15 Data Scientist Project Ideas 2026-27

Welcome! This article is written for students who want to learn data science by doing real projects. Projects are the best way to practice, learn new tools, and build a portfolio you can show to teachers or future employers.

Below you will find an easy-to-follow introduction, 15 detailed Data Scientist project ideas, and practical guidance on how to choose, build, and present your projects.

Each project includes the goal, tools, where to find data, step-by-step actions, what you will learn, and possible extensions.

Must Read: 279+ Chatbot Project Ideas for Students | Tips, Examples & Benefits

Why do projects matter for a student learning data science?

Projects help you move from theory to real skills. Reading about algorithms is helpful, but solving a real problem teaches you how to handle messy data, choose the right model, and explain results clearly. Projects also:

• Give you experience with tools like Python, pandas, scikit-learn, and visualization libraries.
• Help you develop problem-solving and communication skills.
• Let you show your work in a portfolio for college admissions, internships, or competitions.
• Make learning fun because you solve problems that interest you.

Throughout this article I will use simple language and clear steps so you can follow and complete each project.

15 Data Scientist Project Ideas 2026-27

Each project below is described in detail so you can start right away.

1. Predicting Student Scores (Supervised Regression)

Goal: Build a model that predicts student exam scores based on study time, attendance, previous marks, sleep, and other features.

Tools & Skills: Python, pandas, matplotlib/plotly, scikit-learn, train/test split, linear regression, evaluation metrics (RMSE, MAE, R²).

Where to get data: Create a small dataset yourself or use public datasets like “Student Performance” on UCI Machine Learning Repository.

Step-by-step:

1. Collect or download the dataset. If creating your own, include columns: study_hours, attendance_percent, homework_completion, sleep_hours, previous_score, final_score.
2. Load data into pandas and inspect for missing values and outliers.
3. Clean the data: fill or drop missing values, correct formats.
4. Explore the data with plots: scatter plots and correlation matrix.
5. Split the data into training and test sets (e.g., 80/20).
6. Train a linear regression model; also try decision tree regressor.
7. Evaluate models using RMSE and R². Compare results.
8. Write a short report explaining which features mattered most.
9. Optionally deploy as a simple web app (see presentation tips).

What you will learn: Data cleaning, exploratory data analysis (EDA), regression modeling, model evaluation, feature importance.

Extensions: Add more features (family background, extracurriculars), try regularization (Ridge/Lasso), or use cross-validation.

2. Movie Recommendation System (Collaborative Filtering)

Goal: Recommend movies to users based on ratings from other users.

Tools & Skills: Python, pandas, NumPy, scikit-learn, Surprise library or matrix factorization, basic SQL (optional), evaluation metrics like precision@k and RMSE.

Where to get data: MovieLens dataset (small versions available for students).

Step-by-step:

1. Download MovieLens small dataset.
2. Load user, movie, and rating tables. Merge into a single frame if needed.
3. Perform EDA: number of users, movies, rating distribution.
4. Build a simple user-based collaborative filtering (find similar users by cosine similarity).
5. Or use matrix factorization (SVD) from Surprise library.
6. Evaluate with train/test or cross-validation. Report RMSE.
7. Create a small function: given a user id, return top 10 recommended movies.
8. Optionally make a demo interface (Jupyter widget or a simple HTML form).

What you will learn: Recommender systems basics, similarity measures, matrix factorization, evaluation of recommenders.

Extensions: Add content-based features (movie genres), combine collaborative and content-based approaches, or implement popularity-based fallback.

3. Predicting Heart Disease (Classification)

Goal: Predict whether a person has heart disease using health metrics like blood pressure, cholesterol, age, and smoking status.

Tools & Skills: Python, pandas, scikit-learn, classification algorithms (logistic regression, decision trees, random forest), confusion matrix, precision, recall, ROC-AUC.

Where to get data: UCI Heart Disease dataset or similar public datasets.

Step-by-step:

1. Load and inspect the dataset.
2. Handle missing values and convert categorical fields to numeric using one-hot encoding.
3. Visualize relationships (age vs disease rate, cholesterol distribution).
4. Split the data into training and test sets.
5. Train multiple classifiers: logistic regression, random forest.
6. Evaluate using accuracy, precision, recall, and ROC-AUC. Plot ROC curve.
7. Use feature importance from random forest to see key predictors.
8. Document results and write suggestions (e.g., which biomarkers to monitor).

What you will learn: Supervised classification, metrics for imbalanced data, encoding categorical variables, feature importance.

Extensions: Use SMOTE to handle class imbalance, tune model hyperparameters using GridSearchCV.

4. House Price Prediction (Regression with Real Estate Data)

Goal: Predict house prices based on area, number of rooms, location, age, and amenities.

