Remove noise from the data using ML/DL techniques (Python)

Looking for an expert experienced in ML/Al and python. My task need specific things to analyze:
1. I have calculated euclidean distance for each point so now want to plot those points
2. To eliminate noisy trajectory points using Kalman filter method. This recursive method removes points at a larger distance from the fitting line. First, it considers the relationship of a given trajectory point, denoted as the covariance matrix, with previous points to evaluate the next
point. A simple linear model has been applied for extracting each trajectory fitting line. After deriving a trajectory
fitting line, the variance (σ) of two consecutive points within the trajectory is computed, and points at a distance
greater than 2σ from the fitting line are further considered as noise and then eliminated. This threshold provides
a balance between the objective of eliminating noisy trajectory points and that of keeping valuable trajectory
points
budget: 45$
duration: 2 days max
Programming language: Python
PS: code is already done for preprocessing ask for it to be done. please if you know what i am talking about gig else please value others time