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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn import linear_model | |
Stock_Market = {'Year': [2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2022,2021,2021,2021,2021,2021,2021,2021,2021,2021,2021,2021,2021], | |
'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1], | |
'EconomicGrowth_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75], | |
'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1], | |
'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719] | |
} | |
df = pd.DataFrame(Stock_Market,columns = ['Year','Month','EconomicGrowth_Rate','Unemployment_Rate','Stock_Index_Price']) | |
x = df[['EconomicGrowth_Rate','Unemployment_Rate']] | |
y = df['Stock_Index_Price'] | |
lr = linear_model.LinearRegression() | |
lr.fit(x,y) | |
st.title("Stock Index Prediction using Linear Regression") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.image('stockindex.jpeg',width=700) | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.subheader("Relationship between Stock Index Price and Economic Growth Rate") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
plt.figure(figsize=(9,6)) | |
plt.xlabel("Economic Growth Rate") | |
plt.ylabel("Stock Index Price") | |
plt.scatter(df['EconomicGrowth_Rate'],df['Stock_Index_Price'],color='g') | |
plt.tight_layout() | |
st.set_option('deprecation.showPyplotGlobalUse',False) | |
st.pyplot() | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
st.subheader("Relationship between Stock Index Price and unemployment Rate") | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
plt.figure(figsize=(9,6)) | |
plt.xlabel("Unemployment Rate") | |
plt.ylabel("Stock Index Price") | |
plt.scatter(df['Unemployment_Rate'],df['Stock_Index_Price'],color='g') | |
plt.tight_layout() | |
st.set_option('deprecation.showPyplotGlobalUse',False) | |
st.pyplot() | |
st.text(" ") | |
st.text(" ") | |
st.text(" ") | |
i = st.number_input("Enter Economic Growth Rate") | |
st.text(" ") | |
st.text(" ") | |
u = st.number_input("Enter Unemployment Rate") | |
st.text(" ") | |
st.text(" ") | |
if st.button("Predict"): | |
st.subheader("Predicted Value of Stock Index") | |
st.text(lr.predict([[i,u]])) | |