Welcome to the Emergency Calls Data Analysis project repository. This project focuses on extracting, processing, and visualizing data from the "Emergency – 911 Calls, Montgomery County" dataset obtained from Kaggle. The primary goal is to analyze emergency call trends in Montgomery County, Pennsylvania, over multiple years.
URL for the Dataset:
https://www.kaggle.com/datasets/mchirico/montcoalert
The dataset contains records of emergency calls made to Montgomery County. It consists of 663,522 records, each with 9 attributes. The emergency calls are broadly classified into three categories: EMS, Fire, and Traffic.
Description about the attributes in the dataset:
| Attribute Name | Description |
|---|---|
| Lat | Latitude of the station |
| Lng | Longitude of the station |
| Desc | Description of the Emergency Call |
| Zip | Zipcode |
| title | Emergency Reason |
| Timestamp | Timestamp of Call (YYYY-MM-DD HH:MM:SS) |
| Twp | Township |
| Addr | Address |
| e | Dummy Variable (always 1) |
- Data Exploration: Understand the dataset's shape, attributes, and any anomalies or inconsistencies.
- Data Processing: Clean the data by handling null values, extracting critical information, and organizing it for analysis.
- Data Visualization: Create visualizations to represent trends and patterns in emergency calls across different categories and years.
pandas
NumPy
Seaborn
Matplotlib
- Dataset Understanding: Analyze the dataset's structure and attribute types.
- Anomaly Detection: Identify and address any inconsistencies or anomalies in the dataset.
- Data Cleaning: Remove null values and prepare the data for analysis.
- Data Extraction: Extract critical information related to emergency calls into separate attributes.
- Visualization: Create insightful visualizations to aid in data interpretation and analysis.
Please don't hesitate to offer suggestions, report any issues you encounter, share your feedback, or engage in any other form of communication! Your input is highly valued and appreciated.