Tools & Skills: Python, pandas, scikit-learn, feature engineering, gradient boosting models (XGBoost/LightGBM optional).

Where to get data: Kaggle has multiple house price datasets. You can also use local property listings.

Step-by-step:

1. Download dataset and inspect.
2. Clean data: convert area units, fix incorrect values.
3. Engineer features: distance to city center, presence of a garden, floor number.
4. Encode categorical variables (neighborhood) and scale features if needed.
5. Try multiple models: linear regression, RandomForest, XGBoost.
6. Evaluate with RMSE and use cross-validation.
7. Create a prediction pipeline that takes house details and outputs a price estimate.
8. Compare model predictions to actual prices and discuss errors.

What you will learn: Real-world data cleaning, feature engineering, regression techniques, model comparison.

Extensions: Build an interactive calculator or map-based visualization to show price predictions by area.

5. Sentiment Analysis on Tweets (NLP Classification)

Goal: Determine whether tweets are positive, negative, or neutral about a topic.

Tools & Skills: Python, pandas, NLTK or spaCy, scikit-learn, TF-IDF, simple neural nets (optional), data cleaning for text (tokenization, stopwords), evaluation metrics for classification.

Where to get data: Twitter API (requires account) or precompiled sentiment datasets like Sentiment140 or Kaggle datasets.

Step-by-step:

1. Get tweets for a topic (or download dataset).
2. Clean text: remove URLs, mentions, punctuation, and convert to lowercase.
3. Tokenize and remove stopwords. Optionally use stemming or lemmatization.
4. Convert text to numeric features using TF-IDF or word embeddings (optional).
5. Train a classifier (Naive Bayes or logistic regression).
6. Evaluate using accuracy and F1-score; create confusion matrix.
7. Show example tweets and predicted sentiments to explain model behavior.
8. Discuss limitations: sarcasm, misspellings, or slang.

What you will learn: Natural Language Processing (NLP) basics, text preprocessing, vectorization, model evaluation for text.

Extensions: Use pre-trained models (BERT) for better performance, or build a real-time sentiment dashboard.

6. Image Classification of Handwritten Digits (Computer Vision)

Goal: Recognize handwritten digits (0–9) using the MNIST dataset.

Tools & Skills: Python, NumPy, matplotlib, scikit-learn, TensorFlow or PyTorch (for deep learning), convolutional neural networks (CNNs).

Where to get data: MNIST dataset (available in many libraries like Keras datasets).

Step-by-step:

1. Load MNIST using Keras or other libraries.
2. Visualize sample digits to understand data.
3. Preprocess: normalize pixel values, reshape images if needed.
4. Start with a simple classifier (logistic regression) on flattened pixels.
5. Progress to a CNN using Keras: Conv -> Pool -> Dense.
6. Train model and evaluate on test set. Track accuracy.
7. Show misclassified digits and analyze why the model failed.
8. Try data augmentation (rotations, shifts) to improve performance.

What you will learn: Image preprocessing, deep learning basics, convolution layers, model training and evaluation.

Extensions: Try classification of letters or fashion items (Fashion-MNIST) or build a small web app to draw digits and get predictions.

7. Customer Segmentation (Clustering)

Goal: Group customers into segments based on purchase behavior so businesses can target each group differently.

Tools & Skills: Python, pandas, scikit-learn, clustering algorithms (k-means, hierarchical), PCA for dimensionality reduction, silhouette score for evaluation.

Where to get data: Public retail datasets (Kaggle) or create a dataset with features: total_spent, visits_per_month, average_order_value, category_preference.

Step-by-step:

1. Load the customer dataset and inspect distributions.
2. Standardize numeric features (important for clustering).
3. Use k-means clustering and try different k values.
4. Use elbow method and silhouette score to choose k.
5. Visualize clusters using PCA or t-SNE.
6. Describe each cluster: high spenders, frequent but low spenders, occasional buyers.
7. Suggest marketing actions for each cluster.

What you will learn: Unsupervised learning, feature scaling, clustering evaluation, customer insights.

Extensions: Use clustering on product features to find product groupings or combine clustering with prediction models.

8. Time Series Forecasting: Sales Prediction

Goal: Forecast future sales for a store using past sales data.

Tools & Skills: Python, pandas, statsmodels, Prophet (optional), ARIMA models, visualization of trends and seasonality.

Where to get data: Retail sales data on Kaggle or create mock monthly sales data.

