Boosting Machine Learning Model Efficiency via Enhanced Data Preprocessing and Feature Engineering

Enhancing the Efficiency of a Model through Data Preprocessing and Feature Engineering

Abstract:

In today's data-driven world, algorithms play an essential role in various applications. The efficiency of theselargely deps on the quality of the input data and the preprocessing steps taken prior to their application. This paper investigates how effective data pre and feature engineering can significantly enhance model performance. We delve into methodologies such as normalization, scaling, encoding categorical variables, handling missing values, and dimensionality reduction strategies.

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We utilize a structured approach to tackle these issues by first collecting raw data from diverse sources. Subsequently, we conduct exploratory data analysis EDA to understand the characteristics of the dataset thoroughly. Based on our insights from EDA, we apply pre like normalization using Min-Max scaling or Z-score normalization and feature engineering steps such as creating interaction features, polynomial features, or extracting meaningful attributes.

Results:

Our results demonstrate a significant improvement in model efficiency after applying data preprocessing and feature engineering techniques. A model trned with preprocessed data shows a higher accuracy rate compared to the initial model without these improvements. Notably, dimensionality reduction techniques like PCA Principal Component Analysis helped in reducing computational complexity while retning the essential information.

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By optimizing input data through data preprocessing and feature engineering strategies, we can significantly boost the performance of . These strategies not only improve prediction accuracy but also enhance model interpretability. It is crucial to invest time in refining and enhancing raw data as it serves as a foundation for building robust predictive. Future research should focus on developing adaptive methods that automatically select the best pre and feature engineering strategies based on dataset characteristics.

Keywords: , Data Preprocessing, Feature Engineering, Model Efficiency, Performance Improvement
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