mehdixlabetix/CO2EmissionRawanda

This repository contains a Jupyter notebook that predicts CO2 emissions in Rwanda using machine learning. The notebook imports a dataset of historical CO2 emissions data and uses a variety of machine learning models to predict future emissions. The best model is then selected and used to generate predictions for the next year. # CO2 Emission Prediction in Rwanda This Jupyter Notebook presents a data analysis and machine learning project focused on predicting CO2 emissions in Rwanda. The notebook explores various data preprocessing steps, feature engineering, model selection, and evaluation techniques to create a predictive model for CO2 emissions. ## Table of Contents - Introduction - Dependencies - Dataset - Methodology - Importing Libraries - Reading and Exploring Data - Data Preprocessing - Exploratory Data Analysis (EDA) - Feature Engineering - Model Selection - Model Training and Evaluation - Conclusion - Usage - Acknowledgments - License ## Introduction The purpose of this project is to develop a machine learning model that predicts CO2 emissions in Rwanda. The project involves a comprehensive data analysis, including data preprocessing, exploratory data analysis, feature engineering, and the selection of suitable machine learning algorithms. ## Dependencies The following Python libraries are required to run this notebook: - pandas - numpy - geopandas - shapely - folium - matplotlib - seaborn - scikit-learn - xgboost - lightgbm - fasteda - optuna - haversine You can install these dependencies using the following command: ```bash pip install pandas numpy geopandas shapely folium matplotlib seaborn scikit-learn xgboost lightgbm fasteda optuna haversine ``` ## Dataset The dataset for this project consists of CSV files: 'train.csv' and 'test.csv'. These files contain relevant features and CO2 emission values that are used for training and evaluating the predictive model. ## Methodology ### Importing Libraries The initial step involves importing necessary Python libraries for data analysis, visualization, and modeling. ### Reading and Exploring Data The provided CSV files are read into dataframes using the `pandas` library. Exploratory data analysis techniques are applied to understand the dataset's characteristics and structure. ### Data Preprocessing Data preprocess …