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.
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Data Exploration: Conduct exploratory data analysis to understand the distribution, correlations, and patterns within the dataset.
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Feature Selection: Identify key features that significantly influence a country's GDP per capita.
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Model Development: Build a predictive model using machine learning algorithms to forecast GDP per capita.
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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:
- Clone the repository:
git clone https://github.com/ujair-shaha/gdp_analysis.git