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Introduction :- Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed. In other words, it is a way of teaching computers to learn from experience and improve their performance over time. Machine learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on a labeled dataset, where the correct output or label is known for each input or instance. Unsupervised learning involves training a model on an unlabeled dataset, where the model must discover patterns or structure in the data without any guidance. Reinforcement learning involves training a model to make decisions based on rewards or punishments received from the environment. The model learns by trial and error, and aims to maximize its reward over time. • Data Collection: This involves collecting and acquiring the data that will be used for training the machine learning model. The data could come from a variety of sources, such as databases, files, web scraping, or APIs. • Data Preparation: This involves cleaning and preprocessing the data to make it suitable for use in the machine learning model. This includes tasks such as handling missing data, removing outliers, normalizing the data, and encoding categorical variables. • Feature Engineering: This involves...

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