app.py

import streamlit as st # type: ignore
import numpy as np
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
from plotly import graph_objs as go
from sklearn.linear_model import LinearRegression

st.set_option('deprecation.showPyplotGlobalUse', False)

data = pd.read_csv('Salary_Data.csv')
st.write(data.head())
X = np.array(data[['YearsExperience']])
lr = LinearRegression()
lr.fit(X, np.array(data.Salary))

nav = st.sidebar.radio('Navigation',['Home','Prediction', 'About'])
if nav == 'Home':
    col1,col2,col3 = st.columns([1,2,1])
    with col2:
        st.title('Salary Prediction')
    st.image('salary.jpg',width=600)
    if st.checkbox('Show Table'):
        st.write(data)
    graph = st.selectbox('What kind of graph you want to plot?',['Non interactive','Interactive'])
    val = st.slider('Filter data using Years', 0,20)
    data = data.loc[data.YearsExperience>= val]
    if graph == 'Non interactive':
        plt.figure(figsize=(10,5))
        plt.scatter(data.YearsExperience,data.Salary)
        plt.xlabel('Years of experience')
        plt.ylabel('Salaries')
        st.pyplot()
    else:
        layout = go.Layout(xaxis = dict(range=[0,16]),
                           yaxis = dict(range=[0,210000]))
        fig = go.Figure(data=go.Scatter(x=data.YearsExperience,y=data.Salary,
                                 mode='markers'),layout=layout)
        st.plotly_chart(fig)
elif nav == 'Prediction':
    st.header('Know your salary')
    values = st.number_input('Enter your exp',0,20,step=1)
    values = np.array(values).reshape(-1,1)
    pred = lr.predict(values)[0]
    if st.button('Predict'):
        st.success(f"Your Predicted Salary is {round(pred)}")