!pip install pystan==2.19.1.1 prophet

import numpy as np  
import pandas as pd 
import tensorflow as tf
import seaborn as sns
import matplotlib.pyplot as plt
import tensorflow as tf
import os
from sklearn.preprocessing import MinMaxScaler
from prophet import Prophet
from prophet.plot import add_changepoints_to_plot, plot_plotly, plot_components_plotly
import warnings
import plotly.graph_objects as go
import plotly.express as px
warnings.filterwarnings("ignore")
sns.set_theme()

data = pd.read_csv('/content/Final dataset - CHITTAGONG 2023-01-20 to 2023-02-18.csv')
data.head()

# Prepare data for Prophet
days = int(7)
predicted_data = pd.DataFrame()  

model1 = Prophet()

df_for_humidity=data[['Date', 'Humidity%']]
df_for_humidity['Date']=pd.to_datetime(df_for_humidity['Date'])
df_for_humidity.rename(columns = {'Date':'ds'}, inplace = True)
df_for_humidity.rename(columns = {'Humidity%':'y'}, inplace = True)
model1.fit(df_for_humidity)

future = model1.make_future_dataframe(periods = days)
future.tail(5)

forecast = model1.predict(future)
predicted_data['Date'] = forecast['ds']
predicted_data['Humidity%'] = forecast['yhat']
# forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

fig = model1.plot(forecast,figsize=(26, 6))
# fig = m.plot(forecast,figsize=(20, 12))
a = add_changepoints_to_plot(fig.gca(), model1, forecast)
plt.xlabel('Date')
plt.ylabel('Humidity')
plt.tight_layout()
plt.show()

data1 = pd.read_csv('/content/Final dataset - CHITTAGONG 2023-01-20 to 2023-02-18.csv')
data1.head()

model2 = Prophet()

df_for_temp=data1[['Date', 'Temparature(c)']]


df_for_temp

df_for_temp['Date']=pd.to_datetime(df_for_temp['Date'])
df_for_temp.rename(columns = {'Date':'ds'}, inplace = True)
df_for_temp.rename(columns = {'Temparature(c)': 'y'}, inplace = True)


df_for_temp

model2.fit(df_for_temp)

future = model2.make_future_dataframe(periods = days)
future.tail(5)


forecast = model2.predict(future)
predicted_data['Temparature(c)'] = forecast['yhat']
# forecast = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

fig = model2.plot(forecast,figsize=(26, 6))
# fig = m.plot(forecast,figsize=(20, 12))
a = add_changepoints_to_plot(fig.gca(), model2, forecast)
plt.xlabel('Date')
plt.ylabel('Temperature')
plt.tight_layout()
plt.show()

predicted_data.iloc[-7:,:]

from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

ls = linear_model.LinearRegression()



X = data["Humidity%"].values.reshape(-1,1)
y = data["Temparature(c)"].values.reshape(-1,1)

X_train, X_test, y_train, y_test = train_test_split(X, 
                                                    y, 
                                                    test_size=0.33, 
                                                    shuffle=True, random_state=0)
print("Linear Regression")
ls.fit(X_train, y_train)
print("alpha = ",ls.coef_[0])
print("beta = ",ls.intercept_)
print("\n\nCalculating some regression quality metrics")
y_pred = ls.predict(X_test)
print("MSE = ",mean_squared_error(y_test, y_pred))
print("R2 = ",r2_score(y_test, y_pred))