upload app.py

app.py

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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()
+