https://drive.google.com/file/d/1CnCnFGRt0LDJibsDuH2690gTPpcB-RJx/view?usp=sharing

import streamlit as st

import joblib

import pandas as pd

model = joblib.load('model.pkl')

features = ['Hobby', 'OpenSource', 'Country', 'Student', 'Employment', 'FormalEducation',

'UndergradMajor', 'CompanySize', 'DevType', 'YearsCoding', 'YearsCodingProf']

st.title('Job Satisfaction Prediction')

with st.form(key='prediction_form'):

Hobby = st.selectbox('Hobby:', ['Yes', 'No'])

OpenSource = st.selectbox('Open Source:', ['Yes', 'No'])

Country = st.selectbox('Country:', ['United States', 'India', 'Germany'])

Student = st.selectbox('Student:', ['Yes', 'No'])

Employment = st.selectbox('Employment:', ['Employed full-time', 'Employed part-time', 'Self-employed', 'Unemployed'])

FormalEducation = st.selectbox('Formal Education:', ["Bachelor’s degree (BA, BS, B.Eng., etc.)",

"Master’s degree (MA, MS, M.Eng., MBA, etc.)",

"Doctoral degree (PhD)"])

UndergradMajor = st.selectbox('Undergrad Major:', ["Computer science, computer engineering, or software engineering",

"Information technology, networking, or system administration",

"Other engineering discipline"])

CompanySize = st.selectbox('Company Size:', ['Fewer than 10 employees', '10 to 19 employees', '20 to 99 employees',

'100 to 499 employees', '500 to 999 employees', '1,000 to 4,999 employees'])

DevType = st.selectbox('Dev Type:', ['Developer, back-end', 'Developer, front-end', 'Developer, full-stack'])

YearsCoding = st.selectbox('Years Coding:', ['0-2 years', '3-5 years', '6-8 years', '9-11 years'])

YearsCodingProf = st.selectbox('Years Coding Professionally:', ['0-2 years', '3-5 years', '6-8 years', '9-11 years'])

submit_button = st.form_submit_button(label='Get Prediction')

if submit_button:

# Collect user input

input_data = {

'Hobby': Hobby,

'OpenSource': OpenSource,

'Country': Country,

'Student': Student,

'Employment': Employment,

'FormalEducation': FormalEducation,

'UndergradMajor': UndergradMajor,

'CompanySize': CompanySize,

'DevType': DevType,

'YearsCoding': YearsCoding,

'YearsCodingProf': YearsCodingProf

}

# Convert user input to DataFrame

input_df = pd.DataFrame([input_data])

# Ensure the input has the same columns as the training data

input_df = input_df[features]

# Make prediction

prediction = model.predict(input_df)

# Display the prediction

st.write(f'Predicted Job Satisfaction: {prediction[0]}')

here are requirement->

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler, OneHotEncoder

from sklearn.compose import ColumnTransformer

from sklearn.pipeline import Pipeline

from sklearn.impute import SimpleImputer

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report