Starter Project

import anvil.server anvil.server.connect("XR3N6TCMMANMPIP4ETR4FN6V-KZTY2LRNDSFKR2UX")

Connecting to wss://anvil.works/uplink
Anvil websocket open
Connected to "Default environment (dev)" as SERVER

import pandas as pd import seaborn as sns import numpy as np import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format = 'retina'

df=pd.read_csv('GoldPrice.csv', parse_dates=['Date']) df.head()

plt.figure(figsize=(16, 9)) sns.lmplot(x='Open', y='High', data=df, ci=None)

plt.figure(figsize=(16, 9)) sns.lmplot(x='Open', y='Low', data=df, ci=None)

import sklearn from sklearn.linear_model import LinearRegression

model = LinearRegression() # create object/instance (create an instance from LinearRegression class) model

model.fit(df[['Open']], df['High'])

model.score(df[['Open']], df['High'])

model.coef_

model.predict([[73495.23]])

model2 = LinearRegression() # create object/instance (create an instance from LinearRegression class) model2

model2.fit(df[['Open']], df['Low'])

model2.score(df[['Open']], df['Low'])

model2.coef_

model2.predict([[73495.23]])

@anvil.server.callable def predict_peak(open_price): regression = model.predict([[open_price]]) return regression[0] @anvil.server.callable def predict_low(open_price): regression2 = model2.predict([[open_price]]) return regression2[0]