Update app.py

app.py

@@ -69,8 +69,3 @@ fig = px.choropleth(
 # Display the map
 st.plotly_chart(fig)
 
-st.write("""I began by acquiring a dataset on child mortality rates, with countries as rows and years as columns. The dataset contained child mortality rates as the number of deaths per 1,000 live births.
-To make the dataset suitable for visualization, I transformed it into a long format using pandas.melt(), creating three columns: country, year, and mortality_rate. This step allowed for efficient filtering and visualization.
-I chose a choropleth map because it effectively communicates regional differences using a color gradient. Each country is color-coded based on its mortality rate for a selected year, offering immediate visual insights.
-I implemented a slider widget for year selection, enabling users to dynamically explore mortality rates over time.
-This required ensuring that the year column was properly formatted as numeric data, and filtering the dataset based on the slider’s value.""")