Update README.md to include pip install

README.md

@@ -6,7 +6,7 @@ LogLead is designed to efficiently benchmark log anomaly detection algorithms an
 Currently, it features nearly 1,000 unique anomaly detection combinations, encompassing 8 public datasets, 11 log representations (enhancers), and 11 classifiers. These resources enable you to benchmark your own data, log representation, or classifier against a diverse range of scenarios. LogLead is an actively evolving project, and we are continually adding new datasets, representations, and classifiers. If there's something you believe should be included, please submit a request for a dataset, enhancer, or classifier in the [issue tracker](https://github.com/EvoTestOps/LogLead/issues).
 
 A key strength of LogLead is its custom loader system, which efficiently isolates the unique aspects of logs from different systems. This design allows for a reduction in redundant code, as the same enhancement and anomaly detection code can be applied universally once the logs are loaded. 
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 ## Installing LogLead
 
 Simply install with `pip`:
@@ -21,7 +21,7 @@ Install them manually (preferably in a conda enviroment).
 ### Known issues
 
 - If `scikit-learn` wheel fails to compile, check that you can `gcc` and `g++` installed.
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 ## Demos
 In the following demonstrations, you'll notice a significant aspect of LogLead's design efficiency: code reusability. Both demos, while analyzing different datasets, share a substantial amount of their underlying code. This not only showcases LogLead's versatility in handling various log formats but also its ability to streamline the analysis process through reusable code components.