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Question 2: Hierarchical Clustering and Classification Objective: Segment students into distinct groups based on their academic and extracurricular profiles and analyze the characteristics of each cluster. Tasks: 1. Data Preparation: . Load the mathnew.csv dataset. Select relevant features for clustering: Age, StudyHours, AttendanceRate, Extracurricular, MathScore. Normalize the selected features to ensure equal weighting in clustering. 2. Clustering: Determine the optimal number of clusters using methods such as the Elbow Method and Silhouette Analysis. Perform hierarchical clustering using Ward's method. Visualize the dendrogram to illustrate the clustering process. 3. Cluster Validation: Validate the stability of the clusters using bootstrapping or by splitting the data into training and testing sets. Compute cluster validity indices such as Silhouette Score, Davies-Bouldin Index, etc. 4. Characterization of Clusters: For each identified cluster, compute the mean and distribution of each variable. Analyze how clusters differ in terms of academic performance (MathScore), study habits (StudyHours), attendance (Attendance Rate), and extracurricular involvement. 5. Classification: Build a classification model (e.g., Decision Tree, Random Forest) to predict cluster membership based on the original features. Evaluate the classification model's performance using metrics like accuracy, precision, recall, and F1-score. Expected R Tasks: • Data normalization using scale(). • Hierarchical clustering using hclust() and visualization with ggdendro or ggplot2. . Determining optimal clusters with factoextra or cluster packages. • Building classification models using rpart for Decision Trees or randomForest package. • Model evaluation with caret package.

Question 2: Hierarchical Clustering and Classification Objective: Segment students into distinct groups based on their academic and extracurricular profiles and analyze the characteristics of each cluster. Tasks: 1. Data Preparation: . Load the mathnew.csv dataset. Select relevant features for clustering: Age, StudyHours, AttendanceRate, Extracurricular, MathScore. Normalize the selected features to ensure equal weighting in clustering. 2. Clustering: Determine the optimal number of clusters using methods such as the Elbow Method and Silhouette Analysis. Perform hierarchical clustering using Ward's method. Visualize the dendrogram to illustrate the clustering process. 3. Cluster Validation: Validate the stability of the clusters using bootstrapping or by splitting the data into training and testing sets. Compute cluster validity indices such as Silhouette Score, Davies-Bouldin Index, etc. 4. Characterization of Clusters: For each identified cluster, compute the mean and distribution of each variable. Analyze how clusters differ in terms of academic performance (MathScore), study habits (StudyHours), attendance (Attendance Rate), and extracurricular involvement. 5. Classification: Build a classification model (e.g., Decision Tree, Random Forest) to predict cluster membership based on the original features. Evaluate the classification model's performance using metrics like accuracy, precision, recall, and F1-score. Expected R Tasks: • Data normalization using scale(). • Hierarchical clustering using hclust() and visualization with ggdendro or ggplot2. . Determining optimal clusters with factoextra or cluster packages. • Building classification models using rpart for Decision Trees or randomForest package. • Model evaluation with caret package.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.3: Measures Of Spread
Problem 8PPS
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These question need to be solved using R with the given data, please do not provide AI solution , also i need detailed solution , do everything in detail which is required, answer it as soon as possible.

Question 2: Hierarchical Clustering and Classification Objective: Segment students into distinct groups based on their academic and extracurricular profiles and analyze the characteristics of each cluster. Tasks: 1. Data Preparation: . Load the mathnew.csv dataset. Select relevant features for clustering: Age, StudyHours, AttendanceRate, Extracurricular, MathScore. Normalize the selected features to ensure equal weighting in clustering. 2. Clustering: Determine the optimal number of clusters using methods such as the Elbow Method and Silhouette Analysis. Perform hierarchical clustering using Ward's method. Visualize the dendrogram to illustrate the clustering process. 3. Cluster Validation: Validate the stability of the clusters using bootstrapping or by splitting the data into training and testing sets. Compute cluster validity indices such as Silhouette Score, Davies-Bouldin Index, etc. 4. Characterization of Clusters: For each identified cluster, compute the mean and distribution of each variable. Analyze how clusters differ in terms of academic performance (MathScore), study habits (StudyHours), attendance (Attendance Rate), and extracurricular involvement. 5. Classification: Build a classification model (e.g., Decision Tree, Random Forest) to predict cluster membership based on the original features. Evaluate the classification model's performance using metrics like accuracy, precision, recall, and F1-score. Expected R Tasks: • Data normalization using scale(). • Hierarchical clustering using hclust() and visualization with ggdendro or ggplot2. . Determining optimal clusters with factoextra or cluster packages. • Building classification models using rpart for Decision Trees or randomForest package. • Model evaluation with caret package.
Transcribed Image Text:Question 2: Hierarchical Clustering and Classification Objective: Segment students into distinct groups based on their academic and extracurricular profiles and analyze the characteristics of each cluster. Tasks: 1. Data Preparation: . Load the mathnew.csv dataset. Select relevant features for clustering: Age, StudyHours, AttendanceRate, Extracurricular, MathScore. Normalize the selected features to ensure equal weighting in clustering. 2. Clustering: Determine the optimal number of clusters using methods such as the Elbow Method and Silhouette Analysis. Perform hierarchical clustering using Ward's method. Visualize the dendrogram to illustrate the clustering process. 3. Cluster Validation: Validate the stability of the clusters using bootstrapping or by splitting the data into training and testing sets. Compute cluster validity indices such as Silhouette Score, Davies-Bouldin Index, etc. 4. Characterization of Clusters: For each identified cluster, compute the mean and distribution of each variable. Analyze how clusters differ in terms of academic performance (MathScore), study habits (StudyHours), attendance (Attendance Rate), and extracurricular involvement. 5. Classification: Build a classification model (e.g., Decision Tree, Random Forest) to predict cluster membership based on the original features. Evaluate the classification model's performance using metrics like accuracy, precision, recall, and F1-score. Expected R Tasks: • Data normalization using scale(). • Hierarchical clustering using hclust() and visualization with ggdendro or ggplot2. . Determining optimal clusters with factoextra or cluster packages. • Building classification models using rpart for Decision Trees or randomForest package. • Model evaluation with caret package.
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