matastanamee/Mini-Project-Prediction-of-GDP-in-African-countries

Mini-Project: Prediction of GDP in African Countries is a project that aims to predict the Gross Domestic Product (GDP) of various African countries using machine learning techniques. The project involves data preprocessing, model training, and evaluation, along with visualizing the results. --- ### README # Mini-Project: Prediction of GDP in African Countries This project focuses on predicting the Gross Domestic Product (GDP) of African countries using machine learning algorithms. The goal is to develop and evaluate models that can accurately forecast GDP based on historical and socio-economic data. ## Project Overview This repository contains the code, data, and documentation related to the GDP prediction project. The project is divided into several parts, including data preprocessing, model development, and result visualization. The primary objective is to use machine learning techniques to predict the GDP of various African countries and visualize the results to gain insights into economic trends. ### Key Features - **Data Preprocessing**: Clean and prepare the data for model training, including handling missing values and normalizing the data. - **Model Development**: Implement machine learning models to predict GDP, such as linear regression, decision trees, or more advanced algorithms. - **Evaluation**: Assess the performance of the models using metrics like Mean Squared Error (MSE) and R-squared. - **Visualization**: Generate graphs and charts to visualize the predictions and compare them against actual GDP data. ## Repository Structure - **source code**: Contains the Python scripts and Jupyter notebooks used for data preprocessing, model training, and evaluation. - **picture of graph**: Includes visual representations of the model predictions compared to actual GDP data. - **CSS322_Sec3_Project Report.pdf**: The project report detailing the methodology, results, and conclusions. - **Presentation_slides.pdf**: Slides used for presenting the project, summarizing the key points and findings. ## Getting Started ### Prerequisites To run this project, you will need: - **Python 3.x**: The programming language used for this project. - **Required Libraries**: Install the necessary Python libraries using the following command: ```bash pip in …