CapsureLabs incorporates advanced machine learning techniques to empower data analysis, enabling predictive insights and decision-making across the platform. This section provides a foundation for implementing machine learning models for data processing and prediction, covering algorithms for data preparation, predictive modeling, and automation.
1.2 Predictive Model for User Engagement Analysis
1.2.1 Data Preparation and Processing
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Load and clean dataset
data = pd.read_csv("user_engagement.csv")
data.dropna(inplace=True) # Remove missing values
# Feature selection
X = data[['time_spent', 'actions', 'pages_visited', 'user_age']]
y = data['engagement_label'] # 1 for high engagement, 0 for low engagement
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
1.2.2 Model Training: Random Forest Classifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Model setup and training
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Evaluate accuracy
y_pred = model.predict(X_test)
print("Model Accuracy:", accuracy_score(y_test, y_pred))
1.2.3 Saving and Loading the Model for Production
import joblib
# Save model to file
joblib.dump(model, 'user_engagement_model.pkl')
# Load model
model = joblib.load('user_engagement_model.pkl')
1.3 Data Analysis and Prediction API Implementation
from flask import Flask, request, jsonify
import joblib
import numpy as np
app = Flask(__name__)
# Load pre-trained model
model = joblib.load('user_engagement_model.pkl')
@app.route('/predict', methods=['POST'])
def predict():
data = request.json
features = np.array([data['time_spent'], data['actions'], data['pages_visited'], data['user_age']]).reshape(1, -1)
prediction = model.predict(features)
return jsonify({'engagement_prediction': int(prediction[0])})
if __name__ == '__main__':
app.run(debug=True)
