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Classification algorithms include logistic regression, k-nearest neighbors and supportvectormachines (SVMs), among others. Naïve Bayes algorithms include decisiontrees , which can actually accommodate both regression and classification algorithms.
Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training. ExploratoryDataAnalysis (EDA) EDA is a crucial preliminary step in understanding the characteristics of the dataset. classification, regression) and data characteristics.
Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decisiontrees, random forests, supportvectormachines, and neural networks. Here is a brief description of the same.
That post was dedicated to an exploratorydataanalysis while this post is geared towards building prediction models. Feel free to try other algorithms such as Random Forests, DecisionTrees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc., among unsupervised models.
Key Components In Data Science, key components include data cleaning, ExploratoryDataAnalysis, and model building using statistical techniques. ML focuses on algorithms like decisiontrees, neural networks, and supportvectormachines for pattern recognition.
In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratorydataanalysis (EDA). Random Forest Classifier (rf): Ensemble method combining multiple decisiontrees.
Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis. DecisionTrees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.
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