arima model.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import OrdinalEncoder
import warnings
warnings.filterwarnings("ignore")


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df=pd.read_csv(r"""C:\Users\sooja\Downloads\Untitled spreadsheet - Sheet1.csv""")


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df


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df.drop(df.columns[[5]],axis = 1,inplace=True)


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df.info()


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bool_series = pd.isnull(df["avg price"])


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bool_series


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df['orderdate']=pd.to_datetime(df['orderdate'])


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import seaborn as sns


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sns.boxplot(df['avg price'])


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sns.boxplot(df['ordercount'])


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filter1=df['ordercount']<50000
df.where(filter1,inplace=True)


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sns.boxplot(df['ordercount'])


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enc=OrdinalEncoder()


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df['prod']=enc.fit_transform(df[['prod_name']]).astype(int)


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from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
import statsmodels.graphics.tsaplots as sm


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df.set_index('orderdate',inplace=True)


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df['pricediff']=df['avg price']-df['avg price'].shift(1)


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df['ordercountdif']=df['ordercount']-df['ordercount'].shift(1)


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fig=plt.figure(figsize=(12,8))
ax1=fig.add_subplot(211)
fig=sm.plot_acf(df['pricediff'].dropna(),lags=40,ax=ax1)

ax2=fig.add_subplot(212)
fig=sm.plot_pacf(df['pricediff'].dropna(),lags=40,ax=ax1)


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fig=plt.figure(figsize=(12,8))
ax1=fig.add_subplot(211)
fig=sm.plot_acf(df['ordercountdif'].dropna(),lags=40,ax=ax1)

ax2=fig.add_subplot(212)
fig=sm.plot_pacf(df['ordercountdif'].dropna(),lags=40,ax=ax1)


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from statsmodels.tsa.arima_model import ARIMA


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model=ARIMA(df['avg price'],order=(1,1,0))
model_fit=model.fit()


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model_fit.summary()


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df.info()


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df['priceforecast']=model_fit.predict(start=753,end=897)


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df['priceforecast'].plot(figsize=(12,6))


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df['priceforecast']


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model1=ARIMA(df['ordercount'],order=(1,1,1))
model_fit1=model.fit()


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model_fit1.summary()


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df['dforecast']=model_fit.predict(start=753,end=897)


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df['dforecast'].plot(figsize=(12,6))


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df['dforecast']


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