IN THIS PROJECT, YOU ARE PROVIDED WITH A MODIFIED VERSION OF THE RED WINE DATASET. YOU ARE TO FOLLOW THE STEPS BELOW USING THE OBJECTED-ORIENTED APPROACH TO PERFORM EXPLORATORY DATA ANALYSIS.SECTION I:Create a folder and name it with your index numberCopy the modified red_wine.csv dataset into the folderStart a Jupyter Notebook Server using the Command Prompt InterfaceSECTION II:In the Jupyter Notebook environment, write the code needed to perform the following task:Using markdown, write you provide your name, index number, and classInstall and import the following Python Libraries:PandasMatplotlibNumPyImport the modified red_wine.csv dataset into your working notebook environmentPreview the first 10 rows using the head methodPreview the last 20 rows using the tail methodUse the info method to check basic information on the datasetUse the describe method to obtain statistical information on the dataset.Deploy all manner of skills and techniques including graphs and plots to obtain meaningful insight from the dataset.Use markdown to report on all plots and graphs obtained.

a) Download the Data from Blackboard. B) Launch JupyterNotebook. C) Create a new Notebook. D) Import pandas and numpy libraries. E) Import dataand name it df

In preparation for this tutorial: a) Download the Data from Blackboard. B) Launch JupyterNotebook. C) Create a new Notebook. D) Import pandas and numpy libraries. E) Import dataand name it df.

library(mclust) data=read.csv("wine2.csv", header = TRUE) data_pca <- prcomp(data[, 2:14], scale = TRUE) data_pca_df <- data.frame(data_pca$x[, 1:2]) mclust_model <- Mclust(data_pca_df, G = 3) cluster_labels <- mclust_model$classification plot(data_pca_df, col = cluster_labels, pch = 16) based on these codes, how can i add some extra codes to calculate MCR(misclassification rate )

data <- read.csv("wine2.csv", header = TRUE, sep=",") library(EMMIXmfa) model <- mfa(data, g=3, q=6,itmax=500, nkmeans=1, nrandom=5) summary(model) cluster_assignments <- model$classification true_labels <- iris[,-1] mcr_mfa <- mean(cluster_assignments != true_labels) cat("Misclassification Rate (MCR) for MFA model:", mcr_mfa, "\n") the result of above codes show the MCR is NA ,why and how to fix it

Question 1What functionality is provided by the Jupyter Notebook file to Data Scientists?1 pointAllows to combine descriptive text, code blocks, and code output in a single fileRuns the code and generates the output in a separate fileAllows to code in only one languageDoes not allow to share the file in any format