Create app.py

app.py

@@ -0,0 +1,46 @@
+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)}")