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.gitattributes

@@ -33,3 +33,5 @@ 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
+all_dataset.csv filter=lfs diff=lfs merge=lfs -text
+erica-zhou-IHpUgFDn7zU-unsplash.jpg filter=lfs diff=lfs merge=lfs -text

ProjectAkhir.py

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+# mengimpor seluruh library
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import streamlit as st
+from babel.numbers import format_currency
+sns.set(style='dark')
+
+# create_daily_orders() digunakan untuk menyiapkan daily_orders
+
+
+def create_orders_daily(df):
+    create_orders_daily = df.resample(rule='M', on='order_approved_at').agg({
+        "order_id": "nunique",
+        "price": "sum"
+    })
+    create_orders_daily.index = create_orders_daily.index.strftime(
+        '%Y-%m-%d')  # mengubah format order date menjadi nama bulan
+    create_orders_daily = create_orders_daily.reset_index()
+    create_orders_daily.rename(columns={
+        "order_id": "order_count",
+        "price": "revenue"
+    }, inplace=True)
+
+    return create_orders_daily
+
+
+# create_sum_order() untuk menyiapkan sum_orders
+def create_sum_order(df):
+    sum_order = df.groupby("product_category_name").product_id.nunique(
+    ).sort_values(ascending=False).reset_index()
+    return sum_order
+
+
+# create_state() digunakan untuk menyiapkan state
+def create_state(df):
+    state = df.groupby(
+        by="customer_city").product_id.nunique().reset_index()
+
+    return state
+
+# create_order_status() digunakan untuk menyiapkan order_statuse
+
+
+def create_order_status(df):
+    order_status = df.groupby(
+        by="order_status").product_id.nunique().reset_index()
+
+    return order_status
+
+
+# Menyimpan berkas data dari google colba.
+all_dataset = pd.read_csv("all_dataset.csv")
+
+# Filter untuk kolom order_approved_at dan order_status
+datetime_columns = ["order_approved_at", "order_status"]
+all_dataset.sort_values(by="order_approved_at", inplace=True)
+all_dataset.reset_index(inplace=True)
+
+all_dataset_index = all_dataset.set_index('order_approved_at')
+
+for column in datetime_columns:
+    all_dataset[column] = pd.to_datetime(all_dataset['order_approved_at'])
+
+# membuat filter dengan widget date input serta menambahkan logo perusahaan pada sidebar
+min_date = all_dataset["order_approved_at"].min()
+max_date = all_dataset["order_approved_at"].max()
+
+with st.sidebar:
+    # Menambahkan logo perusahaan
+    st.title("Toko Kita")
+    st.image("https://raw.githubusercontent.com/MuhamdIlyas/ProjectDicodingDataScience/a773b6e2b6b6b1a890c7ccf635de83fc9b487de2/erica-zhou-IHpUgFDn7zU-unsplash.jpg",
+             width=None, use_column_width=None)
+    # https://unsplash.com/photos/IHpUgFDn7zU
+
+    # Mengambil start_date & end_date dari date_input
+    start_date, end_date = st.date_input(
+        label='Rentang Waktu', min_value=min_date,
+        max_value=max_date,
+        value=[min_date, max_date]
+    )
+
+# start_date dan end_date di atas akan digunakan untuk memfilter all_dataset. Data yang telah difilter ini selanjutnya akan disimpan dalam main_dataset
+main_dataset = all_dataset[(all_dataset["order_approved_at"] >= str(start_date)) &
+                           (all_dataset["order_approved_at"] <= str(end_date))]
+
+daily_order = create_orders_daily(main_dataset)
+sum_order = create_sum_order(main_dataset)
+state = create_state(main_dataset)
+order_status = create_order_status(main_dataset)
+# rfm_df = create_rfm_df(main_dataset)
+
+# Menambahkan Header
+st.header('Toko Kita Dashboard :sparkles:')
+
+# menampilkan informasi total order dan revenue dalam bentuk metric() yang ditampilkan menggunakan layout columns()
+st.subheader('Daily Orders')
+
+col1, col2 = st.columns(2)
+
+with col1:
+    total_orders = daily_order.order_count.sum()
+    st.metric("Total orders", value=total_orders)
+
+with col2:
+    total_revenue = format_currency(
+        daily_order.revenue.sum(), "AUD", locale='es_CO')
+    st.metric("Total Revenue", value=total_revenue)
+
+# Perfoma penjualan
+fig, ax = plt.subplots(figsize=(16, 8))
+ax.plot(
+    daily_order["order_approved_at"].values,
+    daily_order["order_count"].values,
+    marker='o',
+    linewidth=2,
+    color="#90CAF9"
+)
+ax.tick_params(axis='y', labelsize=20)
+ax.tick_params(axis='x', labelsize=15, rotation=45)
+
+st.pyplot(fig)
+
+# Menampilkan 5 produk paling laris dan paling sedikit terjual melalui sebuah visualisasi data
+st.subheader("Best & Worst Performing Product")
+
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(35, 15))
+
+colors = ["#90CAF9", "#D3D3D3", "#D3D3D3", "#D3D3D3", "#D3D3D3"]
+
+sns.barplot(x="product_id", y="product_category_name",
+            data=sum_order.head(5), palette=colors, ax=ax[0])
+ax[0].set_ylabel(None)
+ax[0].set_xlabel(None, fontsize=30)
+ax[0].set_title("Best Performing Product", loc="center", fontsize=50)
+ax[0].tick_params(axis='y', labelsize=35)
+ax[0].tick_params(axis='x', labelsize=30)
+
+sns.barplot(x="product_id", y="product_category_name", data=sum_order.sort_values(
+    by="product_id", ascending=True).head(5), palette=colors, ax=ax[1])
+ax[1].set_ylabel(None)
+ax[1].set_xlabel(None, fontsize=30)
+ax[1].invert_xaxis()
+ax[1].yaxis.set_label_position("right")
+ax[1].yaxis.tick_right()
+ax[1].set_title("Worst Performing Product", loc="center", fontsize=50)
+ax[1].tick_params(axis='y', labelsize=35)
+ax[1].tick_params(axis='x', labelsize=30)
+
+st.pyplot(fig)

all_dataset.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fcedf26d8a0e8103cca6d3d89cfc389797674eede60cd042b7713d96c01f47e3
+size 50361516

requirements.txt

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+pandas==2.1.1
+matplotlib==3.2.2
+seaborn==0.11.2
+streamlit==1.3.0
+babel==2.13.0