#naive bayes classifier

[codingprolab]

CS 440: INTRODUCTION TO ARTIFICIAL INTELLIGENCE Project : Face and Digit Classification

In this project, you will design three classifiers: a naive Bayes classifier, a perceptron classifier and a classifier of your choice. You will test your classifiers on two image data sets: a set of scanned handwritten digit images and a set of face images in which edges have already been detected. Even with simple features, your classifiers will be able to do quite well on these tasks when given…

[tccicomputercoaching]

Machine Learning Project Ideas for Beginners

Machine Learning (ML) is no longer something linked to the future; it is nowadays innovating and reshaping every industry, from digital marketing in healthcare to automobiles. If the thought of implementing data and algorithms trials excites you, then learning Machine Learning is the most exciting thing you can embark on. But where does one go after the basics? That answer is simple- projects!

At TCCI - Tririd Computer Coaching Institute, we believe in learning through doing. Our Machine Learning courses in Ahmedabad focus on skill application so that aspiring data scientists and ML engineers can build a strong portfolio. This blog has some exciting Machine Learning project ideas for beginners to help you launch your career along with better search engine visibility.

Why Are Projects Important for an ML Beginner?

Theoretical knowledge is important, but real-learning takes place only in projects. They allow you to:

Apply Concepts: Translate algorithms and theories into tangible solutions.

Build a Portfolio: Showcase your skills to potential employers.

Develop Problem-Solving Skills: Learn to debug, iterate, and overcome challenges.

Understand the ML Workflow: Experience the end-to-end process from data collection to model deployment.

Stay Motivated: See your learning come to life!

Essential Tools for Your First ML Projects

Before you dive into the ideas, ensure you're familiar with these foundational tools:

Python: The most popular language for ML due to its vast libraries.

Jupyter Notebooks: Ideal for experimenting and presenting your code.

Libraries: NumPy (numerical operations), Pandas (data manipulation), Matplotlib/Seaborn (data visualization), Scikit-learn (core ML algorithms). For deep learning, TensorFlow or Keras are key.

Machine Learning Project Ideas for Beginners (with Learning Outcomes)

Here are some accessible project ideas that will teach you core ML concepts:

1. House Price Prediction (Regression)

Concept: Regression (output would be a continuous value). 

Idea: Predict house prices based on given features, for instance, square footage, number of bedrooms, location, etc. 

What you'll learn: Loading and cleaning data, EDA, feature engineering, and either linear regression or decision tree regression, followed by model evaluation with MAE, MSE, and R-squared. 

Dataset: There are so many public house price datasets set available on Kaggle (e.g., Boston Housing, Ames Housing).

2. Iris Flower Classification (Classification)

Concept: Classification (predicting a categorical label). 

Idea: Classify organisms among three types of Iris (setosa, versicolor, and virginica) based on sepal and petal measurements. 

What you'll learn: Some basic data analysis and classification algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees), code toward confusion matrix and accuracy score. 

Dataset: It happens to be a classical dataset directly available inside Scikit-learn.

3. Spam Email Detector (Natural Language Processing - NLP)

Concept: Text Classification, NLP.

Idea: Create a model capable of classifying emails into "spam" versus "ham" (not spam).

What you'll learn: Text preprocessing techniques such as tokenization, stemming/lemmatization, stop-word removal; feature extraction from text, e.g., Bag-of-Words or TF-IDF; classification using Naive Bayes or SVM.

Dataset: The UCI Machine Learning Repository contains a few spam datasets.

4. Customer Churn Prediction (Classification)

Concept: Classification, Predictive Analytics.

Idea: Predict whether a customer will stop using a service (churn) given the usage pattern and demographics.

What you'll learn: Handling imbalanced datasets (since churn is usually rare), feature importance, applying classification algorithms (such as Random Forest or Gradient Boosting), measuring precision, recall, and F1-score.

Dataset: Several telecom-or banking-related churn datasets are available on Kaggle.

5. Movie Recommender System (Basic Collaborative Filtering)

Concept: Recommender Systems, Unsupervised Learning (for some parts) or Collaborative Filtering.

Idea: Recommend movies to a user based on their past ratings or ratings from similar users.

