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Naïve Bayes algorithms include decisiontrees , which can actually accommodate both regression and classification algorithms. Random forest algorithms —predict a value or category by combining the results from a number of decisiontrees.
Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. This step ensures that all relevant data is available in one place.
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.
C Classification: A supervised Machine Learning task that assigns data points to predefined categories or classes based on their characteristics. Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities.
I would perform exploratorydataanalysis to understand the distribution of customer transactions and identify potential segments. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. What approach would you take?
Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design, is paramount in Data Science roles. Clustering algorithms such as K-means and hierarchical clustering are examples of unsupervised learning techniques.
The programming language can handle Big Data and perform effective dataanalysis and statistical modelling. R allows you to conduct statistical analysis and offers capabilities of statistical and graphical representation. How is R Used in Data Science?
It involves handling missing values, correcting errors, removing duplicates, standardizing formats, and structuring data for analysis. ExploratoryDataAnalysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) This helps formulate hypotheses.
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). Are there clusters of customers with different spending patterns? #3.
LIME can help improve model transparency, build trust, and ensure that models make fair and unbiased decisions by identifying the key features that are more relevant in prediction-making. LIME provides explanations for individual predictions by approximating the model locally with an interpretable model like a decisiontree.
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