Python - Machine Learning Part 5: Advanced Techniques and Deployment

Once a model is trained and evaluated, it can be improved using advanced techniques and deployed to production. Python libraries like Flask and FastAPI make deployment easier.

Examples:

Hyperparameter Tuning

from sklearn.model_selection import GridSearchCV

from sklearn.svm import SVC

# Data

X = [[1], [2], [3], [4]]

y = [0, 0, 1, 1]

# Hyperparameter tuning

model = SVC()

param_grid = {'C': [1, 10], 'kernel': ['linear', 'rbf']}

grid = GridSearchCV(model, param_grid)

grid.fit(X, y)

print(grid.best_params_)

Explanation: GridSearchCV tests multiple parameter combinations to optimize model performance.

Model Deployment Using Flask

from flask import Flask, request, jsonify

import pickle

app = Flask(__name__)

@app.route('/predict', methods=['POST'])

def predict():

    data = request.get_json()

    model = pickle.load(open('model.pkl', 'rb'))

    prediction = model.predict([data['input']])

    return jsonify({'prediction': prediction[0]})

app.run()

Explanation: This example demonstrates deploying a model using Flask, where the user sends input data via an API.

Real-Time Prediction

Use deployed models in real-time systems like recommendation engines or fraud detection systems.

Conclusion

Python machine learning covers a vast spectrum, from data preprocessing to advanced deployment techniques. Each step is crucial for building robust, efficient, and scalable solutions. By mastering the concepts in this guide, developers can unlock the potential of machine learning for diverse applications.