What you'll learn: Matrix factorization, user-item interaction data, basic collaborative filtering techniques, evaluating recommendations.

Dataset: MovieLens datasets (small or 100k version) are excellent for this.

Tips for Success with Your ML Projects

Start Small: Do not endeavor to build the Google AI in your Very First Project. Instead focus on grasping core concepts.

Understand Your Data: Spend most of your time cleaning it or performing exploratory data analysis. Garbage in, garbage out, as the data thinkers would say.

Reputable Resources: Use tutorials, online courses, and documentation (say, Scikit-learn docs).

Join Communities: Stay involved with fellow learners in forums like Kaggle or Stack Overflow or in local meetups.

Document Your Work: Comment your code and use a README for your GitHub repository describing your procedure and conclusions.

Embrace Failure: Every error is an opportunity to learn.

How TCCI - Tririd Computer Coaching Institute Can Help

Venturing into Machine Learning can be challenging and fulfilling at the same time. At TCCI, our programs in Machine Learning courses in Ahmedabad are created for beginners and aspiring professionals, in which we impart:

A Well-Defined Structure: Starting from basics of Python to various advanced ML algorithms.

Hands-On Training: Guided projects will allow you to build your portfolio, step by-step.

An Expert Mentor: Work under the guidance of full-time data scientists and ML engineers.

Real-World Case Studies: Learn about the application of ML in various industrial scenarios.

If you are considering joining a comprehensive computer classes in Ahmedabad to start a career in data science or want to pursue computer training for further specialization in Machine Learning, TCCI is the place to be.

Are You Ready to Build Your First Machine Learning Project?

The most effective way to learn Machine Learning is to apply it. Try out these beginner-friendly projects and watch your skills expand.

Contact us

Location: Bopal & Iskcon-Ambli in Ahmedabad, Gujarat

Call now on +91 9825618292

Visit Our Website: http://tccicomputercoaching.com/

[moonstone987]

Machine Learning Training in Kochi: Building Smarter Futures Through AI

In today’s fast-paced digital age, the integration of artificial intelligence (AI) and machine learning (ML) into various industries is transforming how decisions are made, services are delivered, and experiences are personalized. From self-driving cars to intelligent chatbots, machine learning lies at the core of many modern technological advancements. As a result, the demand for professionals skilled in machine learning is rapidly rising across the globe.

For aspiring tech professionals in Kerala, pursuing machine learning training in Kochi offers a gateway to mastering one of the most powerful and future-oriented technologies of the 21st century.

What is Machine Learning and Why Does it Matter?

Machine learning is a subfield of artificial intelligence that focuses on enabling computers to learn from data and improve over time without being explicitly programmed. Instead of writing code for every task, machine learning models identify patterns in data and make decisions or predictions accordingly.

Real-World Applications of Machine Learning:

Healthcare: Predicting disease, personalized treatments, medical image analysis

Finance: Fraud detection, algorithmic trading, risk modeling

E-commerce: Product recommendations, customer segmentation

Manufacturing: Predictive maintenance, quality control

Transportation: Route optimization, self-driving systems

The scope of ML is vast, making it a critical skill for modern-day developers, analysts, and engineers.

Why Choose Machine Learning Training in Kochi?

Kochi, often referred to as the commercial capital of Kerala, is also evolving into a major technology and education hub. With its dynamic IT parks like Infopark and the growing ecosystem of startups, there is an increasing need for trained professionals in emerging technologies.

Here’s why best machine learning training in Kochi is an excellent career investment:

1. Industry-Relevant Opportunities

Companies based in Kochi and surrounding regions are actively integrating ML into their products and services. A well-trained machine learning professional has a strong chance of landing roles in analytics, development, or research.

2. Cost-Effective Learning

Compared to metro cities like Bangalore or Chennai, Kochi offers more affordable training programs without compromising on quality.

3. Tech Community and Events

Tech meetups, hackathons, AI seminars, and developer communities in Kochi create excellent networking and learning opportunities.

What to Expect from a Machine Learning Course?

A comprehensive machine learning training in Kochi should offer a well-balanced curriculum combining theory, tools, and hands-on experience. Here’s what an ideal course would include:

1. Mathematics & Statistics

A solid understanding of:

Probability theory

Linear algebra

Statistics

Optimization techniques

These are the foundational pillars for building effective ML models.

