10 progressive data science projects bridging soil science domain expertise with modern ML, analytics, and engineering. Each project targets skills most demanded by junior Data Scientist roles.
Exploratory data analysis on soil property datasets — pH, organic matter, nutrient content, and texture. Uncover patterns, distributions, and correlations through statistical summaries and rich visualizations.
Design and populate a relational database for agricultural field experiments. Practice writing complex analytical queries — JOINs, CTEs, window functions, and aggregations on multi-table research data.
Build regression models to predict soil organic carbon (SOC) content from physical and chemical soil properties. Compare Linear Regression, Random Forest, and XGBoost with rigorous evaluation and feature importance analysis.
Classification model predicting optimal crop types from combined soil and climate features. Implement proper train/test splitting, handle class imbalance, and evaluate with precision, recall, F1, and ROC-AUC metrics.
Time series analysis and forecasting of agricultural commodity prices (wheat, corn, sunflower). Explore seasonality, trend decomposition, and compare ARIMA, SARIMA, and Facebook Prophet for multi-step ahead predictions.
NLP pipeline for topic modeling on agricultural research abstracts. Build a corpus from open-access papers, preprocess text, apply TF-IDF vectorization, and discover latent themes using Latent Dirichlet Allocation (LDA).
Apply unsupervised learning to soil microbiome data to discover natural community patterns. Use K-Means, hierarchical clustering, and DBSCAN, then visualize high-dimensional structure with PCA and t-SNE embeddings.
Image classification model using Convolutional Neural Networks to detect plant stress and disease from leaf photographs. Leverage transfer learning with pre-trained models (ResNet, EfficientNet) and data augmentation techniques.
Build an interactive web dashboard with Streamlit and Plotly for exploring soil health data. Include filters, dynamic charts, geographic map visualizations, and responsive layouts that non-technical stakeholders can use.
Complete production-grade ML pipeline for predicting drought conditions: automated data ingestion, feature engineering, model training with MLflow tracking, Docker containerization, and REST API deployment with FastAPI.