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README.md

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+\#Student placement Prediction System
+
+\#Overview
+This project predicts whether a student will be  placed based on Academic performances and skills-based features using Machine Learning
+
+
+
+\#Features
+-Data Preprocessing
+-Random Forest model learning
+-Prediction System
+-Streamlit Web App
+-real-time prediction
+
+
+
+\#Tech Stack
+-Python
+-Pandas,NumPy
+-Scikit-learn
+-Streamlit
+
+
+
+\#Input Features
+-Gender
+-10th Board and marks
+-12th Board and marks
+-Stream
+-CGPA
+-Internships
+-Training
+-Backlog
+-Innovative
+-Communications Skills
+-Techincal Course
+
+
+
+\#Output
+-Placed/Not Placed
+
+
+
+\#project structure
+data/
+sample.csv
+notebooks/
+eda:ipynb
+src/
+train-model.py
+predict.py
+app.py
+requirements.txt
+README.md
+
+
+
+\#How to run
+1.Install all dependencies
+pip install -r requirements.txt
+2.train the model
+cd src/train\_model.py
+3.Run the streamlit app
+streamlit run app.py
+
+
+
+\#Author
+Nithyashree .L
+
+
+
+
+
+title:StudentPlacement
+
+colorFrom:blue
+
+colorTo:purple
+
+sdk:streamlit
+
+app\_file:app.py
+

app.py

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+import streamlit as st
+import numpy as np
+import joblib
+
+#load the training the model
+model=joblib.load("./models/models.pkl")
+st.set_page_config(page_title="🎓Student Placement Prediction System",page_icon="🎓",layout="wide")
+#tit
+st.markdown("<h1 style='text-align: center;background-color:darkblue; color: white;'>🎓Student Placement Prediction System</h1>",unsafe_allow_html=True)
+st.markdown("---")
+st.sidebar.header("Student Details")
+gender=st.selectbox("Gender",["Male","Female"])
+tenth_board=st.selectbox("10th Board",["CBSE","Diploma","ICSE","ISE","Other state Board","State Board","WBBSE"])
+tenth_marks=st.number_input("10th marks")
+twelfth_board=st.selectbox("12th Board",["CBSE","Diploma","ISE","Other state Board","State Board","WBCHSE"])
+twelfth_marks=st.number_input("12th marks")
+stream=st.selectbox("Stream",["Civil Engineering","Computer Science and Engineering","Computer Science in AIML","Electronics and Communication Engineering","Information Technology","Mechanical Engineering","Production Engineering"])
+cgpa=st.number_input("Cgpa")
+internship=st.selectbox("Internships(Y/N)",["Yes","No"])
+training=st.selectbox("Training(Y/N)",["Yes","No"])
+backlog=st.number_input("Backlog in 5th sem")
+innovative_project=st.selectbox("Innovative Project(Y/N)",["Yes","No"])
+communication=st.slider("Communication Skills",0,5)
+Course=st.selectbox("Technical Course(Y/N)",["Yes","No"])
+
+st.markdown("📑Students Inputs Summary")
+col1,col2,col3=st.columns(3)
+col1.metric("10th Marks",tenth_marks)
+col2.metric("12th Marks",twelfth_marks)
+col3.metric("CGPA",cgpa)
+st.markdown("---")
+if st.button("Predict Placement"):
+    gender=1 if gender=="Male" else 0
+    tenth_board_encoded=["CBSE","Diploma","ICSE","ISE","Other state Board","State Board","WBBSE"].index(tenth_board)
+    twelfth_board_encoded=["CBSE","Diploma","ISE","Other state Board","State Board","WBCHSE"].index(twelfth_board)
+    stream_encoded=["Civil Engineering","Computer Science and Engineering","Computer Science in AIML","Electronics and Communication Engineering","Information Technology","Mechanical Engineering","Production Engineering"].index(stream)
+    internship=1 if internship=="Yes" else 0   
+    training=1 if training=="Yes" else 0 
+    backlog=1 if backlog>0 else 0
+    innovative_project=1 if innovative_project=="Yes" else 0
+    courses=1 if Course=="Yes" else 0
+
+    input_data=np.array([[gender,tenth_board_encoded,tenth_marks,twelfth_board_encoded,twelfth_marks,stream_encoded,cgpa,internship,training,backlog,innovative_project,communication,courses]])
+    prediction=model.predict(input_data)
+
+    if prediction[0]==1:
+        st.success("🎉🎉Student will be placed")
+        st.balloons()
+    else:
+        st.error("❌😒Student will not be placed")
+        st.snow()

requirements.txt

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+pandas
+numpy
+matplotlib
+joblib
+streamlit 
+scikit-learn
+seaborn
+jupyter
+