by admin | Nov 23, 2022 | R
this project has to be done in RMarkdown
The dataset attached, rubric and project information is attached
please follow project rubric.
Dataset found on Kaggle: Swiss banknote counterfeit detection (https://www.kaggle.com/datasets/chrizzles/swiss-banknote-conterfeit-detection)
please answer only part a, c
a)PCA analysis group includes: This group will use PCA to reduce the dimension of data. At the same time look for potential correlations between different predictors.
c)Hypothesis testing group 2 includes: This group will try to use appropriate statistical techniques to infer the population means of each predictor variable for counterfeit and real bills.
by admin | Nov 22, 2022 | R
This is the Group project. My part is to do the result And the Conclusion part. The topic is about the Mental effect of Covid in students. the following is the rubric for Conclusion and result.
Conclusions and interesting future research questions.Summarize your results and state the conclusions of your analysis.
Are there any new questions that are open with your analysis? Are there any other questions that you would have tackled if you had had more time?
Note: I have also attached the picture of the sample how conclusion should like and the picture of rubric, please read the rubric and sample picture
by admin | Nov 21, 2022 | R
Assignment #3 – Intro to modeling
Please note that these assignments are meant to build on concepts from before so please keep that in mind when solving the problems set forth.
Your submission for this will be one file that has answers to all of the parts below in HTML format.
Part 1 – Get set up for the exam.
Navigate to Module 1 – Resources, download all exam files, and ensure that everything works prior to Exam 1 (you’ll find this all on the study guide). There isn’t anything to submit for part 1.
Part 2 – Regression and Predictions
Before getting started, please be sure to download the “vehicles Download vehiclesDownload vehicles” data-set.
1. Partition your data so that 60% is training, and 40% is testing data. Create a data-frame called “training_vehicles” (this will have 60% of your original vehicles data) and another called “testing_vehicles” (this will have 40% of your original vehicles data).
2. Create a linear regression model to predict the MPG of a vehicle. Explain if your model is statistically significant and tell me the proportion of variation that is accounted for in the linear model. You can use simple linear regression or multiple linear regression (the choice is yours).
3. Create a visual that shows the residuals of your linear model. Explain what this tells you about your model.
Part 3 – Regression Continued (Putting it all together)
Documentation for factors: https://r4ds.had.co.nz/factors.html (and we’ve covered it in class).Links to an external site.
Documentation for how to use the recode() function to accomplish the conversions below (this is an assessment regarding reading documentation pages as I’ve shown in class): https://dplyr.tidyverse.org/reference/recode.html Links to an external site..
Data for questions: dcbikeshare-1.csv Download dcbikeshare-1.csv
Metadata for questions: https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+DatasetLinks to an external site.
Hint: mutate(season = factor(season, levels = c(“spring”, “summer”, “fall”, “winter”))) ##This is is how you set “spring” as the baseline after you recode your data. The baseline is the first position – in this case spring is at position 1, summer is at position 2, fall is at position 3, winter at position 4.
Question 1: Convert the season variable to be a factor with meaningful names per the metadata. Set spring as the baseline level (the first level).
Question 2: Convert the binary variables holiday and working day to be factors with levels no (0) and yes (1), with no as the baseline level (the first level).
Question 3: Convert the yr variable to be a factor with levels 2011 and 2012, with 2011 as the baseline level (the first level).
Question 4: Convert the weathersit variable as 1 – clear, 2 – mist, 3 – light precipitation, 4- heavy precipitation. Set clear as the baseline. Note: in the data-set, there are no instances of “4”, but you can still write the code for it.
Question 6: Fit a linear model predicting the total bike rentals from daily temperature. Explain the performance of this model in 3-4 sentences.
Question 7: Fit a linear model predicting total daily bike rentals from season, year, whether the day is holiday or not, whether the day is a working day or not, the weather category, temperature, feeling temperature, humidity, and windspeed. What is the adjusted R-squared value of this model?
Note: Everyone is to submit this individually even if you did or did not work with your group. If you worked with your group, I expect your submission to be the same as your group members.
by admin | Nov 18, 2022 | R
Combine finance, statistics, and the use of R to predict stock returns and forecast models for the rate of inflation. You cannot use excel!!! There needs to be to files: 1, the document with the answers, and 2, the R file along with how you solved for the answers.
by admin | Nov 18, 2022 | R
After you have extracted data from either of the sites (data.gov.uk) or NOMIS DATA PORTAL integrate the data with the CSV FILE I have shared below using SQLite. the CSV file you get after integration.Kindly share. Do not do ANALYSIS.
by admin | Nov 16, 2022 | R
Description
What makes some songs become popular? The dataset describes popular songs based on auditory features such as loudness and tempo.
Goal
Construct a model using a dataset of popular songs to predict ratings based on auditory features of the songs included in scoringData.csv. (You may use linear regression, logistic regression, feature selection e.g. Lasso, decision tree or advance tree)
Metric
Submissions will be evaluated based on RMSE (root mean squared error). Lower the RMSE, better the model.
Submission File
The submission file should be in text format (.csv) with only two columns, id and rating. The rating column must contain predicted rating. The number of decimal places to use is up to you. The file should contain a header and have the following format:
“id”,”rating”
50400,37.3065
96747,37.1732
1824,36.9784
67597,36.9780
86944,36.8176
85423,37.0173
An example submission file (example_submission.csv) is shared with the set of files under Data.
Sample Code
Here is an illustration in R of how you can create a model, apply it to scoringData.csv to prepare a submission file.
# ensure analysisData.csv and scoringData.csv are in your working directory
# following code will read data and construct a simple model
songs = read.csv(‘analysisData.csv’)
model = lm(rating~ tempo+time_signature,songs)
# read in scoring data and apply model to generate predictions
scoringData = read.csv(‘scoringData.csv’)
pred = predict(model,newdata=scoringData)
# construct submission from predictions
submissionFile = data.frame(id = scoringData$id, rating = pred)
write.csv(submissionFile, ‘sample_submission.csv’,row.names = F)
* Disclaimer: This data is to be used solely for the purpose of the Project for this course. It is not recommended for any use outside of this competition.
Submission Count:
By the end of the competition, you must have at least 3 submissions. At least one must use a forest ranger model.
Attached File Descriptions:
analysisData.csv: Data for building a model
scoringData.csv: Use for applying predictions or scoring
example_submission.csv: Sample submission file in the desired format
