This Is The Link Of The Book For The Assignment

This Is The Link Of The Book For The Assignmenthttpproquestsafari

This is the link of the book for the Assignment: Assignment: 1. 6.1, and 6.3 on page and answer Essay Question: Explain the differences between statistical and machine-learning approaches to the analysis of large datasets. 2. Chapter 7: Problems 1 and 2 on Page 146 and answer Essay Question: How does a k-Nearest Neighbor learner make predictions about new data points? How does a distance-weighted k-Nearest Neighbor learner differ from a standard k-Nearest Neighbor learner? What is locally weighted regression? 3. Chapter 8: Problem 1 a, b, c & d on Page 162. Special note about Chapter 8: Problem 1. Please add the following to the problem statement: Consider the following customer: Age=40, Experience=10, Income=84, Family=2, CCAvg=2, Education_2=1, Education_3=0, Mortgage=0, Securities Account=0, CD Account=0, Online=1 and Credit card = 1. This was omitted from the textbook. This week's assignment requires that you use Excel pivot tables. 4. Essay Question: Why is naive Bayesian classifier called Naive? Briefly outline the major ideas of naive Bayesian classification. Thank you.

Paper For Above instruction

The assignment encompasses a range of topics related to data analysis, machine learning algorithms, and statistical approaches, derived from specific chapters in a designated textbook. It includes analytical comparisons between statistical and machine-learning methods, exploration of k-Nearest Neighbor algorithms, an understanding of locally weighted regression, and an investigation into the naive Bayesian classifier. Additionally, practical application through Excel pivot tables is required to enhance data handling skills.

Differences Between Statistical and Machine-Learning Approaches

Statistical and machine-learning approaches are both pivotal in large dataset analysis but differ fundamentally in their methodologies, objectives, and implementation. Statistical methods traditionally rely on explicit models and assumptions about data distributions. They emphasize understanding the underlying data generation processes, often using hypothesis testing, confidence intervals, and parameter estimation (James et al., 2013). These approaches prioritize interpretability and inferential reasoning, aiming to validate hypotheses about the data.

In contrast, machine-learning approaches focus on predictive accuracy, often employing algorithms that can automatically learn complex patterns from data without explicitly modeling the data distribution. Machine learning exploits algorithms like decision trees, neural networks, and ensemble methods, which can handle high-dimensional data and nonlinear relationships more efficiently (Hastie, Tibshirani, & Friedman, 2009). Machine learning models prioritize prediction over interpretability, often at the expense of model transparency.

While statistical approaches often require assumptions about data normality or linearity, machine learning techniques are more flexible but may require larger data volumes and computational resources. The integration and evolution of both fields have led to hybrid approaches, leveraging the strengths of each to improve large dataset analysis (James et al., 2013).

k-Nearest Neighbor (k-NN) Classifier Predictions and Variants

The k-Nearest Neighbor (k-NN) algorithm is a simple, instance-based learning method. To predict the class of a new data point, the algorithm calculates the distance between the new point and all other data points in the training set, typically using Euclidean distance. It then identifies the 'k' closest neighbors and assigns the class based on a majority vote among these neighbors (Cover & Hart, 1967). For example, if k=5, the class label most frequently observed among these five neighbors is assigned to the new point.

The distance-weighted k-NN modifies this approach by giving different weights to neighbors based on their proximity, often assigning higher weights to closer neighbors. This means that nearer neighbors have more influence on the prediction than those farther away. Commonly, weights are inversely proportional to the distance, thereby emphasizing the relevance of the closest data points (Dudani, 1976). This variation often improves prediction accuracy, especially when data points are unevenly distributed.

Locally weighted regression (LWR) extends the concept further by fitting simple models locally around the target point rather than making a direct class label prediction. LWR uses nearby data points, weighted by their distance, to fit a linear or nonlinear model specifically tailored for that locality, providing a flexible prediction mechanism well-suited for nonlinear data patterns (Loader, 1999).

Customer Data Case and Use of Excel Pivot Tables

In analyzing the specified customer profile—Age=40, Experience=10, Income=84, Family=2, CCAvg=2, Education_2=1, Education_3=0, Mortgage=0, Securities Account=0, CD Account=0, Online=1, CreditCard=1—it is essential to utilize Excel pivot tables for efficient data analysis. Pivot tables facilitate summarization, categorization, and pattern recognition within large datasets, enabling quick insights into customer behavior, segmentation, and relationships among variables (Walkenbach, 2010). For example, pivot tables can illustrate average income by education level or online account activity across different age groups, aiding decision-making and strategic planning.

Naive Bayesian Classifier and Its Naivety

The naive Bayesian classifier is called 'naive' because it assumes strong independence among features given the class label. In reality, features in most datasets are often correlated, but the classifier simplifies computation by treating them as conditionally independent, which rarely holds in practice. This assumption greatly reduces the computational complexity and eases the estimation of probabilities (Russell & Norvig, 2016).

The core idea of naive Bayesian classification involves calculating the posterior probability of each class given the feature values, using Bayes’ theorem. The classifier predicts the class with the highest posterior probability. Despite its simplicity and the 'naive' independence assumption, the naive Bayesian classifier often performs surprisingly well, especially in high-dimensional spaces such as text classification and spam filtering (Manning, Raghavan, & Schütze, 2008).

In essence, the naive Bayesian approach combines prior probabilities of classes with the likelihood of observed features, multiplying these probabilities under the independence assumption. Its efficiency and robustness make it a popular choice for many real-world classification problems, despite its simplistic assumptions.

Conclusion

This assignment highlights the interplay between traditional statistical methods and modern machine learning algorithms in analyzing large datasets. Understanding the differences and applications of these approaches enables analysts and data scientists to select appropriate tools for specific problems. Incorporating practical skills, such as using Excel pivot tables, complements theoretical knowledge and enhances data management capabilities. The exploration of algorithms like k-NN and naive Bayes demonstrates the diversity of model complexity and assumptions, emphasizing the importance of context and data characteristics in choosing the right analytical approach.

References

• Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
• Dudani, S. A. (1976). The distance-weighted k-nearest neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6(4), 431–433.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
• Loader, C. (1999). Locally Weighted Regression. Springer.
• Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
• Walkenbach, J. (2010). Excel 2010 Bible. Wiley.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.