rootcode-creator/Court-Judgments-Prediction-using-Machine-Learning

Judgments Prediction of Supreme Court of Nigeria (SCN) Court Judgments Prediction using Machine Learning Supreme Court of Nigeria (SCN) appeal-case outcome prediction with interpretable ML workflows. ## Court Judgments Prediction — README This project predicts judicial outcomes for Supreme Court of Nigeria appeal cases using supervised machine learning models and compares their performance with visual diagnostics and confusion matrices. ## Table of Contents - 🚀 Project intro - 📁 Project structure - 📊 Dataset - 🧠 Modeling approach - 🤖 Models evaluated - 📈 Outputs and artifacts - ⚙️ How to run - 📄 License ## 🚀 Project intro The repository demonstrates an end-to-end ML workflow for legal outcome prediction: - Data profiling and exploratory analysis of SCN appeal data - Feature engineering and preprocessing (including encoding/scaling) - Training and comparison of multiple classification algorithms - Performance reporting with confusion matrices and plots ## 📁 Project structure ```txt Court-Judgments-Prediction-using-Machine-Learning/ ├── CSE 445.ipynb ├── scn_appeal_cases_data.csv ├── datasetprofiling.html ├── Dataset description.pdf ├── IMAGES/ │ ├── Confusion Matrix for LogisticRegression().png │ ├── Confusion Matrix for SVC().png │ ├── Feature_corelation_rc_heatmap_plot.png │ └── ... ├── LICENSE └── README.md ``` ## 📊 Dataset - **Source:** Primsol Law Pavilion archive (distributed via Mendeley) - **Records:** 5,585 appeal cases - **Scope:** Criminal and civil appeal matters from the Supreme Court of Nigeria - **Dataset link:** data.mendeley.com ## 🧠 Modeling approach The notebook performs: - Data cleaning and exploratory visual analysis - Feature transformation with `LabelEncoder` and scaling tools where required - Train/test split and model training - Evaluation using accuracy metrics, classification reports, and confusion matrices ## 🤖 Models evaluated Primary and baseline models included in the notebook: - `DummyClassifier` - `DecisionTreeCl …