Update README.md

README.md

@@ -1,157 +1,157 @@
----
-title: My Streamlit Project
-emoji: 🔥
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-python_version: '3.10'
-tags:
-- streamlit
-- data-visualization
-app_file: app.py
-sdk_version: 1.38.0
----
-
-
-# 🌍 **FINDEX Data Analysis & Prediction App** 📊
-
-Welcome to the **FINDEX Data Analysis & Prediction App**! This app provides insights and predictions related to financial inclusion based on the Global Findex 2021 dataset from the World Bank. The app allows users to explore data distributions, statistics, and use machine learning to predict the likelihood of bank account ownership.
-
-## 🚀 **Table of Contents**
-- [Introduction](#introduction)
-- [Features](#features)
-- [Installation](#installation)
-- [Usage](#usage)
-- [Pages Overview](#pages-overview)
-- [Folder Structure](#folder-structure)
-- [Contributing](#contributing)
-- [License](#license)
-- [Contact](#contact)
-
----
-
-## 🌟 **Introduction**
-
-The **FINDEX Data Analysis & Prediction App** is a multi-page Streamlit application that provides a detailed exploration of financial inclusion data. The app uses machine learning models to predict the likelihood of an individual owning a bank account based on various socioeconomic factors, including income, education, and digital payment usage.
-
-Whether you're looking to explore demographic trends or predict financial behavior, this app delivers intuitive visualizations and powerful AI-driven insights.
-
----
-
-## 🔥 **Features**
-- **Data Visualization**: Visualize the distribution of key features, including age, income, and account ownership.
-- **Statistics Overview**: View summary statistics on different demographic groups.
-- **Machine Learning Predictions**: Predict the likelihood of bank account ownership using an XGBoost model.
-- **SHAP Explanations**: Understand the key factors behind the predictions with SHAP visualizations.
-- **Filtering Options**: Customize your analysis by filtering data by gender, education, age, and more.
-
----
-
-## 💻 **Installation**
-
-To run the app locally, follow these steps:
-
-1. **Clone the repository**:
-    ```bash
-    git clone https://github.com/Driisa/Final-submission-BDS-app.py.git
-    ```
-
-2. Navigate to the project directory:
-    ```bash
-    cd FINDEX-App
-    ```
-
-3. Set up a virtual environment (optional but recommended):
-    ```bash
-    python -m venv venv
-    source venv/bin/activate  # On Windows use: venv\Scripts\activate
-    ```
-
-4. Install the required dependencies:
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-5. Run the app:
-    ```bash
-    streamlit run app.py
-    ```
-
-6. Open your browser: Visit http://localhost:8501 to start using the app!
-
----
-
-## 🕹 **Usage**
-
-- **Navigate the App**: Use the sidebar to switch between different pages such as Info, Distribution, Statistics, and Prediction.
-- **Input Your Data**: On the Prediction page, provide the input data for demographic and socioeconomic factors to predict bank account ownership.
-- **Visualize Data**: Use the Distribution and Statistics pages to explore data distributions and key insights from the dataset.
-
----
-
-## 📚 **Pages Overview**
-
-1️⃣ **Info**  
-Provides an introduction to the Global Findex dataset and the key variables used in the analysis.
-
-2️⃣ **Distribution**  
-Visualize the distribution of various features, including age, mobile ownership, internet access, and account ownership, with bar plots and histograms.
-
-3️⃣ **Statistics**  
-Analyze summary statistics for key demographic groups, including age and education, and view bar plots showing account ownership across income and age groups.
-
-4️⃣ **Prediction**  
-Predict whether an individual has a bank account based on inputted data. The prediction is powered by an XGBoost model, and SHAP visualizations help explain the predictions.
-
----
-
-## 📁 **Folder Structure**
-
-```bash
-FINDEX-App/
-├── models/
-│   ├── df.joblib
-│   ├── df_sample.joblib
-│   ├── ohe.joblib
-│   ├── scaler.joblib
-│   └── xgb_clf.joblib
-├── app.py
-├── README.md
-├── requirements.txt
-└── .streamlit/
-```
-
-## 💡 **Contributing**
-
-Want to contribute to the project? Here’s how:
-
-1. **Fork the repository**.
-2. **Create a new branch** for your feature or bug fix:
-    ```bash
-    git checkout -b feature-new-feature
-    ```
-3. Make your changes and commit them:
-    ```bash
-    git commit -m "Added a new feature!"
-    ```
-4. Push the changes to your forked repo:
-    ```bash
-    git push origin feature-new-feature
-    ```
-5. Open a Pull Request, and let’s review your changes!
-
----
-
-## 📜 **License**
-
-This project is licensed under the MIT License. See the LICENSE file for more details.
-
----
-
-## 📞 **Contact**
-
-If you have any issues or suggestions, feel free to reach out by opening a GitHub issue. We’d love to hear from you and improve the app based on your feedback!
