Implement different ML algorithms with open source data files

[eaglewarrior]

1. Star it

2. Fork the repo

3. Clone it onto your PC.

4. Create a folder with your GitHub username

5. Create separate files for all the issues you are solving and always open an issue that has all details of the process or method you will use to perform anomaly detection and wait till it is assigned (not more than 2-3 hours it will take, we are passionate open source developers )

6. Open PRs for the issues you are solving. (You can open multiple PRs for different issues by branching).

7. Make sure the data is only from the given category (No repetitions of same data )

          a. Healthcare -covid, heart attack, cancer, etc
          
          b. Finance -stocks etc
          
          c. Retail or CPG
          
          d. Image classifciation
          
          e. Time series
   

Only code like .py are not accepted please push proper jupyter (.ipynb) files with problem statement and solution analysis.

Note: Don't upload zip , please upload in a separate folder and we request to follow the given instructions in the repository

[gauriimaheshwarii]

Hi! Can I work on this issue?

[eaglewarrior]

@gauriimaheshwarii yes please follow the instructions and raise pull request

[gauriimaheshwarii]

Hi @eaglewarrior, tried implementing an ML algorithm.
Jupyter notebook (.ipynb) files are attached.

Kindly merge my PR #46.
Thanks!!