Using the attached file “TSLA_B.csv” conduct the following analysis and report on your findings. Note that all written work MUST be completed in Jupyter Notebook Markdown (please review “Jupyter Notebook Tutorial” Notes in Moodle if you are unsure of this). All Code must be included in code blocks (As normal). No other upload will be accepted. ? Characterisation of the data set: size; number of attributes; has/does not have missing values, number of observations etc. ? Application of Data preparation/evaluation methods (Cleaning, renaming, etc) and EDA visualizations (plural), including a clear and concise explanation of your rationale for what you are doing with the data and why you are doing it. ? Use PCA to establish the minimum number of features needed for retaining 95% variance in the data and then implement PCA to dimensionally reduce the data to the number of features that you have discovered. Include a clear and concise explanation of your rationale for what you are doing with the data and why you are doing it. ? Explain in your own words what the “Curse of Dimensionality ” is. ?