Step-by-step:

1. Load time-stamped sales data and set the index to date.
2. Visualize the series and decompose into trend, seasonality, and residuals.
3. Split into training and test sets by date (do not random split).
4. Try simple models: moving average, exponential smoothing.
5. Fit ARIMA or SARIMA if seasonal patterns exist. Alternatively use Facebook Prophet for quick results.
6. Evaluate forecasts using MAE or MAPE.
7. Plot actual vs predicted and discuss possible improvements.

What you will learn: Time series basics, decomposition, model choice for ordered data, forecast evaluation.

Extensions: Add external (exogenous) variables like promotions or holidays to improve forecasts.

9. Fraud Detection (Anomaly Detection)

Goal: Detect unusual or fraudulent transactions among many normal transactions.

Tools & Skills: Python, pandas, scikit-learn, isolation forest, one-class SVM, evaluation on imbalanced data, precision-recall.

Where to get data: Credit card fraud detection datasets on Kaggle.

Step-by-step:

1. Load the dataset and inspect class imbalance (fraud vs normal).
2. Use unsupervised methods like Isolation Forest or autoencoders if labels are rare.
3. If labels exist, try supervised algorithms with careful evaluation (precision and recall matter more than accuracy).
4. Use stratified sampling to build training and validation sets.
5. Tune thresholds to balance false positives and false negatives.
6. Create a rule-based filter for very obvious frauds (e.g., huge amounts).
7. Present a short action plan for flagged transactions (manual review, temporary hold).

What you will learn: Handling imbalanced datasets, anomaly detection techniques, precision vs recall trade-offs.

Extensions: Build an alert system or real-time detector using streaming data.

10. Topic Modeling for School Articles (Unsupervised NLP)

Goal: Discover common topics in a set of articles, essays, or school reports.

Tools & Skills: Python, gensim or scikit-learn, TF-IDF, LDA (Latent Dirichlet Allocation), text preprocessing.

Where to get data: Collections of articles, school essays, or scraped news articles (ensure permission).

Step-by-step:

1. Gather a set of documents (e.g., essays or news articles).
2. Clean the text and tokenize; remove stopwords.
3. Convert to document-term matrix using TF-IDF or count vectors.
4. Apply LDA to find 5–10 topics.
5. Inspect top words for each topic and assign labels.
6. Visualize topics and show how each document mixes topics.
7. Use findings to group essays by theme or to suggest reading lists.

What you will learn: Unsupervised NLP, topic extraction, interpreting machine-discovered topics.

Extensions: Use topic modeling to monitor changes in topics over time.

11. Traffic Accident Analysis and Prediction

Goal: Analyze traffic accident data to find risky locations and predict accident severity.

Tools & Skills: Python, pandas, geopandas (optional), visualization (map plots), classification/regression models.

Where to get data: Government open-data portals often publish accident datasets. Use public datasets with location and severity columns.

Step-by-step:

1. Load accident dataset including date, time, location, weather, vehicle types, and severity.
2. Clean and map location coordinates; use geopandas to plot accident density.
3. Identify hotspots with high accident frequency.
4. Train a model to predict severity based on features (weather, time, vehicle count).
5. Evaluate classification results (accuracy, recall).
6. Create a report with maps and recommendations to improve safety.

What you will learn: Working with geospatial data, visualization on maps, real-world data analysis, predictive modeling.

Extensions: Build a dashboard highlighting hotspots and predictions per road segment.

12. Energy Consumption Forecasting for a Home

Goal: Predict household electricity usage to help save energy and cost.

Tools & Skills: Python, pandas, time series modeling, feature engineering for time-of-day and temperature.

Where to get data: Smart meter datasets (open data) or synthetic home energy datasets.

Step-by-step:

1. Load hourly/daily energy consumption data.
2. Add features: hour of day, day of week, temperature, holiday flags.
3. Visualize patterns: peak hours, seasonal variations.
4. Use time series models or tree-based regressors with lag features.
5. Evaluate model using MAE or MAPE.
6. Provide tips like shifting heavy usage to off-peak hours.

What you will learn: Time series forecasting with external features, practical energy awareness.

Extensions: Create an alert system that predicts unusually high consumption and suggests actions.

13. Loan Default Prediction (Banking Use Case)

Goal: Predict whether a loan applicant is likely to default, helping banks approve loans safely.

Tools & Skills: Python, pandas, scikit-learn, class imbalance handling, feature selection, ROC-AUC.

Where to get data: Lending Club datasets or other public loan datasets.

Step-by-step:

1. Load dataset including borrower income, employment length, credit score, loan amount, and default label.
2. Clean and encode categorical fields.
3. Split data and train classifiers (logistic regression, random forest).
4. Address imbalance using sampling techniques or class weights.
5. Evaluate using ROC-AUC and confusion matrix.
6. Identify top predictive features and explain why they matter.
7. Prepare a short policy suggestion for approving or rejecting loans.

