployment → Host it somewhere free or cheap (I like Vercel for this) so others can interact with it
Monitoring → If possible, log predictions, monitor model performance over time, and set up alerts for drift (even simple ones!)
Documentation → You know the drill by now 😉
4. Business-Driven AI Projects (Do Anytime)
Unlike the first three steps, you can do this one anytime — even while you’re working on Steps 1–3. Because this section shows off a different set of skills: using AI to augment intelligence and build automations.
Pick a project where you can connect data science to real-world decisions and outcomes.
Ideas to get your brain juices flowing…
AI Data Assistant → Build a chatbot that answers questions about a dataset (use an LLM + retrieval)
Automated Data Pipeline → Use an LLM to clean, label, or summarize data as it flows through a pipeline
AI-Powered Analytics → Generate automated weekly reports or SQL queries with GPT and send them via Slack/email
Smart Recommendation Tool → Build a content or product recommender powered by embeddings and similarity search
AI Workflow Automation → Combine GPT with tools like n8n or Zapier to automate repetitive tasks (e.g., categorize support tickets, summarize feedback)
Don’t forget to make sure there’s clear business relevance, including a clear problem statement, trade-offs, results and (of course) documentation.
I hope you found this guide helpful. If any of this feels intimidating, remember, start small. Define your MVP (minimum viable product) or SLC (simple, loved and complete) product — that’s a great place to start!