An interactive web application built with Streamlit that analyzes Nepal's key economic indicators. This project leverages a machine learning model to forecast the nation's GDP growth rate, providing insights into its economic stability and resilience.

Live App Link: https://nepal-economic-dashboard-cnbdzguzvwwg7fuqcguxgx.streamlit.app/

This project presents a comprehensive data science workflow applied to the economic landscape of Nepal. By sourcing data from reputable institutions like the World Bank, the application cleans, analyzes, and visualizes trends in tourism, GDP, remittances, and unemployment.

The core of the project is a predictive AI model that uses these indicators to forecast the GDP growth rate, offering a powerful tool for economists, students, and policymakers.

• Interactive Dashboard: A user-friendly web interface built with Streamlit.

• Exploratory Data Analysis (EDA): In-depth visualizations of economic trends and correlations.

• Predictive AI Model: A Linear Regression model to forecast GDP growth based on user inputs.

• Data-Driven Insights: Analysis of how tourism revenue and personal remittances impact Nepal's economy.

This project is built with the following technologies:

• Python: Core programming language.

• Pandas & NumPy: For data manipulation and numerical operations.

• Scikit-learn: For building and training the machine learning model.

• Matplotlib & Seaborn: For data visualization.

• Streamlit: For creating and deploying the interactive web application.

To get a local copy up and running, follow these simple steps.

Make sure you have Python 3.9 or higher installed on your system.

• Python: Download Python

Follow these steps to set up the project environment:

1. Clone the repository:

   git clone [https://github.com/abinashregmi/nepal-economic-dashboard.git]
   cd nepal-economic-dashboard
   
   

2. Create a Virtual Environment (Recommended): This keeps your project dependencies isolated.

   # For Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   For macOS/Linux
   
   python3 -m venv venv
   source venv/bin/activate
   
   

3. Install required packages: The requirements.txt file lists all the necessary libraries.

   pip install -r requirements.txt
   
   
   

Once the setup is complete, you can run the Streamlit application with a single command:

streamlit run app.py