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dashboard.py
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# -*- coding: utf-8 -*-
"""Untitled14.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1s-pFIY8xxyP9zCCEjeI7v74WatXkzNeL
"""
import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
order_reviews = pd.read_csv('https://raw.githubusercontent.com/yusuf601/BELAJAR/main/order_reviews_dataset.csv')
orders = pd.read_csv('https://raw.githubusercontent.com/yusuf601/BELAJAR/main/orders_dataset.csv')
order_reviews.info()
order_reviews["review_comment_message"].fillna("", inplace=True)
order_reviews["review_comment_title"].fillna("", inplace=True)
orders["order_delivered_customer_date"] = pd.to_datetime(orders["order_delivered_customer_date"])
orders["order_estimated_delivery_date"] = pd.to_datetime(orders["order_estimated_delivery_date"])
order_reviews["review_creation_date"] = pd.to_datetime(order_reviews["review_creation_date"])
order_reviews["review_answer_timestamp"] = pd.to_datetime(order_reviews["review_answer_timestamp"])
order_reviews["customer_satisfaction"] = order_reviews.apply(lambda x: "puas" if x["review_score"] >= 4 and x["review_comment_message"] else "tidak puas", axis=1)
st.set_page_config(layout="wide")
with st.container():
st.title("Dashboard analisis data ")
# Membuat selectbox untuk memilih nilai k
k = st.sidebar.selectbox("Pilih nilai k", [2, 3, 4, 5])
k_options = ["2 (recommended)", "3", "4 ⚠️", "5 ⚠️"]
k = st.sidebar.selectbox("Pilih nilai k", k_options)
k = int(k[0])
if k > 3: st.warning("Nilai k yang tinggi akan menyebabkan overfitting.")
# Menampilkan data order_reviews
st.write("Data order_reviews")
st.dataframe(order_reviews)
# Ubah nilai customer_satisfaction menjadi numerik
order_reviews["customer_satisfaction"] = order_reviews["customer_satisfaction"].map({"puas": 1, "tidak puas": 0})
X = order_reviews[["review_score", "customer_satisfaction"]]
st.write("Pusat cluster awal:")
centroids = X.sample(k)
st.dataframe(centroids)
labels = np.zeros(len(X))
iterations = 0
stop = False
while not stop:
distances = np.sqrt(((X - centroids.iloc[0])**2).sum(axis=1))
for i in range(1, k):
distances = np.c_[distances, np.sqrt(((X - centroids.iloc[i])**2).sum(axis=1))]
new_labels = np.argmin(distances, axis=1)
if np.array_equal(labels, new_labels):
stop = True
else:
labels = new_labels
centroids = X.groupby(labels).mean()
iterations += 1
st.write(f"Iterasi ke-{iterations}:")
st.dataframe(centroids)
st.write("Hasil akhir:")
st.dataframe(centroids)
st.write(f"Jumlah iterasi: {iterations}")
fig, ax = plt.subplots()
ax.scatter(X["review_score"], X["customer_satisfaction"], c=labels, cmap="rainbow")
ax.scatter(centroids["review_score"], centroids["customer_satisfaction"], marker="*", s=200, c="black")
ax.set_xlabel("review_score")
ax.set_ylabel("customer_satisfaction")
ax.set_title("Clustering dengan K-means sederhana")
st.pyplot(fig)
# Membuat kontainer untuk menampilkan data orders
with st.container():
# Menampilkan data orders
st.write("Data orders")
st.dataframe(orders)
orders["delivery_difference"] = orders["order_delivered_customer_date"] - orders["order_estimated_delivery_date"]
st.write("Persentase pesanan yang terkirim")
st.write(orders["order_status"].value_counts(normalize=True)["delivered"] * 100)
avg_diff = orders.groupby("customer_id")["delivery_difference"].mean()
fig, ax = plt.subplots()
ax.bar(avg_diff.index, avg_diff.values)
ax.set_title("Rata-Rata Delivery Difference per Customer ID")
ax.set_xlabel("Customer ID")
ax.set_ylabel("Delivery Difference")
ax.set_xticks(avg_diff.index)
ax.set_xticklabels(avg_diff.index, rotation=90)
st.pyplot(fig)
# Membuat bagian yang dapat diperluas untuk menampilkan data order_reviews
with st.expander(label="Data order_reviews", expanded=True):
# Membuat selectbox untuk memilih nilai k
k = st.sidebar.selectbox("Pilih nilai k", [2, 3, 4, 5])
k_options = ["2 (recommended)", "3", "4 ⚠️", "5 ⚠️"]
k = st.sidebar.selectbox("Pilih nilai k", k_options)
k = int(k[0])
if k > 3: st.warning("Nilai k yang tinggi akan menyebabkan overfitting.")
