Add app py

app.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+from pylab import rcParams
+from statsmodels.tsa.seasonal import seasonal_decompose
+import streamlit as st
+
+st.title('Project Canada Goose')
+st.write('Mempertahankan brand "canada goose" agar tetap menjadi penjualan tertinggi (untuk 1 tahun kedepan) dengan metode time series forecasting')
+st.markdown('# All Data')
+@st.cache
+def load_csv_data():
+    data = pd.read_csv('Final_Data_Sales.csv')
+
+    # Convert data yang bukan datetime yang seperti 0000-0000 ke Datetime agar hasilnya NaT
+    data['sold_at'] = pd.to_datetime(data['sold_at'], errors='coerce')
+    data['created_at'] = pd.to_datetime(data['created_at'], errors='coerce')
+    data['shipped_at'] = pd.to_datetime(data['shipped_at'], errors='coerce')
+    data['delivered_at'] = pd.to_datetime(data['delivered_at'], errors='coerce')
+    data['returned_at'] = pd.to_datetime(data['returned_at'], errors='coerce')
+
+    # Ambil data date dari data setelahnya.
+    data.fillna(method='bfill', inplace=True)
+    return data
+
+data_load_state = st.text('Loading data...')
+# Load 10,000 rows of data into the dataframe.
+data = load_csv_data()
+st.dataframe(data)
+# Notify the reader that the data was successfully loaded.
+data_load_state.text("Ini adalah data keseluruhan dari data csv")
+
+total_data = data.shape
+st.write(f'Total Datanya adalah : {total_data}')
+
+# Data Cleaning
+data = data.dropna()
+st.write("Jumlah data setelah menghapus missing value:", len(data))
+
+#Statistika Deskriptif
+st.markdown('## Statistika Deskriptif')
+analisis = data.copy()
+analisis = analisis[['sale_price', 'cost']]
+st.table(analisis.describe())
+
+#Perbandingan Shipped, Processing, Cancelled, Complete dan Returned
+st.markdown("## Perbandingan Shipped, Processing, Cancelled, Complete dan Returned")
+# plt.figure(figsize=(10,5))
+# plt.pie(data['status'].value_counts(), labels=data['status'].unique(), autopct='%.2f%%')
+# plt.show()
+fig1, ax1 = plt.subplots()
+ax1.pie(data['status'].value_counts(), labels=data['status'].unique(), autopct='%.2f%%')
+st.pyplot(fig1)
+
+#Brand Terlaris
+st.markdown("## Brand Terlaris")
+st.write("Ini adalah top 5 brand terlaris ")
+brand = data[['product_id','product_brand', 'sale_price']]
+brand = brand.groupby(['product_id','product_brand'], as_index=False)['sale_price'].sum()
+brand = brand.sort_values('sale_price', ascending=False)
+st.table(brand.head(5))
+
+#Penjualan Tertinggi Berdasarkan Product Brand
+st.markdown("## Penjualan Tertinggi Berdasarkan Product Brand")
+def perbandingan(w, a, x, y, z):
+    plt.figure(figsize=(20, 8))
+
+    plt.subplot(221)
+    plt.grid()
+    plt.bar(w[a], w['sale_price'], label="Sale Price")
+    plt.title(y)
+
+    plt.subplot(222)
+    plt.grid()
+    plt.bar(x[a], x['sale_price'], label="Sale Price")
+    plt.title(z)
+    st.pyplot(plt)
+
+product_brand = brand
+pb = product_brand[['product_brand', 'sale_price']]
+sh = pb.sort_values('sale_price').tail(5)
+sl = pb.sort_values('sale_price').head(5)
+
+perbandingan(sh, 'product_brand', sl, 'Penjualan Tertinggi Berdasarkan Product Brand', 'Penjualan Terendah Berdasarkan Product Brand')
+
+#Visualisasi Data Sale Price
+st.markdown(' # Visualisasi Data Sale Price Khusus Untuk Canada Goose')
+cg = data.copy()
+cg= cg[['created_at','product_brand','sale_price']]
