碳排放预测模型,基于Holt winter、Prophet、ANN、CNN、LSTM神经网络的（预测未来发展趋势）Python

# Net-Zero-Strategy-Cross-country-Co2_prediction_and_Forecast_-OWID- Phase 1 Notebook Content: Project Overview Notebook setup - Libraries and data imports Data Overview Data shape preview column description data type (columns) Statistical summary missing values Data Cleaning Handling missing data optimally Final shape of data Export of cleaned DataFrame to a file 1. Project Overview Industrialization and Human Emission of carbon-di-oxide are among the key drivers of climate change. Aim of project: Analysis of country specific record in desinging machine learning models for prediction and Forcast of CO2 emmision, leveraging records from vast majority of countries worldwide including: Annual production of coal from different sources like coal and cement population Econimic indicator - GDP Emissions from oil etc Using recent records from 1990-2020 The project is sub-divided into four phases: Data cleaning and preparation Data Visualization and Exploration Predictive analysis with Random Forest algorithm k-nearest neighbors algorithm Decision Trees learning algorithm multilayer perceptron(Neural network model) Forcast analysis: Univariate