Data Science Project Ideas

Analyze a public dataset (e.g. Titanic or Iris) and build an interactive dashboard to communicate insights.

• Load and clean data using pandas, handling nulls and duplicates
• Generate descriptive statistics for all features
• Create at least 6 visualizations (distributions, correlations, box plots) using matplotlib/seaborn
• Build an interactive dashboard with Plotly Dash or Streamlit
• Document findings in a written summary section

Predict house sale prices using the Ames Housing dataset with feature engineering and regression models.

• Perform EDA and handle missing values with justification
• Engineer at least 5 new features from existing columns
• Train and compare Linear Regression, Ridge, and Lasso models
• Evaluate using RMSE and R² on a held-out test set
• Plot feature importances and residuals

Build a binary classification model to predict which telecom customers will churn using real-world structured data.

• Handle class imbalance using SMOTE or class weights
• Train at least 3 classifiers (Logistic Regression, Random Forest, XGBoost)
• Tune hyperparameters with GridSearchCV or RandomizedSearchCV
• Report precision, recall, F1, ROC-AUC, and confusion matrix
• Generate a SHAP summary plot for model explainability

Build a collaborative and content-based hybrid recommendation system using the MovieLens dataset.

• Implement user-based and item-based collaborative filtering from scratch
• Build a content-based model using TF-IDF on movie metadata
• Combine both into a hybrid recommender with a blending weight parameter
• Evaluate with RMSE and Precision@K metrics
• Expose recommendations via a simple Streamlit UI

Build an end-to-end NLP pipeline to classify tweet sentiment and visualize trends over time.

• Collect or load labeled tweet data and perform text preprocessing (tokenization, stopword removal, lemmatization)
• Train a TF-IDF + Logistic Regression baseline and a fine-tuned BERT model
• Compare both models on accuracy, F1, and inference speed
• Visualize sentiment trends over time with rolling averages
• Package the inference step as a reusable Python module

Forecast retail product demand using classical and ML-based time series methods on the Rossmann store dataset.

• Decompose series into trend, seasonality, and residuals
• Implement ARIMA and Exponential Smoothing baselines
• Engineer lag features, rolling statistics, and calendar features for an XGBoost model
• Compare models using MAE and MAPE on a future holdout window
• Plot forecast vs actuals with confidence intervals

Train a convolutional neural network to classify images from CIFAR-10, applying transfer learning and data augmentation.

• Build and train a custom CNN baseline with PyTorch or TensorFlow
• Apply data augmentation (flips, crops, color jitter) to reduce overfitting
• Fine-tune a pretrained ResNet-18 model on the same dataset
• Track training/validation loss and accuracy with TensorBoard
• Compare custom CNN vs transfer learning in a results table

Train a fraud detection model and deploy it as a production-ready REST API with monitoring and CI/CD.

• Train an XGBoost fraud classifier with full preprocessing pipeline using scikit-learn Pipeline
• Serialize the model with MLflow, logging params, metrics, and artifacts
• Wrap the model in a FastAPI REST endpoint with input validation via Pydantic
• Containerize with Docker and deploy to a cloud platform (Render, Railway, or AWS EC2)
• Implement basic data drift detection using Evidently AI and schedule periodic reports

Simulate a full competitive data science workflow by building a stacked ensemble for a structured prediction problem.

• Perform rigorous EDA and document all hypotheses before modeling
• Build 5+ diverse base models (LGBM, XGBoost, CatBoost, RF, ElasticNet)
• Implement k-fold stacked generalization with out-of-fold predictions
• Tune the meta-learner and compare CV score to individual model scores
• Generate a reproducible experiment report with all results and code in a Jupyter notebook