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Unsupervised models Unsupervised models typically use traditional statistical methods such as logistic regression, time series analysis, and decisiontrees. They often play a crucial role in clustering and segmenting data, helping businesses identify trends without prior knowledge of the outcome.
Importance of Data Science Data Science is crucial in decision-making and businessintelligence across various industries. By leveraging data-driven insights, organisations can make more informed decisions, optimise processes, and gain a competitive edge in the market.
Techniques like linear regression, time series analysis, and decisiontrees are examples of predictive models. These models enable businesses to anticipate customer behaviour, forecast sales, or predict risks. Model Validation Model validation is a critical step to evaluate the model’s performance on unseen data.
In the final stage, the results are communicated to the business in a visually appealing manner. This is where the skill of data visualization, reporting, and different businessintelligence tools come into the picture. Decisiontrees are more prone to overfitting. So, this is how we draw a typical decisiontree.
Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. What are the advantages and disadvantages of decisiontrees ? It is essential to provide a unified data view and enable businessintelligence and analytics. Explain the Extract, Transform, Load (ETL) process.
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