2. Programming Skills

Python is the dominant language in ML.

Students will learn how to use libraries like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras.

3. Supervised & Unsupervised Learning

Algorithms like Linear Regression, Decision Trees, Random Forest, SVM, KNN, and Naive Bayes

Clustering techniques like K-means, DBSCAN, and Hierarchical Clustering

4. Deep Learning

Basics of neural networks

CNNs for image recognition

RNNs and LSTMs for sequential data like text or time series

5. Natural Language Processing (NLP)

Understanding text data using:

Tokenization, stemming, lemmatization

Sentiment analysis, spam detection, chatbots

6. Model Evaluation & Deployment

Confusion matrix, ROC curves, precision/recall

Deploying ML models using Flask or cloud services like AWS/GCP

7. Real-World Projects

Top training institutes ensure that students work on real datasets and business problems—be it predicting house prices, classifying medical images, or building recommendation engines.

Career Scope After Machine Learning Training

A candidate completing machine learning training in Kochi can explore roles such as:

Machine Learning Engineer

Data Scientist

AI Developer

NLP Engineer

Data Analyst

Business Intelligence Analyst

These positions span across industries like healthcare, finance, logistics, edtech, and entertainment, offering both challenging projects and rewarding salaries.

How to Choose the Right Machine Learning Training in Kochi

Not all training programs are created equal. To ensure that your investment pays off, look for:

Experienced Faculty: Instructors with real-world ML project experience

Updated Curriculum: Courses must include current tools, frameworks, and trends

Hands-On Practice: Projects, case studies, and model deployment experience

Certification: Recognized certificates add weight to your resume

Placement Assistance: Support with resume preparation, mock interviews, and job referrals

Zoople Technologies: Redefining Machine Learning Training in Kochi

Among the many institutions offering machine learning training in Kochi, Zoople Technologies stands out as a frontrunner for delivering job-oriented, practical education tailored to the demands of the modern tech landscape.

Why Zoople Technologies?

Industry-Aligned Curriculum: Zoople’s training is constantly updated in sync with industry demands. Their machine learning course includes real-time projects using Python, TensorFlow, and deep learning models.

Expert Trainers: The faculty includes experienced professionals from the AI and data science industry who bring real-world perspectives into the classroom.

Project-Based Learning: Students work on projects like facial recognition systems, sentiment analysis engines, and fraud detection platforms—ensuring they build an impressive portfolio.

Flexible Batches: Weekend and weekday batches allow both students and working professionals to balance learning with other commitments.

Placement Support: Zoople has an active placement cell that assists students in resume building, interview preparation, and job placement with reputed IT firms in Kochi and beyond.

State-of-the-Art Infrastructure: Smart classrooms, AI labs, and an engaging online learning portal enhance the student experience.

With its holistic approach and strong placement track record, Zoople Technologies has rightfully earned its reputation as one of the best choices for machine learning training in Kochi.

Final Thoughts

Machine learning is not just a career path; it’s a gateway into the future of technology. As companies continue to automate, optimize, and innovate using AI, the demand for trained professionals will only escalate.

For those in Kerala looking to enter this exciting domain, enrolling in a well-rounded machine learning training in Kochi is a wise first step. And with institutes like Zoople Technologies leading the way in quality training and real-world readiness, your journey into AI and machine learning is bound to be successful.

So, whether you're a recent graduate, a software developer looking to upskill, or a data enthusiast dreaming of a future in AI—now is the time to start. Kochi is the place, and Zoople Technologies is the partner to guide your transformation.

[programmingandengineering]

ECE368 Lab 1: Classification with Multinomial and Gaussian Models

Naive Bayes Classifier for Spam Filtering In the rst part of the lab, we use a Na ve Bayes Classi er to build a spam email lter based on whether and how many times each word in a xed vocabulary occurs in the email. Suppose that we need to classify set of N emails, and each email n is represented by fxn; yng; n = 1; 2; : : : ; N, where yn is the class label which takes the value yn = ( 1 if…

[thatware03]

Unlocking the Power of Logistic Regression and Naive Bayes for SEO and Sentiment Analysis

In the evolving world of SEO optimization and digital marketing, data-driven strategies play a pivotal role. Two advanced techniques, Logistic Regression and Naive Bayes, are transforming the way websites optimize their content and analyze user sentiments, providing deeper insights and better engagement outcomes.

