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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import streamlit as st |
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import warnings |
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warnings.filterwarnings("ignore") |
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@st.cache_data |
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def datu(): |
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return pd.read_csv("Rain_Forecast.csv") |
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data = datu() |
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data = data.set_index(data.Date) |
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data.drop(columns = ["Date"], inplace = True) |
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train = data.iloc[200:] |
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from statsmodels.tsa.statespace.sarimax import SARIMAX |
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forecast_model=SARIMAX(train,order=(1,1,1),seasonal_order=(1,1,1,12)) |
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forecast_model=forecast_model.fit() |
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