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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+train_users_2.csv filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import pycountry
+import plotly.graph_objects as go
+import plotly.figure_factory as ff
+import pycountry_convert as pc
+import pandas as pd
+import numpy as np
+
+import pycountry
+import pycountry_convert as pc
+user = pd.read_csv('train_users_2.csv')
+user = user[user['age'] < 100]
+user["year"] = user["date_account_created"].str[:4].astype(int)
+user["month"] = user["date_account_created"].str[5:7].astype(int)
+user['year-month'] = user['date_account_created'].str[:7]
+user['date_first_booking'] = user['date_first_booking'].replace(np.nan, '2020-13-31')
+
+user['month_booking'] = user['date_first_booking'].str[5:7].astype(int) 
+user['year_booking'] = user['date_first_booking'].str[:4].astype(int)
+user['year-month_booking'] = user['date_first_booking'].str[:7]
+user["language"] = user["language"].str.upper()
+
+df = user
+external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
+st.set_page_config(layout="wide")
+st.title("Airbnb New User Bookings")
+col0, col1, col2 = st.columns([0.5, 2, 2])  # Adjust the width ratios here
+with col0:
+    region = st.selectbox("Select Region", df['language'].unique(), index=0)
+    column = st.selectbox("Select Column", ['first_device_type', 'first_browser','affiliate_provider'], index=0)
+    year = st.slider("Select Year", df['year'].min(), df['year'].max(), df['year'].max())
+with col1:
+    # Update graph based on column and year selection
+    filtered_df = df[(df['year'] == year)]
+    visit = filtered_df[column].value_counts()
+    booking = filtered_df[filtered_df['year_booking']==year][column].value_counts()
+    counts = pd.merge(visit, booking, left_index=True, right_index=True).reset_index()
+    counts.columns = ['first_device_type', 'visit', 'booking']
+    counts['rate'] = counts['booking'] / counts['visit']
+    counts_t = counts.melt(id_vars=['first_device_type'], value_vars=['visit', 'booking'])
+    fig = px.bar(counts_t, x="first_device_type", y="value", color='variable')
+    fig.update_layout(yaxis2=dict(overlaying='y', side='right', range=[0, 1]))
+    fig.add_trace(go.Scatter(x=counts['first_device_type'], y=counts['rate'], mode='lines+markers', name='Conversion Rate', yaxis='y2'))
+    fig.update_layout(height=250, margin={'l': 20, 'b': 50, 't': 10, 'r': 10}, hovermode='closest')
+    st.plotly_chart(fig)
+
+    # Update new-users figure based on region selection
+    dff = df[df['language'] == region]
+    visit = dff.groupby(['year', 'month'])['year-month'].value_counts().reset_index(name='count')
+    visit = visit.iloc[:, 2:]
+    booking = dff.groupby(['year_booking', 'month_booking'])['year-month_booking'].value_counts().reset_index(name='count')
+    booking = booking[booking['year_booking'] != 2020]
+    booking = booking.iloc[:, 2:]
+    counts = pd.merge(visit, booking, left_on=['year-month'], right_on=['year-month_booking'])
+    counts.columns = [ 'year-month', 'visit',  'year-month_booking', 'booking']
+    counts['rate'] = counts['booking'] / counts['visit']
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=counts['year-month'], y=counts['visit'], mode='lines', name='visit'))
+    fig.add_trace(go.Scatter(x=counts['year-month'], y=counts['booking'], mode='lines', name='booking'))
+    fig.update_layout(yaxis2=dict(overlaying='y', side='right', range=[0, 1]))
+    fig.add_trace(go.Scatter(x=counts['year-month'], y=counts['rate'], mode='lines+markers', name='Conversion Rate', yaxis='y2'))
+    fig.update_layout(height=250, margin={'l': 20, 'b': 30, 'r': 10, 't': 10})
+    st.plotly_chart(fig)
+
+with col2:
+    # Update destination-country figure based on year selection
+    dff = df.loc[(df["country_destination"]!="NDF") & (df["country_destination"]!="other")]
+    dff = dff[dff['year'] == year]
+    counts = dff['country_destination'].value_counts()
+    counts = pd.DataFrame(counts)
+    counts.reset_index(inplace=True)
+    counts.columns = ['country_destination', 'count']
+    counts['country'] = counts['country_destination'].apply(lambda x: pycountry.countries.get(alpha_2=x).alpha_3)
+    counts['continent'] = counts['country_destination'].apply(lambda x: pc.convert_continent_code_to_continent_name(pc.country_alpha2_to_continent_code(x)))
+    fig = px.scatter_geo(counts, locations="country", color="continent", size='count', hover_name=counts['country'])
+    fig.update_traces(customdata=counts['country'])
+    title = '<b>{}</b>'.format(year)
+    fig.add_annotation(x=0, y=0.85, xanchor='left', yanchor='bottom', xref='paper', yref='paper', showarrow=False, align='left', text=title)
+    fig.update_layout(height=250, margin={'l': 20, 'b': 30, 'r': 10, 't': 10})
+    fig.update_layout(clickmode='event+select')
+    st.plotly_chart(fig)
+    # Update new-users-age figure based on selected country
+    country_name = st.selectbox("Select Country", df['country_destination'].unique(), index=0)
+    dff = df[df['country_destination'] == country_name]
+    dff = dff[dff['year'] == year]  # Modify this line to match your data
+    dff = dff[dff['gender'] != '-unknown-']
+    fig = px.violin(dff, x="gender", y="age", box=True, color="gender", violinmode='overlay')
+    title = '<b>{}</b>'.format(year)
+    fig.add_annotation(x=0, y=0.85, xanchor='left', yanchor='bottom', xref='paper', yref='paper', showarrow=False, align='left',
+                    text=title)
+    fig.update_layout(height=225, margin={'l': 20, 'b': 30, 'r': 10, 't': 10})
+    st.plotly_chart(fig)
+
+

requirements.txt

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+streamlit
+pandas
+plotly
+pycountry
+plotly
+pandas
+numpy
+pycountry
+pycountry-convert

train_users_2.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d57dabe8b06534e51f86520ce6a66b6f67cce1704fb77a3fcfb596124858818
+size 24853881