⚠️ use sh setup instead of the below...

# python3 -m venv venv # FOR DEBIAN

# FOR MACOS
python3.10 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

LevelZeroAnalysis

http://drivendata.github.io/cookiecutter-data-science/#analysis-is-a-dag

RESULTS:

Methodology

This project processes a dataset of 911 incidents responcees and analyzes the time it takes for ("AMR" and "PF&R") to arrive at the scene. The purpose is in understanding the difference arrival times and how often such delays occur. The data is then further analyzed to understand the trends on a weekly basis and various statistics are computed to summarize the findings.

Initialization and Loading Dataset:

Necessary libraries (like pandas) are imported.
A few file paths and filenames are defined to specify where your dataset is located.
The dataset is loaded into a variable called data from the specified CSV file.
Arrival times are extracted and converted to a datetime format.

Merged Arrivals Analysis:

The data is filtered to capture records corresponding to two dispatch stations: "AMR" and any station that starts with "PF&R".
The earliest arrival time for each incident is computed for both these stations.
These arrival times are then merged based on the incident to calculate the difference in arrival times.
This difference (termed as wait_seconds) represents the time difference between the two stations' arrival for the same incident.
If "AMR" arrived before or at the same time as "PF&R", the wait time is set to 0.
This merged data is then exported to a CSV file.

Wait Times Analysis:

The incidents where the wait time is greater than or equal to 1 minute are filtered.
The wait times are converted to minutes for easier interpretation.
Additional filters are applied to identify incidents where the wait time is 5 minutes or more, 10 minutes or more, and 15 minutes or more.
The incidents that had a wait time of 10 minutes or more are exported to a separate CSV file.

Incidents By Week Analysis:

The week in which each incident occurred is extracted. This is done for all the filtered datasets.
The number of incidents that happened in each week is counted.
A summary table is created which shows the number of incidents that had wait times of 1+ minute, 5+ minutes, 10+ minutes, and 15+ minutes in each week.
This summary table is exported to a CSV file.

Data Visualization and Summary:

Various statistics, such as the total number of incidents and the average wait time, are calculated.
A summary of the findings is printed, which includes:
Total number of incidents.
Average wait time.
Number of incidents where the wait time was 5+ minutes, 10+ minutes, and 15+ minutes.

1. install

python3 -m venv venv
source venv/bin/activate
pip3 install --upgraded pip
pip3 install -r requirements.txt

chmod +x run

2. edit code (paths are hard-coded)

nano main.py

3. run

./run

PlebeiusGaragicus / levelzero_analysis