The purpose of the analysis for this project is to determine the effects of CO2 on climate and to gather information from raw data to create a visualisation of the effects and trends displayed in a specific region.  Datasets used in this analysis are sourced from online public source Data was extracted, processed and mined using the Knowledge Discovery Databases methodology.

The aim of this project was to spread awareness of the effects of climate change and its effects and to find out the risk in our atmosphere and what effects it has.  The report looks at what effects , if any , from deposits in our air and atmosphere and displays a visualisation from the data analysed in the area.  RStudio is used as the main hub of the project with R Language used for the coding. 

Machine learning algorithms are implemented, the algorithms which investigated are “K-means” and “Apriori Algorithm”. Neural network algorithm is used to produce prediction output. Plots are generated from the neural network to visualise the data in RStudio.  This project implements and compares sentiment analysis techniques such as logistic regression, support vector machines and a recurring neural network on text documents.

The main technology used is R Markdown which allows me to combine both R and Python programming languages within the same script. This also allows me to generate a web document showing the code and its output, including graphs generated from the same web page, resulting in ease of use when reading the results and insights into the analysis. Using R Markdown allows access to Python libraries from R and R libraries from Python. Data will be processed in Excel /SPSS with a Progression Analysis to make Predictions.