I am new to data science and have been doing research to familiarize myself and try to find a solution to my problem but I have not come across anything that seems to fit. I am looking to learn and use Python for this process.
My situation is as follows: I have multiple error logs for the cars I am testing on. Every time there is an error (there are multiple types) I have a log that has many different characteristics of the car at that time of the error. These include things like time of day, speed, location, temperature, and many more. EDIT: If needed I could also have data for normal operation as well.
My goal is to find patterns in the characteristics that have a correlation with and could be contributing to the error. I am not looking to develop a model that will predict future errors. I am only focused on seeing the patterns themselves and understanding them, and having it become more accurate the more data I give it when we encounter the error again.
For example, I want to feed it all my data for one type of error, and have it tell me something along the lines of "When speed is above x you will likely get this error", "When car is in reverse and running at night you will likely get this error", "There is no correlation between outside temperature and this error", etc. And then proceed to do this for every type of error.
Some of the variables could be dependant on each other. Some of the variables might not be relevant/meaningful.
How can I do this in Python? Any guidance is appreciated. I have tried to be as specific as I can and will update my post if further details are needed.