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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
# 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)
# Create Flask app
app = Flask(__name__)
# Define API route for making predictions
@app.route('/predict', methods=['POST'])
def predict():
# Get input data from request
data = request.get_json()
# Convert input data to dataframe
input_data = pd.DataFrame(data, index=[0])
# Make predictions using the trained pipeline
predictions = pipeline.predict(input_data)
# Prepare response
response = {'prediction': predictions[0]}
return jsonify(response)
# Run the Flask app
if __name__ == '__main__':
app.run(debug=True)
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