use https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=MCRFPUS2&f=M to prepare data for time-series analysis Data needs to be melted All missing data must be removed Year should be converted to ‘string’ data type such that the Year is 4-digit string ‘Month’ and ‘Year’ should be combined and converted to Pandas datetime data type and promoted as index Unnecessary columns should be removed Prepare a time-series of quarterly average daily production data Prepare a time-series of annual average daily production data

Composite datatypes can only have multi row &multi column values in them Answers: Yes No

What precisely is metadata? An answer to this question may be found in the result set's metadata. Result set information may be beneficial in a variety of situations.

Using any dataset apply Apriori Algorithm and Anomalies detection methods to the dataset. Source code needed.

Take the Absenteeism at work Data Set, obtained from the UC-Irvine Machine Learning Data Repository, as a data set to be discretized. Perform data discretization for two of the numerical attributes using the Equal-Width or EqualFrequency method. (Let the stopping criteria be max-interval = 4). You need to write a small program to do this to avoid clumsy numerical computation. Submit your simple analysis and your test results: split points, final intervals, and your documented source program.

A set of 11 data points is given in the attached Excel spreadsheet: PS5_prob1_FD_data.xlsx.Task: Perform numerical differentiation to approximate the second derivative of theprovided data. In doing so, address the following prompts:1. Write your own MATLAB code to perform an appropriate Finite Difference (FD)approximation for the second derivative at each point in the provided data. Note:You are welcome to use the “lowest order” approximation of the second derivative f ′′(x).a) “Read in” the data from the Excel spreadsheet using a built-in MATLAB com-mand, such as xlsread, readmatrix, or readtable – see docs for more info.b) Write your own MATLAB function to generally perform an FD approximationof the second derivative for an (arbitrary) set of n data points. In doing so, usea central difference formulation whenever possible.c) Call your own FD function and apply it to the given data. Report out/displaythe results.2. Reflect on your results by comparing to the exact solution.a) The…

Use Python Code Your task is : Visualization of confirmed cases of COVID-19 by country . (Use pandas to read the data table csv. Example df = pd.read_csv('COVID19.csv' ) then make a visualization using that csv table) Province/State Country Lat Long ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 2/1/2020 2/2/2020 2/3/2020 2/4/2020 2/5/2020 2/6/2020 2/7/2020 2/8/2020 2/9/2020 ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 3/1/2020 3/2/2020 3/3/2020 3/4/2020 3/5/2020 3/6/2020 3/7/2020 3/8/2020 3/9/2020 ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 4/1/2020 4/2/2020 4/3/2020 4/4/2020 4/5/2020 4/6/2020 4/7/2020   Afghanistan 33 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0…

Machine learning Data Set Vegetation classification Dataset Stream false tous вже false Slope false tome tone steep modetabe steep steep Flat Elevation high low Vegetation Chapparal riparian medium medium high steep highest steep high First Calculate total entropy for the given dataset riparian chapparal Conifor Conifer chapparal

R code  Use mt cars dataset  Split the dataset into train and test  Build logistic regression and decision tree  And compare the accuracy

How to code the decision boundaries that determine if a RandomForestClassifier classifies a dataset as one or another? For instance, using the iris dataset, what values determines if the RandomForestClassifier leans to one category of flower or the other.