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Data Structures Writeup #8

Open Aditi99b opened 6 months ago

Aditi99b commented 6 months ago

Collections

Lists and Dictionaries

APIs and JSON

Frontend

Optional/Extra, Algorithm Analysis

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Data preprocessing, such as one-hot encoding, is applied to ensure the input data is in a suitable format for the model.

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A pipeline is created to streamline the preprocessing and modeling steps, ensuring seamless integration.

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The pipeline is trained on the training data, enabling both preprocessing and model learning to occur simultaneously.

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Predictions are made on new data using the trained pipeline, which automatically applies the necessary preprocessing steps before making predictions.

Discuss concepts and understanding of Linear Regression algorithms. In machine learning, linear regression serves as a foundational algorithm for modeling the relationship between independent variables and a continuous target variable. By fitting a linear equation to the observed data, it enables prediction of outcomes based on input features, making it applicable in diverse domains such as finance, healthcare, and marketing. Linear regression's simplicity, interpretability of coefficients, and ability to capture linear relationships make it a valuable tool for both predictive modeling and understanding the underlying patterns in data.

Discuss concepts and understanding of Decision Tree analysis algorithms. Decision Tree analysis involves recursively partitioning the feature space to construct a tree-like structure where decisions are made at each node based on feature values, leading to interpretable rules for prediction. Its simplicity, interpretability, and ability to handle both numerical and categorical data make it widely applicable across domains such as healthcare, finance, and marketing. Extensions like Random Forests and Gradient Boosting enhance predictive performance by aggregating multiple trees while mitigating overfitting, further expanding its utility in machine learning tasks.