# Menampilkan data order_reviews
st.write("Data order_reviews")
st.dataframe(order_reviews)
order_reviews["customer_satisfaction"] = order_reviews["customer_satisfaction"].map({"puas": 1, "tidak puas": 0})
X = order_reviews[["review_score", "customer_satisfaction"]]
st.write("Pusat cluster awal:")
st.dataframe(centroids)
labels = np.zeros(len(X))
iterations = 0
# Menampilkan data order_reviews
st.write("Data order_reviews")
st.dataframe(order_reviews)
col1, col2 = st.columns(2, width=6)
with col1:
k = st.sidebar.selectbox("Pilih nilai k", [2, 3, 4, 5])
k_options = ["2 (recommended)", "3", "4 ⚠️", "5 ⚠️"]
k = st.sidebar.selectbox("Pilih nilai k", k_options)
k = int(k[0])
if k > 3: st.warning("Nilai k yang tinggi akan menyebabkan overfitting.")
# Menampilkan data order_reviews
st.write("Data order_reviews")
st.dataframe(order_reviews)
# Ubah nilai customer_satisfaction menjadi numerik
order_reviews["customer_satisfaction"] = order_reviews["customer_satisfaction"].map({"puas": 1, "tidak puas": 0})
X = order_reviews[["review_score", "customer_satisfaction"]]
centroids = X.sample(k, random_state=42)
st.write("Pusat cluster awal:")
st.dataframe(centroids)
labels = np.zeros(len(X))
iterations = 0
stop = False
while not stop:
distances = np.sqrt(((X - centroids.iloc[0])**2).sum(axis=1))
for i in range(1, k):
distances = np.c_[distances, np.sqrt(((X - centroids.iloc[i])**2).sum(axis=1))]
new_labels = np.argmin(distances, axis=1)
if np.array_equal(labels, new_labels):
stop = True
else:
labels = new_labels
centroids = X.groupby(labels).mean()
iterations += 1
st.write(f"Iterasi ke-{iterations}:")
st.dataframe(centroids)
st.write("Hasil akhir:")
st.dataframe(centroids)
st.write(f"Jumlah iterasi: {iterations}")
fig, ax = plt.subplots()
ax.scatter(X["review_score"], X["customer_satisfaction"], c=labels, cmap="rainbow")
ax.scatter(centroids["review_score"], centroids["customer_satisfaction"], marker="*", s=200, c="black")
ax.set_xlabel("review_score")
ax.set_ylabel("customer_satisfaction")
ax.set_title("Clustering dengan K-means sederhana")
st.pyplot(fig)
# Mengisi kolom kedua dengan data orders
with col2:
# Menampilkan data orders
st.write("Data orders")
st.dataframe(orders)
orders["delivery_difference"] = orders["order_delivered_customer_date"] - orders["order_estimated_delivery_date"]
st.write("Persentase pesanan yang terkirim")
st.write(orders["order_status"].value_counts(normalize=True)["delivered"] * 100)
avg_diff = orders.groupby("customer_id")["delivery_difference"].mean()
fig, ax = plt.subplots()
ax.bar(avg_diff.index, avg_diff.values)
ax.set_title("Rata-Rata Delivery Difference per Customer ID")
ax.set_xlabel("Customer ID")
ax.set_ylabel("Delivery Difference")
ax.set_xticks(avg_diff.index)
ax.set_xticklabels(avg_diff.index, rotation=90)
st.pyplot(fig)