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# Feature Selection With Python (Other (Not Listed) Sample)

Instructions:

Quiz 2 – Feature Selection
Feature selection methods in Python
Instructions
Work in Python
1. Look at the shape of the dataset and print out the number of features. (gnd is the target variable)
2. Apply the Linear Regression model on the given dataset and get the accuracy of the model.
3. Remove low variance features set the threshold = 0.1, 0.2, 0.3, and 0.4
4. Print out the new datasets with selected features.
5. Apply the Linear Regression model on each of these datasets and get the accuracy scores.
6. Compare the accuracy scores and define does the feature selection improve the model or not.
7. Show the of accuracy scores on a plot (line chart).
Submission Instructions:
Please pay attention to the following tips.
• This quiz is an individual exercise.
• Please submit your work before the due date, August 2nd, by the end of the day.

source..
Content:

Feature Selection with Python
Name of Student
Name of Instructor
Name of Institution
Course
Date
Feature Selection with Python
Question One
The shape of the dataset is 800 rows and 258 columns. The number of features is 256, and the target variable (gnd) feature totals 257.

Question Two.
The dataset is split into training and validation testing data to achieve high accuracy of the logistic regression model. The model attained an accuracy of 98.75 percent.

Question Three.
Removing low variance features and setting the threshold to 0.1, 0.2, 0.3, and 0.4.

Question Four.
The new datasets with selected features.
Output exceeds the size limit. Open the full output data in a text editor
Threshold = 0.1
fea.5 fea.6 fea.7 fea.8 fea.9 fea.10 fea.11 \
0 -0.774192 -0.275283 0.034194 -0.399883 -0.815319 -0.955680 -0.989120
1 -0.901515 -0.545999 0.030940 0.411866 0.390567 0.008434 -0.502208
2 -0.961828 -0.717215 -0.144694 0.092191 -0.260683 -0.687438 -0.925676
3 -0.832602 -0.368054 -0.080497 -0.246663 -0.263954 -0.317376 -0.699568
4 -0.988678 -0.829627 -0.330878 0.013998 -0.345315 -0.803120 -0.973332
fea.19 fea.20 fea.21 ... fea.244 fea.245 fea.246 fea.247 \
0 -0.987214 -0.844745 -0.336824 ... -0.183571 0.170864 0.365778 0.414430
1 -0.997706 -0.952953 -0.659959 ... -0.979200 -0.858993 -0.527557 -0.043773
2 -0.986465 -0.932243 -0.783630 ... -0.894444 -0.636530 -0.257442 0.153603
3 -0.999915 -0.992487 -0.873086 ... -0.942932 -0.684969 -0.164910 0.306974
4

...
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