-
----
-
+---
+title: My Streamlit Project
+emoji: 🔥
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+python_version: '3.10'
+tags:
+- streamlit
+- data-visualization
+app_file: app.py
+sdk_version: 1.39.0
+---
+
+
+# 🌍 **FINDEX Data Analysis & Prediction App** 📊
+
+Welcome to the **FINDEX Data Analysis & Prediction App**! This app provides insights and predictions related to financial inclusion based on the Global Findex 2021 dataset from the World Bank. The app allows users to explore data distributions, statistics, and use machine learning to predict the likelihood of bank account ownership.
+
+## 🚀 **Table of Contents**
+- [Introduction](#introduction)
+- [Features](#features)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Pages Overview](#pages-overview)
+- [Folder Structure](#folder-structure)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact](#contact)
+
+---
+
+## 🌟 **Introduction**
+
+The **FINDEX Data Analysis & Prediction App** is a multi-page Streamlit application that provides a detailed exploration of financial inclusion data. The app uses machine learning models to predict the likelihood of an individual owning a bank account based on various socioeconomic factors, including income, education, and digital payment usage.
+
+Whether you're looking to explore demographic trends or predict financial behavior, this app delivers intuitive visualizations and powerful AI-driven insights.
+
+---
+
+## 🔥 **Features**
+- **Data Visualization**: Visualize the distribution of key features, including age, income, and account ownership.
+- **Statistics Overview**: View summary statistics on different demographic groups.
+- **Machine Learning Predictions**: Predict the likelihood of bank account ownership using an XGBoost model.
+- **SHAP Explanations**: Understand the key factors behind the predictions with SHAP visualizations.
+- **Filtering Options**: Customize your analysis by filtering data by gender, education, age, and more.
+
+---
+
+## 💻 **Installation**
+
+To run the app locally, follow these steps:
+
+1. **Clone the repository**:
+    ```bash
+    git clone https://github.com/Driisa/Final-submission-BDS-app.py.git
+    ```
+
+2. Navigate to the project directory:
+    ```bash
+    cd FINDEX-App
+    ```
+
+3. Set up a virtual environment (optional but recommended):
+    ```bash
+    python -m venv venv
+    source venv/bin/activate  # On Windows use: venv\Scripts\activate
+    ```
+
+4. Install the required dependencies:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+5. Run the app:
+    ```bash
+    streamlit run app.py
+    ```
+
+6. Open your browser: Visit http://localhost:8501 to start using the app!
+
+---
+
+## 🕹 **Usage**
+
+- **Navigate the App**: Use the sidebar to switch between different pages such as Info, Distribution, Statistics, and Prediction.
+- **Input Your Data**: On the Prediction page, provide the input data for demographic and socioeconomic factors to predict bank account ownership.
+- **Visualize Data**: Use the Distribution and Statistics pages to explore data distributions and key insights from the dataset.
+
+---
+
+## 📚 **Pages Overview**
+
+1️⃣ **Info**  
+Provides an introduction to the Global Findex dataset and the key variables used in the analysis.
+
+2️⃣ **Distribution**  
+Visualize the distribution of various features, including age, mobile ownership, internet access, and account ownership, with bar plots and histograms.
+
+3️⃣ **Statistics**  
+Analyze summary statistics for key demographic groups, including age and education, and view bar plots showing account ownership across income and age groups.
+
+4️⃣ **Prediction**  
+Predict whether an individual has a bank account based on inputted data. The prediction is powered by an XGBoost model, and SHAP visualizations help explain the predictions.
+
+---
+
+## 📁 **Folder Structure**
+
+```bash
+FINDEX-App/
+├── models/
+│   ├── df.joblib
+│   ├── df_sample.joblib
+│   ├── ohe.joblib
+│   ├── scaler.joblib
+│   └── xgb_clf.joblib
+├── app.py
+├── README.md
+├── requirements.txt
+└── .streamlit/
+```
+
+## 💡 **Contributing**
+
+Want to contribute to the project? Here’s how:
+
+1. **Fork the repository**.
+2. **Create a new branch** for your feature or bug fix:
+    ```bash
+    git checkout -b feature-new-feature
+    ```
+3. Make your changes and commit them:
+    ```bash
+    git commit -m "Added a new feature!"
+    ```
+4. Push the changes to your forked repo:
+    ```bash
+    git push origin feature-new-feature
+    ```
+5. Open a Pull Request, and let’s review your changes!
+
+---
+
+## 📜 **License**
+
+This project is licensed under the MIT License. See the LICENSE file for more details.
+
+---
+
+## 📞 **Contact**
+
+If you have any issues or suggestions, feel free to reach out by opening a GitHub issue. We’d love to hear from you and improve the app based on your feedback!
+
+---
+
 🎉 **Enjoy exploring financial inclusion data with the FINDEX Data Analysis & Prediction App!** 🎉