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PROPOSAL FOR SUPERVISED LEARNING Name of Faculty: Semester: Credits: 6 Target group: B. Tech Honors & DD Students Prerequisites: Should have cleared CL 260, CL 336, CL 325 Scope of Activity: (50100

How to fill out supervised learning

How to fill out supervised learning:

Understand the concept of supervised learning:

1. Supervised learning is a type of machine learning where an algorithm learns from labeled data to make predictions or decisions.
2. It requires a dataset with input variables (features) and corresponding output variables (labels or target variable).

Gather and prepare the labeled dataset:

1. Identify the problem you want to solve or the task you want the algorithm to perform.
2. Collect a dataset that represents the problem/task, ensuring each data point has both input and output values.
3. Clean and preprocess the data by dealing with missing values, outliers, and irrelevant features.

Split the dataset into training and testing sets:

1. Divide the dataset into two separate sets: the training set and the testing set.
2. The training set is used to train the supervised learning algorithm, while the testing set is used to evaluate its performance.
3. It is crucial to have enough data in the training set to ensure the algorithm learns effectively, but also reserve enough data for testing to assess its generalization ability.

Choose a suitable supervised learning algorithm:

1. There are various supervised learning algorithms available, such as linear regression, logistic regression, decision trees, support vector machines (SVMs), and artificial neural networks.
2. Consider the nature of your problem, the type of data, and the algorithm's strengths and weaknesses to select the most appropriate algorithm.

Train the supervised learning algorithm:

1. Feed the training set into the chosen algorithm, allowing it to learn the patterns and relationships between the input and output variables.
2. The algorithm adjusts its internal parameters based on the data to minimize the prediction errors.

Evaluate and fine-tune the algorithm's performance:

1. Use the testing set to evaluate the algorithm's predictive accuracy, which can be measured using metrics like accuracy, precision, recall, or F1 score.
2. If the performance is not satisfactory, consider fine-tuning the algorithm by adjusting its hyperparameters or exploring other algorithms.

Who needs supervised learning:

Businesses and industries:

Many industries, such as finance, healthcare, retail, and manufacturing, can benefit from supervised learning to improve decision-making, customer segmentation, fraud detection, and predictive maintenance.

Researchers and scientists:

Researchers and scientists can use supervised learning to analyze complex data, gain insights, and make predictions in various fields like biology, climate science, genetics, and astronomy.

Data scientists and machine learning practitioners:

Data scientists and machine learning practitioners utilize supervised learning as a fundamental tool in their toolkit to build predictive models, solve classification or regression problems, and perform tasks like image or speech recognition.

Government organizations and public sectors:

Government entities can leverage supervised learning to enhance public services, optimize resource allocation, analyze social trends, and predict outcomes in law enforcement, transportation, public health, and urban planning.

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