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| import streamlit as st | |
| import joblib | |
| import pandas as pd | |
| #st.title('Placement Prediction app') | |
| # st.markdown(""" | |
| # <style> | |
| # .title { | |
| # font-size: 50px; | |
| # font-weight: bold; | |
| # color: #4CAF50; | |
| # text-align: center; | |
| # font-family: 'Courier New', Courier, monospace; | |
| # } | |
| # </style> | |
| # """, unsafe_allow_html=True) | |
| # st.title('Placement Prediction App') | |
| # st.subheader('Predicting student placement outcomes using machine learning') | |
| # st.markdown('This app uses historical data to predict whether a student will be placed in a company based on their profile.') | |
| try: | |
| model = joblib.load('/Users/nishthapandey/Desktop/PlacementPrediction/model_campus_placement_rf.joblib') | |
| st.success("Model loaded successfully!") | |
| except Exception as e: | |
| st.error(f"Error loading model: {e}") | |
| st.stop() | |
| model = joblib.load(open('/Users/nishthapandey/Desktop/PlacementPrediction/model_campus_placement_rf.joblib','rb')) | |
| def predict_placement(data): | |
| # Preprocess the data | |
| new_data = pd.DataFrame(data) | |
| # Make prediction | |
| prediction = model.predict(new_data)[0] | |
| prob = model.predict_proba(new_data)[0][1] | |
| return prediction, prob | |
| def main(): | |
| st.header('Placement Prediciton App') | |
| gender = st.radio('Gender', ['Male', 'Female']) | |
| ssc_p = st.number_input('Secondary School Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1) | |
| ssc_b = st.radio('Board of Education (SSC)', ['Central', 'Others']) | |
| hsc_p = st.number_input('Higher Secondary Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1) | |
| hsc_b = st.radio('Board of Education (HSC)', ['Central', 'Others']) | |
| degree_p = st.number_input('UG Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1) | |
| branch = st.selectbox('Branch of Study', ['CSE', 'ECE/EN', 'Others']) | |
| workex = st.radio('Work Experience', ['Yes', 'No']) | |
| certifications = st.number_input('Number of Certifications', min_value=0, max_value=10, value=0) | |
| etest_p = st.number_input('Employability Percentage', min_value=0.0, max_value=100.0, value=50.0, step=0.1) | |
| backlogs = st.number_input('Number of Backlogs', min_value=0, max_value=10, value=0) | |
| if st.button('predict'): | |
| new_data = { | |
| 'gender': 0 if gender == "Male" else 1, | |
| 'ssc_p': ssc_p, | |
| 'ssc_b': 1 if ssc_b == "Central" else 0, | |
| 'hsc_p': hsc_p, | |
| 'hsc_b': 1 if hsc_b == "Central" else 0, | |
| 'degree_p': degree_p, | |
| 'Branch': 2 if branch == "ECE/EN" else 1 if branch == "CSE" else 0, | |
| 'Workex': 1 if workex == "Yes" else 0, | |
| 'Certifications': certifications, | |
| 'etest_p': etest_p, | |
| 'Backlogs': backlogs, | |
| } | |
| prediction, probability = predict_placement(new_data) | |
| st.write(f'Percentage of getting placed: {probability*100:.2f}%') | |
| if __name__=='__main__': | |
| main() | |