KevinDHS/Final_Project

1import streamlit as st2import pandas as pd3import matplotlib.pyplot as plt4import seaborn as sns5 6# Add CSS style for color theme7st.markdown(8    """9    <style>10    /* Set text color */11    body {12        color: #333333;13    }14 15    /* Set background color */16    .stApp {17        background-color: #659DBd;18    }19    .divider {20        background-color: #000000; /* Black color */21    }22    </style>23    """,24    unsafe_allow_html=True25)26 27def run():28    st.markdown('<h1 style="text-align: center; color: white;">APART HELPER EDA</h1>', unsafe_allow_html=True)29    st.markdown('<p style="text-align: center;">Apart Helper is a product website designed to assist customers in choosing apartments or properties based on their preferences.</p>', unsafe_allow_html=True)30    st.divider()31 32    # Melakukan loading dataset33    data_sebelum = pd.read_csv('properties_data.csv')34    data_sesudah = pd.read_csv('data_setelah_clustering.csv', index_col=0)35 36    st.header('Exploratory Data Analysis Sebelum Cluster', divider='rainbow')37    st.subheader('Top 5 Location By Average Price')38 39    # Calculate the average price for each neighborhood40    avg_price_by_neighborhood = data_sebelum.groupby('neighborhood')['price'].mean()41 42    # Sort the average prices in descending order and select the top 543    top_5_avg_price = avg_price_by_neighborhood.sort_values(ascending=False).head(5)44 45    # Display the top 5 neighborhoods using Streamlit46    st.bar_chart(top_5_avg_price)47 48    st.subheader('Number of Bedrooms',divider='rainbow')49 50    # Group data by 'no_of_bathrooms' and count frequencies51    bathroom_counts = data_sebelum['no_of_bedrooms'].value_counts().sort_index()52 53    # Plot bar chart using Streamlit54    st.bar_chart(bathroom_counts)55 56    st.subheader('Number of Bathrooms',divider='rainbow')57 58    # Group data by 'no_of_bathrooms' and count frequencies59    bathroom_counts = data_sebelum['no_of_bathrooms'].value_counts().sort_index()60 61    # Plot bar chart using Streamlit62    st.bar_chart(bathroom_counts)63 64 65    st.header('Exploratory Data Analysis Setelah Cluster', divider='rainbow')66    # Assuming mean_data is your DataFrame containing the mean prices for each cluster67    mean_data = data_sesudah.groupby('cluster')['price'].mean().reset_index()68    mean_data['cluster'] = mean_data['cluster'].astype(int)  # Ensure 'cluster' column is of integer type69    mean_data = mean_data.round(0).astype(int)70 71    # Display the DataFrame in Streamlit72    st.dataframe(mean_data)73 74    st.subheader('Average Price by Cluste',divider='rainbow')75 76    # Assuming mean_data is your DataFrame containing the mean prices for each cluster77    mean_data = data_sesudah.groupby('cluster')['price'].mean().reset_index()78    mean_data['cluster'] = mean_data['cluster'].astype(int)  # Ensure 'cluster' column is of integer type79    mean_data = mean_data.round(0).astype(int)80 81    # Plot stacked bar chart82    st.bar_chart(mean_data.set_index('cluster'), use_container_width=True)83 84    st.subheader('Top 5 Location By Average Price Based on Price',divider='rainbow')85    # Filter data for the top 5 neighborhoods86    top_5_avg_price = avg_price_by_neighborhood.sort_values(ascending=False).head(5)87    filtered_data = data_sesudah[data_sesudah['neighborhood'].isin(top_5_avg_price.index)]88 89    # Group by 'neighborhood' and 'cluster', then calculate the average price for each group90    avg_price_by_neighborhood_cluster = filtered_data.groupby(['neighborhood', 'cluster'])['price'].mean().unstack()91 92    # Plot stacked bar chart for top 5 neighborhoods93    st.bar_chart(avg_price_by_neighborhood_cluster, use_container_width=True)    94 95 96    st.subheader('Number of Bedrooms Based on Cluster',divider='rainbow')97    # Group data by 'no_of_bathrooms' and 'cluster', then count frequencies98    bathroom_cluster_counts = data_sesudah.groupby(['no_of_bedrooms', 'cluster']).size().unstack(fill_value=0)99 100    # Plot stacked bar chart101    st.bar_chart(bathroom_cluster_counts, use_container_width=True)102 103    st.subheader('Number of Bathrooms Based on Cluster',divider='rainbow')104    # Group data by 'no_of_bathrooms' and 'cluster', then count frequencies105    bathroom_cluster_counts = data_sesudah.groupby(['no_of_bathrooms', 'cluster']).size().unstack(fill_value=0)106 107    # Plot stacked bar chart108    st.bar_chart(bathroom_cluster_counts, use_container_width=True)109 110if __name__ == '__main__':111    run()112