This repository contains the code and resources for an anomaly detection project. Anomaly detection is the identification of rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
# Clone the repository
git clone https://github.com/yourusername/anomaly-detection-project.git
# Change directory
cd anomaly-detection-project
# Initiate Jupyter Lab or other notebook style IDE
# Import modules
Ensure modules are imported properly in final_report notebook
acquire
prep
explore
# Load data
def acquire_curlogs()
or
def acquire(file_name)
# Run the Final Notebook
# Visualize results
## Data
The data utilized was given by the codeup team and was based on logs representing access on the curriculum across all program types endpoints and users of each program.
## Explorations
Used pandas, seaborn, and matplot lib to research and identified anomalies using probalistic methods with discrete features and use of bollinger bands on the plotted data to show activities passed the upper band.
## Contributors
Theodore Quansah
## Acknowledgments
Codeup instructors