This project investigates a comprehensive dataset containing information on the population, region, area size, mortality, and more of 227 countries. The primary focus is on identifying and modeling the factors that affect a country's GDP per capita. By leveraging the rich dataset, the goal is to build a predictive model that can provide insights into the economic performance of different nations.

The dataset used in this project comprises data points for 227 countries, covering a range of socio-economic indicators. Some of the key features include:

• Population
• Region
• Area Size
• Mortality Rates
• Economic Indicators

The dataset serves as the foundation for the analysis and modeling process.

1. Data Exploration: Conduct exploratory data analysis to understand the distribution, correlations, and patterns within the dataset.

2. Feature Selection: Identify key features that significantly influence a country's GDP per capita.

3. Model Development: Build a predictive model using machine learning algorithms to forecast GDP per capita.

4. Evaluation: Assess the model's performance and interpret the significance of various features in predicting GDP per capita.

The project follows a structured approach, including:

• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Development and Training
• Model Evaluation

The project aims to provide insights into the factors influencing GDP per capita and offer a predictive model capable of estimating a country's economic performance based on the provided features.

To replicate the analysis and explore the results, follow these steps:

1. Clone the repository:

git clone https://github.com/ujair-shaha/gdp_analysis.git