Delete main.py

main.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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