Foreign-Direct-Investment-Analytics

Foreign Direct Investment (FDI) Analytics is a project aimed at analyzing and understanding trends, patterns, and factors influencing foreign direct investment in various countries. FDI plays a crucial role in the economic development of nations, and analyzing its dynamics can provide valuable insights for policymakers, investors, and businesses.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Technologies Used
• Contributing
• License

Overview

Foreign direct investment (FDI) plays a crucial role in the economic development of nations by facilitating capital flows, technology transfer, and job creation. The Foreign Direct Investment Analytics project aims to analyze historical FDI data, identify trends, compare FDI flows between countries, sectors, and regions, and assess the impact of FDI on economic indicators.

Features

• Data collection from reliable sources such as World Bank, UNCTAD, and national statistics agencies.
• Data preprocessing to handle missing values, inconsistencies, and outliers.
• Exploratory Data Analysis (EDA) to understand the distribution, correlations, and dynamics of FDI data.
• Visualization of insights using time series plots, choropleth maps, bar charts, and other visualizations.
• Modeling techniques such as regression, time series analysis, and clustering for forecasting and trend analysis.
• Development of interactive dashboards to allow users to explore FDI data dynamically.
• Report generation summarizing key findings, trends, and recommendations.

Installation

1. Clone the repository to your local machine: git clone https://github.com/kdkunal_45/Foreign_Direct_Investment_Analytics.git

2. Navigate to the project directory: cd Foreign_Direct_Investment_Analytics

3. Install the required dependencies: pip install -r requirements.txt

Usage

1. Prepare the FDI data or use the provided sample dataset.
2. Run the preprocessing scripts to clean and preprocess the data.
3. Explore the data using Jupyter notebooks or Python scripts for EDA.
4. Train and evaluate machine learning models for forecasting and trend analysis.
5. Create visualizations and interactive dashboards to present insights.
6. Generate reports summarizing key findings and recommendations.

Technologies Used

• Programming Languages: Python
• Data Analysis Libraries: Pandas, NumPy, Scikit-learn
• Visualization Libraries: Matplotlib, Seaborn, Plotly
• Dashboard Frameworks: Dash, Streamlit
• Machine Learning Algorithms: Regression, Time Series Analysis, Clustering
• Database Management: SQL, MongoDB
• Web Scraping: BeautifulSoup, Scrapy

Contributing

Contributions are welcome! Please open an issue or submit a pull request with any improvements or suggestions.

License

This project is licensed under the MIT License. See the LICENSE file for details.