This is part of documentation for a course.

This is the portfolio for Machine Learning here

This is the code for Data Exmplration here This is the summary of Data Exploration here

This is the results from the Regression Model here This is the result from the Classification model here

This is the results from the Linear Regression Model in C++ here This is the result from the Naive Bayes model in C++ here This is the document to explain my findings here

Diego Ochoa - Regression here
Dmitrii Obideiko - Classiffication here
Samuel Ofiaza - Clustering here
Jovanni Ochoa(me) - PCA and LCA here
Ved Nigam - Manager here

This is the result from the ML with sklearn Model here

This is the results for the Image Classification here

After applying machine learning techniques I can confidently say that I learned a lot more about hot to manade data to work with. Althought I don't like Machine Learning as much as fixing the data; I can say that I have found a passion as a Data Analyst thanks to the techniques I learned. I plan to continue working in the backend, but I don't think I will continue with Machine Learning. There is not doubt that Machine Learning will go far though given the new up and coming projects such as ChatGPT and others. I plan on keeping up with the changing developments by knowing how to best handle larger datasets. Thanks to these projects, I also learned where to find such datasets.

1. Python
2. R
3. SQL
4. C++

1. Communication
2. Scheduling
3. Time and project management
4. Analytical thinking
5. Teamwork