main.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model

Stock_Market = {'Year': [2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2016, 2016, 2016, 2016,
                         2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016],
                '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.cache_resource
def plot():
    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.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='r')
    plt.tight_layout()
    st.set_option('deprecation.showPyplotGlobalUse', False)
    st.pyplot()
    st.text(" ")
    st.text(" ")
    st.text(" ")


plot()

e = st.number_input("Enter Economic Growth Rate")
st.text(" ")
u = st.number_input("Enter Unemployment Rate")
st.text(" ")

if st.button("Predict"):
    st.subheader("Predicted Value of Stock Index ")
    st.text(lr.predict([[e, u]])[0])