+cg_f = cg.loc[cg['product_brand'] == 'Canada Goose'] #Ambil data Canada Goose Saja
+cg_f = cg_f.sort_values('created_at')
+st.write('Sorting berdasarkan tanggal pada created_at')
+st.dataframe(cg_f)
+
+#Resampling Data to Monthly
+st.markdown('## Resampling data perbulan')
+st.write('Data sale_price disini ditampilkan dalam perbulan')
+
+cg_e = cg_f[['created_at','sale_price']] ## Ambil created at dan sale price
+cg_e = cg_e.sort_values('created_at')
+y = cg_e.set_index('created_at').resample('M').mean() ## Rata rata sale price /bulan agar data tidak lebih 'noisy' (m yang dimaksud adalah month end frequency)
+y = y.dropna() #Hapus Value Kosong
+y = y.rename_axis(None, axis=1).rename_axis('Date', axis=0) #Ubah index yang tadinya 'created_at' menjadi 'Date'
+
+st.dataframe(y.head(10)) #Tampilkan 10 data teratas saja
+
+# Classic Time Series Decomposition -> 1920
+st.markdown('## Classic Time Series Decomposition -> 1920')
+st.markdown('''
+Teknik untuk memisahkan time series menjadi trend, seasonal, dan residual menggunakan movie average, ada 2 tipe:
+
+*Additive = Trend + Seasonal + Residual*\n
+*Multiplicative = Trend * Seasonal * Residual*\n
+
+Additive dipakai **untuk trend dan seasonal yang tidak terlalu bervariasi**\n
+Multiplicative dipakai **untuk trend dan seasonal yang berubah seiring jalannya waktu**
+''')
+rcParams['figure.figsize'] = 10, 5 #Besar Figur
+decomposition = seasonal_decompose(y.copy(), model='additive',period=12)
+
+fig = decomposition.plot()
+st.pyplot(fig)
+
+#Model
+
+y_train, y_test = y[:28], y[-7:] # Pisah data untuk keperlaun model dengan 80% train dan 20% test
+
+st.markdown('# Model')
+st.markdown('## ProphetFB Model')
+from fbprophet import Prophet #Import Prophet FB Model
+
+m = Prophet()
+d = y.copy()
+d= d.reset_index()
+d = d.rename(columns={'Date' : 'ds', 'sale_price' : 'y'})
+
+model = m.fit(d)
+future = m.make_future_dataframe(periods=14, freq='M') #bisa setting periode untuk setting seberapa jauh untuk diprediksi (dalam bulan)
+forecast = m.predict(future)
+forecast = forecast.set_index('ds')
+d = d.set_index('ds')
+final_forecast = forecast['yhat']
+
+fig = plt.figure(figsize=(15,5))
+plt.title("Prediksi untuk 1 tahun kedepan dengan ProphetFB Model")
+plt.plot(d, label="Actual")
+plt.plot(final_forecast, label="Predicted")
+plt.legend(loc = 'upper left')
+st.pyplot(fig)
+
+#Arima Model
+st.markdown("## ARIMA Model")
+from pmdarima import auto_arima
+arima = auto_arima(y_train,start_p=1, start_q=1, max_p=3, max_q=3, m=12,
+                             start_P=0, seasonal=True, d=1, D=1, trace=True,
+                             error_action='ignore',  # don't want to know if an order does not work
+                             suppress_warnings=True,  # don't want convergence warnings
+                             stepwise=True)
+
+n_forecast = len(y_test) + 8
+pred= arima.predict(n_forecast,D=1,seasonal=(1,0,0))
+dates = pd.date_range(y_test.index[-1],periods=n_forecast, freq='M')
+pred= pd.Series(pred, index=dates)
+
+fig = plt.figure(figsize=(15,5))
+plt.title("Prediksi menurut arima untuk 1 tahun kedepan")
+plt.plot(y_train,label="Training")
+plt.plot(y_test,label="Test")
+plt.plot(pred,label="Pred")
+plt.legend(loc = 'upper left')
+st.pyplot(fig)
+
+
+
+
+
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+