Teamname : ML101 #13
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Teamname : ML101 #13
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Team Members:
Description
Preprocessing
We replaced all the missing values with mean first and plotted their histograms.
We could notice that as there were so many NaN values, the frequencies of respective mean data saw a huge spike.
To avoid that we tried the same with KNN (K=5). The resulting graphs were fairly smooth so we decided to go with it.
Regression
Categorical data were avoided for linear regression.
Classification
We tried various algorithms starting with Bayes Classifier( Accuracy was 64%).
Next we tried random forest
@Techtronics21