Create a Weather Table with the help of Data Mining Tool WEKA
@relation weather
@attribute outlook {sunny,rainy,overcast}
@attribute temparature numeric
@attribute humidity numeric
@attribute windy {true,false}
@attribute play {yes,no}
@data
sunny,85.0,85.0,false,no
overcast,80.0,90.0,true,no
sunny,83.0,86.0,false,yes
rainy,70.0,86.0,false,yes
rainy,68.0,80.0,false,yes
rainy,65.0,70.0,true,no
overcast,64.0,65.0,false,yes
sunny,72.0,95.0,true,no
sunny,69.0,70.0,false,yes
rainy,75.0,80.0,false,yes
Procedure:
Steps:
Result:
This program has been successfully executed
Apply Pre-Processing techniques to the training data set of Weather Table
@relation weather
@attribute outlook {sunny,rainy,overcast}
@attribute temparature numeric
@attribute humidity numeric
@attribute windy {true,false}
@attribute play {yes,no}
@data
sunny,85.0,85.0,false,no
overcast,80.0,90.0,true,no
sunny,83.0,86.0,false,yes
rainy,70.0,86.0,false,yesrainy,68.0,80.0,false,yes
rainy,65.0,70.0,true,no
overcast,64.0,65.0,false,yes
sunny,72.0,95.0,true,no
sunny,69.0,70.0,false,yes
rainy,75.0,80.0,false,yes
Procedure:
Pre-processing:
Result:
This program has been successfully executed
Write a program to demonstrate Visualization for Weather . arff.
@relation weather
@attribute outlook {sunny,rainy,overcast}
@attribute temperature numeric
@attribute humidity numeric
@attribute windy {true,false}
@attribute play {yes,no}
@data
sunny,85.0,85.0,false,no
overcast,80.0,90.0,true,no
sunny,83.0,86.0,false,yes
rainy,70.0,86.0,false,yes
rainy,68.0,80.0,false,yes
rainy,65.0,70.0,true,no
overcast,64.0,65.0,false,yes
sunny,72.0,95.0,true,no
sunny,69.0,70.0,false,yes
rainy,75.0,80.0,false,yes
Procedure:
Pre-processing:
Visualization of all Weather:
Write a program to demonstrate Visualization for Weather . arff.
@relation weather
@attribute outlook {sunny,rainy,overcast}
@attribute temparature numeric
@attribute humidity numeric
@attribute windy {true,false}
@attribute play {yes,no}
@data
sunny,85.0,85.0,false,no
overcast,80.0,90.0,true,no
sunny,83.0,86.0,false,yes
rainy,70.0,86.0,false,yes
rainy,68.0,80.0,false,yes
rainy,65.0,70.0,true,no
overcast,64.0,65.0,false,yes
sunny,72.0,95.0,true,nosunny,69.0,70.0,false,yes
rainy,75.0,80.0,false,yes
Procedure:
Steps:
Input Data:
1.Code Execution:
2. Click on Classify Button:
3. Select Bayes then choose NaiveBayes.
4. After that choose the Percentage split in Test options make it 66% or 70% then
click the START option.
5. After that got the Classifier Output:
Results/ Output:
=== Summary ===
Correctly Classified Instances 3 60 %
Incorrectly Classified Instances 2 40 %
Kappa statistic 0
Mean absolute error 0.5129
Root mean squared error 0.5706
Relative absolute error 108.5002 %
Root relative squared error 116.1441 %
Total Number of Instances 5
=== Confusion Matrix ===
a b <-- classified as
3 0 | a = yes
2 0 | b = no
Dataset:
@relation COVID-19
@attribute age numeric
@attribute sex {male, female} @attribute fever numeric
@attribute cough {yes, no}
@attribute sore_throat {yes, no}
@attribute difficulty_breathing {yes, no}
@attribute covid_test_result {positive, negative}
@data
35, male, 99.5, yes, no, no, positive
42, female, 98.8, yes, yes, yes, positive
28, male, 98.0, no, yes, yes, negative
50, female, 99.2, yes, no, no, negative
Description:
The dataset "Covid.arff" contains information related to COVID-19 cases. It
includes various attributes such as age, gender, symptoms, test results, and
whether a patient tested positive or negative for COVID-19.
We aim to use the J48 algorithm to create a decision tree that can predict the
likelihood of a person testing positive for COVID-19 based on these attributes.
Procedure :
Pre-processing:
Visualize all Attribute:
Result:
This program has been successfully executed.
Dataset:
@relation MarketBasketAnalysis
@attribute Item1 {0, 1}
@attribute Item2 {0, 1}
@attribute Item3 {0, 1}
@attribute Item4 {0, 1}
@attribute Item5 {0, 1}
@data
1,1,0,1,0
1,0,1,0,1
0,1,1,1,0
1,0,0,1,1
Description:
Association rule mining is a data mining technique used to find associations,
correlations, or patterns in large datasets. In this program, we will use the Apriori
algorithm, a classic algorithm for this task. We will apply it to the "Market-basket-
analysis.arff" dataset, which typically represents transactions ina retail environment
Procedure:
Pre-Processing and Visualization:
Associate Rule(Apply Apriori Algorithm):
Results/ Output:
=== Run information ===
Scheme: weka.associations.Apriori -N 10 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -S -
1.0 -c -1
Relation: MarketBasketAnalysis Instances: 35
Attributes: 5
Item1 Item2 Item3 Item4 Item5
=== Associator model (full training set) ===
Apriori
=======
Minimum support: 0.25 (9 instances)
Minimum metric <confidence>: 0.9
Number of cycles performed: 15
Generated sets of large itemsets:
Size of set of large itemsets L(1): 10
Size of set of large itemsets L(2): 24
Size of set of large itemsets L(3): 9
Best rules found:
1. Item1=0 16 ==> Item5=1 15 <conf:(0.94)> lift:(1.43) lev:(0.13) [4]
conv:(2.74)
2. Item2=0 16 ==> Item3=1 15 <conf:(0.94)> lift:(1.37) lev:(0.12) [4]
conv:(2.51)
3. Item3=1 Item4=1 13 ==> Item2=0 12 <conf:(0.92)> lift:(2.02) lev:(0.17) [6]
conv:(3.53)
4. Item2=0 Item4=1 13 ==> Item3=1 12 <conf:(0.92)> lift:(1.35) lev:(0.09) [3]
conv:(2.04)
5. Item5=0 12 ==> Item1=1 11 <conf:(0.92)> lift:(1.69) lev:(0.13) [4]
conv:(2.74)
6. Item1=0 Item3=1 12 ==> Item5=1 11 <conf:(0.92)> lift:(1.39) lev:(0.09) [3]
conv:(2.06)
7. Item3=0 11 ==> Item2=1 10 <conf:(0.91)> lift:(1.67) lev:(0.12)
[4]
conv:(2.51)
8. Item1=0 Item2=1 11 ==> Item5=1 10 <conf:(0.91)> lift:(1.38) lev:(0.08)
[2]
conv:(1.89)
9. Item1=1 Item2=0 11 ==> Item3=1 10 <conf:(0.91)> lift:(1.33) lev:(0.07)
[2]
conv:(1.73)
10. Item4=1 Item5=1 10 ==> Item1=0 9 <conf:(0.9)> lift:(1.97) lev:(0.13)
[4]conv:(2.71)
Result:
This program has been successfully executed.