Logistic Regression: Enhancing Predictions in SEO

Logistic Regression is a machine learning technique that predicts binary outcomes (e.g., success vs. failure). In SEO, it can forecast the likelihood of a webpage achieving a certain ranking based on factors like backlinks, page speed, and content quality. This approach allows marketers to focus their resources on elements most likely to improve SERP rankings. Logistic regression models are also used in understanding how variables such as keyword density and user engagement metrics influence a site's overall performance. By applying these insights, businesses can craft precise strategies tailored to their audience and goals.

Naive Bayes: Understanding Sentiments for Better User Experience

The Naive Bayes classifier is instrumental in sentiment analysis. By analyzing customer reviews, social media interactions, and website content, it categorizes sentiments into positive, negative, or neutral. This empowers businesses to understand user perceptions about their brand or product. For example, analyzing the sentiments expressed in reviews can inform content adjustments or service improvements, enhancing customer satisfaction and engagement.

The combination of Naive Bayes with NLP tools enables businesses to process large datasets efficiently, uncovering trends that would be missed with manual reviews. This not only streamlines content strategies but also ensures alignment with user expectations.

Hyper Intelligent SEO: A Game-Changer

When integrated with tools like AI-powered keyword extractors and semantic search optimization, both methods amplify the effectiveness of SEO strategies. Advanced analytics such as TF-IDF keyword extraction or BERT modeling for context optimization ensure that content resonates with search intent, improving rankings and user relevance.

Elevating Strategies with Hyper-Intelligent SEO

Integrating Hyper Intelligence SEO with these machine learning methods amplifies their impact. Advanced tools such as TF-IDF keyword extractors, semantic analysis, and BERT-based contextual modeling ensure content aligns with both user expectations and Google's evolving algorithms. This holistic approach guarantees a competitive edge in the ever-changing digital landscape.

[myprogrammingsolver]

ECE368 Lab 1: Classification with Multinomial and Gaussian Models

Naive Bayes Classifier for Spam Filtering In the rst part of the lab, we use a Na ve Bayes Classi er to build a spam email lter based on whether and how many times each word in a xed vocabulary occurs in the email. Suppose that we need to classify set of N emails, and each email n is represented by fxn; yng; n = 1; 2; : : : ; N, where yn is the class label which takes the value yn = ( 1 if…

[codezup]

Building a Text Classification Model with Naive Bayes and Python

Introduction Building a Text Classification Model with Naive Bayes and Python is a fundamental task in natural language processing (NLP) that involves training a machine learning model to classify text into predefined categories. This tutorial will guide you through the process of building a text classification model using Naive Bayes and Python, covering the technical background, implementation…

[careerguide1]

Top 10 Machine Learning Algorithms You Must Know in 2024

As automation continues to reshape industries, machine learning (ML) algorithms are at the forefront of this transformation. These powerful tools drive innovations in areas like healthcare, finance, and technology. From performing surgeries to playing chess, ML algorithms are revolutionizing how we solve complex problems.

Today’s technological revolution is fueled by the democratization of advanced computing tools, enabling data scientists to develop sophisticated models that tackle real-world challenges seamlessly. Whether it's predicting outcomes, classifying data, or finding patterns, these algorithms are continuously learning and evolving.

Top 10 Machine Learning Algorithms for 2024

Here are the top 10 machine learning algorithms that are crucial for every AI and data science professional to master in 2024:

Linear Regression: Predicts continuous outcomes by establishing a relationship between independent and dependent variables. The regression line minimizes the squared differences between data points and the fitted line.

Logistic Regression: Widely used for binary classification, logistic regression estimates the probability of an event occurring by fitting data to a logit function.

Decision Tree: A decision tree is a straightforward, intuitive model that splits data into branches based on the most important features, used for both classification and regression tasks.

Support Vector Machine (SVM): SVM is used for classification and works by finding the optimal boundary (or hyperplane) that best separates data into different classes.

Naive Bayes: Despite its simplicity, Naive Bayes is powerful for classification tasks, especially with large datasets. It assumes each feature independently contributes to the outcome, which helps with scalability.

