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| import matplotlib.pyplot as plt | |
| from pylab import rcParams | |
| from statsmodels.tsa.seasonal import seasonal_decompose | |
| import streamlit as st | |
| import pandas as pd | |
| from datasets import load_dataset | |
| dataset = load_dataset("shouzen/final_data_sale", use_auth_token=True) | |
| 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') | |
| def load_csv_data(): | |
| tp = pd.read_csv('Final_Data_Sales.csv', iterator=True, chunksize=1000,nrows=50000) # gives TextFileReader | |
| data = pd.concat(tp, ignore_index=True) | |
| # 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...') | |
| 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) | |