Osifo1/Gomycode_Streamlit-chkpnt-2_Financial-Inclusion-in-Africa

The dataset contains demographic information and what financial services are used by approximately 33,600 individuals across East Africa. The ML model role is to predict which individuals are most likely to have or use a bank account. # Financial Inclusion in Africa This repository contains a Jupyter Notebook focused on analyzing and predicting financial inclusion in Africa. The analysis explores key factors that influence individuals' access to financial services and uses machine learning techniques to build a predictive model. ## Project Overview Financial inclusion is a critical aspect of economic development, ensuring individuals and businesses have access to useful and affordable financial products and services. This project utilizes data analysis, feature engineering, and machine learning to predict the likelihood of individuals owning a bank account. ## Dataset The dataset used in this project includes demographic, economic, and regional information of individuals across various African countries. It contains features such as: - **Country** - **Age** - **Gender** - **Marital Status** - **Education Level** - **Employment Status** - **Cellphone Ownership** - **Target Variable**: `bank_account` (Yes/No) ## Steps in the Analysis 1. **Data Loading and Inspection**: - The dataset is loaded using pandas. - Initial exploration includes checking for duplicates and missing values. 2. **Data Profiling**: - A profiling report is generated using `ydata_profiling` to understand the dataset's structure and distribution. 3. **Data Cleaning**: - Handling missing values. - Encoding categorical variables (e.g., one-hot encoding). - Transforming the target variable (`Yes` to 1, `No` to 0). 4. **Feature Engineering**: - Creating new features based on the dataset's characteristics. 5. **Model Building**: - Applying machine learning algorithms to predict financial inclusion. 6. **Model Evaluation**: - Assessing model performance using metrics such as accuracy, precision, recall, and F1-score. ## Requirements The project uses the following Python libraries: - pandas - ydata_profiling - scikit-learn - matplotlib - seaborn Install the required libraries using the command: ```bash pip install -r re …