What you will learn: Risk modeling, working with financial datasets, interpreting model features.

Extensions: Use explainable AI tools (SHAP, LIME) to explain predictions to non-technical stakeholders.

14. Air Quality Analysis and Forecast (Environmental Data)

Goal: Analyze air quality data, find trends, and forecast future pollutant levels.

Tools & Skills: Python, pandas, visualization, time series models, mapping pollutants by location.

Where to get data: Government air quality monitors or open datasets (e.g., motes or city sensors).

Step-by-step:

1. Load pollutant data (PM2.5, PM10, NO2) with timestamps and locations.
2. Visualize daily and monthly trends and compare across locations.
3. Correlate pollution with weather features (temperature, wind).
4. Train simple forecasting models for PM2.5 levels.
5. Create alerts for days likely to exceed safe limits.
6. Suggest public health recommendations (stay indoors, wear masks) for forecasted bad days.

What you will learn: Environmental data handling, public health relevance, time series forecasting.

Extensions: Build a public dashboard displaying live data and forecasts.

15. Sports Performance Analysis (Example: Basketball Shots)

Goal: Analyze a player’s shot data to find strengths and recommend practice areas.

Tools & Skills: Python, pandas, visualization, clustering, simple statistics.

Where to get data: Public sports datasets or sample shot charts provided by leagues; or create mock data.

Step-by-step:

1. Obtain shot chart data with location, shot result (made/missed), time, and game situation.
2. Visualize shot locations and success rates (heatmap or scatter plot).
3. Compute shooting percentages by zone (paint, mid-range, 3-point).
4. Use clustering to group shot patterns and identify underused zones.
5. Provide training suggestions to improve low-percentage areas.
6. Share results as a dashboard or presentation.

What you will learn: Sports analytics basics, spatial analysis, communication of insights.

Extensions: Predict shot outcome probability using logistic regression or build an app for players to track improvement.

How to Choose the Right Project

Choosing a project can be confusing. Here are simple tips:

1. Pick what interests you: Choose a domain you enjoy (sports, music, health, games). Interest helps you finish the work.
2. Start small: For your first projects pick small datasets and simple models. Finish one project before starting another.
3. Focus on learning goals: Decide what you want to learn: data cleaning, visualization, machine learning, or deployment.
4. Think about resources: Ensure you have the tools and data. Some datasets require signups or API keys.
5. Scale up gradually: After finishing a simple version, add complexity (more features, better models).
6. Balance novelty and feasibility: A unique idea is good, but it should be doable within your time and skill level.

How to Present Your Project (For School or Portfolio)

Good presentation matters. Use these steps:

• Start with the problem: Explain why the problem matters (one short paragraph).
• Show the data: Provide key facts like dataset size and main columns.
• Show visuals: Use charts to highlight important findings.
• Explain your model and results: Avoid technical jargon—explain in simple terms.
• Share code and data links: Put your notebook on GitHub or Google Colab.
• Add a short conclusion: What did you learn? What could be improved?
• Optional demo: If you built a small app, include a link or a short video.

Tools and Libraries to Learn (Beginner-Friendly)

• Python – main language for data science.
• pandas – for data manipulation.
• NumPy – numerical computing.
• matplotlib / plotly / seaborn – visualization (matplotlib and plotly are great; seaborn is optional).
• scikit-learn – basic machine learning models.
• Jupyter Notebook or Google Colab – where you write and run code.
• TensorFlow or PyTorch – for deep learning once you feel ready.
• SQL basics – to query structured data.
• Git & GitHub – to save and share your projects.

Tips for Writing Clean Code and Notebooks

• Use clear section headings in your notebook (Data, EDA, Modeling, Results).
• Comment your code so others can understand.
• Show key plots and brief explanations.
• Keep notebooks tidy: avoid printing large tables unless needed.
• Use version control (save a copy to GitHub).
• Include a README that explains how to run your code and where to get the data.

Must Read: 269+ NLP Project Ideas: 5‑Step Guide & Hands‑On Projects

Final Advice and Outro

Data science is a practical field. The fastest way to learn is by doing — starting with one of the 15 projects above will help you understand the full data science workflow: collecting data, cleaning it, exploring it, modeling it, evaluating results, and telling the story.

Pick a project that excites you.

Start small and keep building. Remember to write clear explanations and keep records of what you tried. Share your work on GitHub or as a blog post — explaining your project in writing helps others understand your work and helps you remember what you learned.

If you finish one project, choose another that teaches a new skill (for example: if your first project was regression, try classification or NLP next). Over time you will build a portfolio that shows both depth and variety.

Good luck! Start with one of these Data Scientist project ideas, follow the steps, and have fun learning by building.