Upload placement.py

placement.py

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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)