K-Nearest Neighbors (KNN): KNN is a non-parametric algorithm used for both classification and regression. It classifies new data points by finding the most similar existing data points (neighbors) based on a distance function.

K-Means: An unsupervised clustering algorithm that groups data into k distinct clusters, where the points within each cluster are more similar to each other than to those in other clusters.

Random Forest: This ensemble learning algorithm builds multiple decision trees and combines their predictions to improve accuracy. It is widely used in both classification and regression tasks.

Dimensionality Reduction (PCA): In the era of big data, reducing the number of variables without losing valuable information is critical. PCA helps extract the most important features by reducing data dimensionality.

Gradient Boosting and AdaBoost: These are powerful boosting algorithms that combine several weak models to form a strong model, improving prediction accuracy. They are particularly popular in competitions like Kaggle for handling large, complex datasets.

Why These Algorithms Matter

Understanding these machine learning algorithms is vital because they each have unique strengths that make them suitable for different types of problems. Whether you're working with structured data in finance or unstructured data in healthcare, having a strong grasp of these algorithms will empower you to solve real-world challenges efficiently.

As automation continues to drive industries forward, mastering these algorithms can set you apart in the rapidly evolving fields of AI and data science.

Take Your Machine Learning Skills to the Next Level

Are you ready to dive deeper into the world of machine learning? At Machine Learning Classes in Pune, we provide hands-on experience with the top 10 algorithms mentioned above, enabling you to apply them in real-world scenarios.

Enroll today to future-proof your skills and stay ahead in the ever-changing landscape of technology!

[teguhteja]

Naive Bayes Classifier: Implementing from Scratch in Python

Discover how to implement a Naive Bayes Classifier in Python from scratch. This guide covers probabilistic foundations, step-by-step coding, and practical applications. Perfect for machine learning enthusiasts

The Naive Bayes Classifier stands as a powerful tool in machine learning. This blog post will guide you through implementing this classifier from scratch in Python. We’ll explore the fundamentals of Naive Bayes, its probabilistic foundations, and create a robust implementation without relying on pre-built libraries. Furthermore, we’ll delve into handling data issues and demonstrate practical…

[mvishnukumar]

What are the various classification algorithms?

Classification algorithms are used to categorize data into predefined classes. 

Here’s an overview of several commonly used classification algorithms:

Logistic Regression: This algorithm predicts the probability of a binary outcome using a logistic function. It’s straightforward and interpretable, making it suitable for problems where the relationship between features and the binary outcome is linear.

Decision Trees: Decision trees split the data into subsets based on feature values, forming a tree-like structure where each node represents a feature and each branch represents a decision rule. They are easy to interpret but can be prone to overfitting.

Random Forest: An ensemble method that builds multiple decision trees and aggregates their predictions. Random Forest reduces overfitting and improves accuracy by averaging the results of multiple trees.

Support Vector Machines (SVM): SVM finds the optimal hyperplane that separates data points into different classes with the maximum margin. 

Naive Bayes: Based on Bayes’ theorem, this algorithm assumes feature independence given the class label. It is efficient and performs well with categorical data, making it suitable for text classification and spam detection.

k-Nearest Neighbors (k-NN): k-NN classifies a data point based on the majority class of its k-nearest neighbors. 

Gradient Boosting Machines (GBM): GBM builds an ensemble of weak learners (e.g., decision trees) sequentially, where each new model corrects errors made by the previous ones. It improves prediction accuracy and handles various types of data.

AdaBoost: AdaBoost combines multiple weak classifiers to create a strong classifier. It adjusts the weights of misclassified instances to focus on harder examples in subsequent iterations.

Neural Networks: They can model complex relationships and are used in deep learning for tasks like image and speech recognition.

Each algorithm has its strengths and is suitable for different types of problems and data. The choice of algorithm depends on factors such as the nature of the data, the complexity of the problem, and the desired model performance.

