lynnemunini/safecity-analysis

This repository is responsible for analyzing crime reports, with the aim of identifying common patterns and providing insights to help identify high-risk areas and predict the likelihood of crimes happening in specific locations.

🚨 SafeCity Data Analysis Repository 🚨

This repository is responsible for analyzing crime reports, with the aim of identifying common patterns and providing insights to help identify high-risk areas and predict the likelihood of crimes happening in specific locations. The analysis is done using a dataset of crime reports from the city of Nairobi.

📝 Project Description

This project utilizes data analysis techniques to investigate crime patterns and trends, in order to provide valuable insights for public safety officials and the general public. By analyzing crime data from various neighborhoods, we can identify areas that are more prone to crime and predict the likelihood of future incidents.

⚠️ Important Note

The data used in this project is purely fictional and was generated using the Faker and GeoPy libraries. This is intended solely as a proof of concept. Please do not use the data for any other purpose.

📊 Data

The data used for this project is contained in a CSV file named fake_crime_reports.csv. The file contains rows, each representing a fake crime report with the following attributes:

Category
Latitude
Longitude
Location Name
Date
Victim Gender
Victim Age
Suspect Gender
Demographic
Weather

📈 Results

The analysis performed on the data revealed interesting insights into crime patterns in the city of Nairobi. I was able to identify the most common types of crimes reported. Additionally, I was able to predict the likelihood of crimes happening in specific locations.

📚 Repository Structure

analysis.ipynb: Jupyter notebook containing the data analysis code.
fake_crime_reports.csv: CSV file containing the fake crime reports used for the analysis.
requirements.txt: Text file containing the list of Python packages required to run the analysis.
reportsgenerator.py: Python script used to generate the fake crime reports.
data.json: JSON file containing the analysis results.
README.md: This file, containing information about the project.

🌐 Links

Here is the link to the GitHub repository with the SafeCity Android app: SafeCity Android App

📜 License

This project is licensed under the Apache-2.0 license - see the LICENSE file for details.

📝 Conclusion

Overall, this analysis provides valuable insights into crime patterns in the city of Nairobi. The findings can be used by public safety officials to identify high-risk areas and allocate resources accordingly. Additionally, the analysis can help the general public stay informed about crime trends and take proactive measures to protect themselves and their communities.