Google Collab questions using Python Project Assignment
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Google Collab questions using Python Project Assignment
Please use Google Collab to open the zipped file and follow the instructions.
Answer the questions by using Python.
With what information does Bayes Theorem update our previous knowledge about the data parameters?
What does the prior probability represent?
For the following exercises, work with the wine_flag_training and wine_flag_test data sets. Use either Python to solve each problem. Here are the links to the datasets:
https://raw.githubusercontent.com/ArashVafa/DESC624/master/wine_flag_training.csv
https://raw.githubusercontent.com/ArashVafa/DESC624/master/wine_flag_test.csv
Create two contingency tables, one with Type and Alcohol_flag and another with Type and Sugar_flag. Use the tables in the previous exercise to calculate:
The prior probability of Type = Red and Type = White.
The probability of high and low alcohol content.
The probability of high and low sugar content.
The conditional probabilities p(Alcohol_flag = High ∣ Type = Red) and p(Alcohol_flag = Low ∣ Type = Red).
The conditional probabilities p(Alcohol_flag = High ∣ Type = White) and p(Alcohol_flag = Low ∣ Type = White).
The conditional probabilities p(Sugar_flag = High ∣ Type = Red) and p(Sugar_flag = Low ∣ Type = Red).
The conditional probabilities p(Sugar_flag = High ∣ Type = White) and p(Sugar_flag = Low ∣ Type = White).
Use the probabilities in the previous exercise to discuss
How likely it is that a randomly selected wine is red.
How likely it is that a randomly selected wine has a high alcohol content.
How likely it is that a randomly selected wine has a low sugar content.
Use the conditional probabilities found earlier to discuss
What a typical white wine might have as its alcohol and sugar content.
What a typical red wine might have as its alcohol and sugar content.
Create side‐by‐side bar graphs for Type, one with an overlay of Alcohol_flag and the other with an overlay of Sugar_flag. Compare the graphs to the conditional probabilities you calculated.
Compute the posterior probability of Type = Red for a wine that is low in alcohol content and high in sugar content. Compute the posterior probability of Type = White for the same wine.
Use your answers to the previous exercise to determine which type, red or white, is more probable for a wine with low alcohol and high sugar content. What would the Naïve Bayes classifier classify this wine as?
Compute the posterior probability of Type = Red for a wine that is high in alcohol content and low in sugar content. Compute the posterior probability of Type = White for the same wine.
Use your answers to the previous exercise to determine which type, red or white, is more probable for a wine with high alcohol and low sugar content. What would the Naïve Bayes classifier classify this wine as?
Run the Naïve Bayes classifier to classify wines as white or red based on alcohol and sugar content.
Evaluate the Naïve Bayes model on the wines_test data set. Display the results in a contingency table. Edit the row and column names of the table to make the table more readable. Include a total row and column.
According to your table in the previous exercise, find the following values for the Naïve Bayes model:
Accuracy
Error rate
According to your contingency table, find the following values for the Naïve Bayes model:
How often it correctly classifies red wines.
How often it correctly classifies white wines.
Name a few classification algorithm, choose one and use it on a dataset of your choosing.
Apply ANN and decision tree algorithm on the wine dataset and compare the result? which one is better and why?
Code Text
RUBRIC
Excellent Quality
95-100%
Introduction 45-41 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Literature Support
91-84 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Methodology
58-53 points
Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score
50-85%
40-38 points
More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.
83-76 points
Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.
52-49 points
Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality
0-45%
37-1 points
The background and/or significance are missing. No search history information is provided.
75-1 points
Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.
48-1 points
There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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Google Collab questions using Python Project Assignment