[codingprolab]

COMP90049 Project 1: Naive Bayes and K-Nearest Neighbour for Predicting Stroke

In this project, you will implement Naive Bayes and K-Nearest Neighbour (K-NN) classifiers. You will explore inner workings and evaluate behavior on a data set of stroke prediction, and on this basis respond to some conceptual questions. Implementation The lectures and workshops contained several pointers for reading in data and implementing your Naive Bayes classifier. You are required to…

[codeshive]

DSCI552 Assignment 4 Naive Bayes Classifier and Theory Questions solved

For this assignment you will implement a Naive Bayes Classifier that implements the SKlearn classifier API with fit, predict and score methods. The Naive Bayes Classifer takes as parameter the density function used in the likelihood calcuation: normal: Normal density function knn: K nearest neighbor density function Most of the code already has been written for you. You only need to fill in the…

[ml-nn]

Understanding Naive Bayes

Naive Bayes is a popular and powerful classification algorithm used in machine learning. It's particularly effective for text classification and spam filtering. In this article, we'll delve into the fundamentals of Naive Bayes.

Introduction to Naive Bayes Naive Bayes is a probabilistic algorithm based on Bayes' theorem, which calculates the probability of a hypothesis given some observed evidence. The "naive" part comes from the assumption that features used to describe an observation are independent of each other, even though this might not be the case in reality.

Types of Naive Bayes Classifiers Multinomial Naive Bayes: Used for discrete data, typically text classification where the features are word counts. Gaussian Naive Bayes: Suitable for continuous data that follows a Gaussian distribution. It assumes that features are normally distributed. Bernoulli Naive Bayes: Appropriate for binary data, often used in document classification where features represent word presence or absence.

Read full article at:

[aibyrdidini]

A Comprehensive Guide of Supervised Learning in Machine Learning.

Supervised learning is a cornerstone of machine learning, where algorithms learn from labeled data to make predictions or classify new data.

This approach involves training models on datasets that include both input features and the desired output, enabling the model to learn from given examples and apply this learning to new, unseen data.

Learning Algorithms in Supervised Learning

Several learning algorithms are commonly used in supervised learning, each with its unique approach to learning from data:

- Linear Regression: Used for predicting a continuous outcome variable based on one or more predictor variables.

- Logistic Regression: Applied for binary classification problems, predicting the probability of an instance belonging to a particular class.

- Support Vector Machines (SVM): Effective in high-dimensional spaces and best suited for classification and regression analysis.

- Naive Bayes: A probabilistic classifier based on applying Bayes' theorem with strong independence assumptions between the features.

- K-Nearest Neighbors (K-NN): A non-parametric method used for classification and regression. It classifies new instances based on a similarity measure (e.g., distance functions).

Python Code Example for Supervised Learning

Below is a Python code example demonstrating the use of Logistic Regression, a popular supervised learning algorithm, using the scikit-learn library.

This example creates a synthetic dataset, splits it into training and testing sets, trains a Logistic Regression model, and evaluates its accuracy.

```python

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

# Create a synthetic binary classification dataset

X, y = make_classification(n_samples=1000, n_features=10, n_classes=2)

# Split the dataset into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# Initialize and train the Logistic Regression model

lr = LogisticRegression()

lr.fit(X_train, y_train)

# Make predictions on the test set

y_pred = lr.predict(X_test)

# Evaluate the model's accuracy

accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy}")

```

Applications of Supervised Learning

Supervised learning finds applications across various domains, including:

- Finance: Predicting stock prices or customer churn.

- Healthcare: Diagnosing diseases or predicting patient outcomes.

- E-commerce: Recommending products or predicting customer behavior.

- Transportation: Autonomous vehicle navigation and route optimization.

Conclusion

Supervised learning is a powerful machine learning technique that enables models to learn from labeled data, making accurate predictions or classifications on new data. With a wide range of algorithms and applications, supervised learning continues to be a fundamental approach in the field of machine learning.

RDIDINI PROMPT ENGINEER

[programmingandengineering]

Naive Bayes Classifier Assignment 1 of the Machine Learning 1

1 Introduction In this article, we will discuss the classification problem, in par-ticular with the Naive Bayes Classifier. In classification we have an object, called classifier, that asso-ciates each input with an output (also called class). In order to classify correctly the inputs, the classifier needs to study a set of inputs along with their respective classes. With the training process,…

[ai-news]

A complete worked example for text-review classificationContinue reading on Towards Data Science » #AI #ML #Automation