  The CHIME radio telescope operates in the frequency bandwidth of 400 to 800
MHz. The CHIME/FRB collaboration has a data pipeline that analyzes the data in
real time, suppresses radio frequency interferences (RFI) and searches for
FRBs. However, the RFI removal techniques work best for broadband and narrow
FRBs.We wish to create a RFI removal technique that works without making
assumptions about the characteristics of the FRB signal. In this thesis we
first explore the data of intensity generated by CHIME/FRB backend. After
becoming familiar with the structure and organisation of data we present a new
novel method for RFI removal using unsupervised machine learning clustering
techniques by using multiple beams on CHIME telescope. We are trying to use the
analogy of theory of interference for RFI removal by distinguishing near field
RFI and far field astrophysical signals in the data. We explored many
clustering techniques like K-means,DBSCAN etc but one technique called as
HDBSCAN looks particularly promising. Using HDBSCAN clustering technique we
have developed the new method for RFI removal. The removal technique upto this
point has been developed by us using 3 beams of CHIME telescope. The new novel
idea is still in it's incubatory phase and soon we will try to include more
beams for our new RFI removal method. We have visually observed that RFI has
been been considerably removed from our data. In future we are going to do more
calculations to further measure the signal to noise ratio (SNR) of the FRB
signal after RFI removal and we will use this technique to compare the SNR
measured by current RFI removal technique at CHIME/FRB data pipeline.
