Teamname : ML101 #13
Open
Teamname : ML101 #13
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
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