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import pandas as pd
from flask import Flask, request, jsonify
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder, StandardScaler
from streamlit import *
import joblib
# Load the CSV data
data = pd.read_csv('dataset.csv')
# Split the data into features and labels
X = data.drop('PlacedOrNot', axis=1)
y = data['PlacedOrNot']
# Encode categorical features
categorical_features = ['HistoryOfBacklogs']
for feature in categorical_features:
encoder = LabelEncoder()
X[feature] = encoder.fit_transform(X[feature])
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create the pipeline
numerical_features = ['Internships', 'CGPA']
numerical_transformer = StandardScaler()
categorical_features = [ 'HistoryOfBacklogs']
categorical_transformer = SimpleImputer(strategy='most_frequent')
preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer, numerical_features),
('cat', categorical_transformer, categorical_features)
])
pipeline = Pipeline([
('preprocessor', preprocessor),
('classifier', RandomForestClassifier(random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Evaluate the model
accuracy = pipeline.score(X_test, y_test)
print('Accuracy:', accuracy)
input_type = 'csv'
output_type = 'label'
# Define the function to make predictions using the trained model
def predict_placement(Internships, CGPA, HistoryOfBacklogs):
# Create a DataFrame from the input data
input_df = pd.DataFrame({'Internships': [Internships], 'CGPA': [CGPA], 'HistoryOfBacklogs': [HistoryOfBacklogs]})
# Make a prediction using the trained model
prediction = pipeline.predict(input_df)[0]
# Return the predicted label
return 'Placed' if prediction else 'Not Placed'
# Create the Gradio interface
iface = gr.Interface(fn=predict_placement,
inputs=input_type,
outputs=output_type,
title='Student Job Placement Predictor',
description='Predicts whether a student will be placed in a job or not based on internships, CGPA, and history of backlogs.')
# Launch the Gradio interface
iface.launch() |