Supervised Learning - Classification Week 8 Challenge

2.What are the pros and cons of knn?
->Advantages:

1.No training period,knn is called lazy learner.There is no training dataset and learns from it only at the time of making real time prediction.

2.KNN is very easy to implement:KNN is very easy to implement as the only thing to be calculated is the distance between different point on the basis of data of different featuresand this distance can easily be calculated using distance formula such as Euclidian or Manhattan.

3.As there is no training period this new data can be added at any time since it wont affect the model

Disadvantages:

1.Does not work well with large dataset as calculating distances between each data instance would be very costly.

2.Does not work well with high dimensionality as this will complicate the distance calculating process to calculate distance for each dimension.

3.Sensitive to noisy and missing data

4.Need feature scaling we need to do feature scaling before applying knn algoritham to any dataset.if we dont do so,KNN may genrate wrong prediction