K-Caxton/ML_PROJECT

A machine learning project comparing K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) for predicting financial inclusion in East Africa using the Financial Inclusion in Africa dataset. # Financial Inclusion Prediction Using Machine Learning A machine learning project that compares the performance of K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN) in predicting bank account ownership using the Financial Inclusion in Africa dataset. ## Features - Data preprocessing and feature engineering - One-Hot Encoding and feature scaling - Stratified 2-Fold Cross-Validation - Comparison of four machine learning models - Performance evaluation using Accuracy, Precision, Recall, and F1-Score ## Technologies - Python - Pandas - NumPy - Matplotlib - Scikit-learn ## Repository Contents - `financial_inclusion.ipynb` – Jupyter Notebook containing the implementation. - `Train.csv` – Dataset used for model training. - `ML_project ppt.pptx` – Project presentation. - `ML_project Report.docx` - Project Report - `README.md` – Project documentation. ## Author **Caxton Kiptoo** **Justus Onyango** Bachelor of Science in Statistics and Data Science Strathmore University