Add Figures and Analysis

app.py

@@ -10,39 +10,46 @@ if 'sample_data' not in st.session_state:
 	client = Socrata("data.cityofnewyork.us", None)
 
 	query = "INCIDENT_DATETIME >= '2024-03-01T00:00:00' AND INCIDENT_DATETIME < '2024-04-01T00:00:00'"
-	results = client.get("76xm-jjuj", where=query, limit=100)
+	results = client.get("76xm-jjuj", where=query, limit=200)
 
 	data = pd.DataFrame.from_records(results)
 	data.columns = data.columns.str.upper()
-	data.dropna(inplace=True)
+	data.dropna(inplace=True, ignore_index=True)
+	
+	data.drop(labels=['CAD_INCIDENT_ID'], axis=1, inplace=True)
 	st.session_state.sample_data = data
 
 sample_data = st.session_state.sample_data
 
 st.title('EMS Call Classifier')
-st.write("This project aims to improve the accuracy of predicting the nature of emergency calls in NYC, thereby improving emergency response times. It utilizes historical EMS dispatch data and real-time weather conditions to predict call types. The project's ultimate goal is to get New Yorkers the help they need even faster.")
+st.write("This project aims to improve the accuracy of predicting the nature of emergency calls in NYC, thereby improving emergency response times. It utilizes historical EMS dispatch data to predict call types, ultimately allowing New Yorkers to get the help they need even faster.")
 
 st.header('The Data')
 st.write('Provided through the NYC Open Data.')
-t1, t2 = st.tabs(['EMS Incident Dispatch', 'Weather'])
+
+t1 = st.tabs(['EMS Incident Dispatch'])[0]
 t1.markdown(
 """The [EMS Incident Dispatch Data](https://data.cityofnewyork.us/Public-Safety/EMS-Incident-Dispatch-Data/76xm-jjuj/about_data) is generated by the EMS Computer Aided Dispatch System, and covers information about the incident as it relates to the assignment of resources and the Fire Department’s response to the emergency.
 
 The 6GB of data spans from April 2008 to October 2024 and employs the use of over 140 distinct call types (e.g. “Eye Injury” or “Cardiac Arrest”).
 """)
 
-t2.write("The Weather Data comes from...")
-
-t1.subheader('Sample of First 100 Incidents from March 2024')
+t1.subheader(f'Sample of First {sample_data.shape[0]-1} Incidents from March 2024')
 t1.dataframe(sample_data, use_container_width=True)
 
 st.header("Analysis")
-st.write("A visualization of an insight we gained from exploring and analyzing the data.")
+st.write("When analyzing our data, we realized that around half our types rarely were ever used for typing a case. Around 20 types alone were only represented once in the dataset.")
+
+st.image("figures/FrequencyFinalCallTypeCounts.png", caption='Most types have very rare occurrences in our data.', use_column_width=True)
+
+st.write("We determined that types with less than 0.05% frequency were insignificant and would only throw off our model. Thus, entries with these rare types were dropped.")
 
 st.header("Our Model")
 st.markdown(
 """
 Our model utilizes a Random Forest Multiclassifier enhanced with Gradient Boosting. We selected key features like time of day, day of week, borough, police precinct, and zip code, which proved most relevant in predicting the nature of an EMS dispatch incident. Initial call type and initial severity level were also included to provide a baseline for our predictions.
+
+In our training, we used a May 2024 to June 2024 data subset with over 270k incidents after dropping around 5k rows of incomplete entries and rare types.
 """	
 )