royChuks/Nigeria-Terrorism-Data-App

Attack Analysis Application Overview This application performs exploratory data analysis (EDA) and machine learning on attack trends using a dataset. It provides insights into attack distribution, trends, and geospatial representation. The machine learning models implemented include Logistic Regression, Random Forest, and XGBoost. Features Exploratory Data Analysis (EDA): Attack trends over time Feature correlation matrix Target distribution Geospatial heatmap of attack locations Machine Learning Models: Logistic Regression Random Forest XGBoost Model Evaluation: Accuracy, F1 Score, ROC AUC, Precision, Recall, and Confusion Matrix Interactive UI using PyQt5: Tabs for trends, maps, correlation, target distribution, and model results Buttons for generating visualizations Installation Prerequisites Ensure you have Python 3.8+ installed on your system. Install Required Packages Run the following command to install dependencies: pip install -r requirements.txt Usage Ensure the dataset (NigeriaData_Cleaned.csv) is placed in the project directory. Run the application: python T_naija.py Use the UI to explore attack trends, visualize data, and analyze machine