Category: Programming

Text Classification using Python or R For this assignment, you will do the following: Dataset selection Download a labeled text classification CSV dataset from a reputable source (e.g., Kaggle or a government website). Briefly state the source and task. Provide a clear description of the dataset you selected. Model Development (Python or R) Write a Python or R program to develop an NLP text classification model, including these steps: Load and explore the data. Preprocess the text (e.g., lowercase, remove punctuation/stopwords). Convert text to numerical features (e.g., BoW, TF-IDF). Train a classification model (e.g., Naive Bayes). Evaluate the model on a test set, reporting accuracy, precision, recall, F1-score, and the confusion matrix. Interpretation and Documentation Briefly interpret your evaluation metrics and discuss any challenges or potential improvements, focusing on: Overall Performance: What does the accuracy score tell you about how well your model generally classifies the text data? Class-Specific Performance: Examine the precision and recall for each class. Are there any classes that your model struggles to classify correctly? How are these reflected in the confusion matrix (e.g., high false positives or false negatives)? Limitations and Improvements: Briefly discuss one potential limitation of your chosen approach (e.g., the feature extraction method or the model) and suggest one way you could potentially improve the model’s performance in the future. Submission Guidelines Include the CSV dataset and dataset description in your submission Copy and paste your well-commented Python or R code and the documented results, interpretation, and discussion directly into a Word or Google Document.

Cluster Analysis using Python or R For this assignment, you will do the following: Dataset Selection Download a clustering CSV dataset file from a reputable site, such as Kaggle or a government website. The dataset should be suitable for clustering (i.e., it should contain multiple features that can be used to group data points). Provide a clear description of the dataset you selected, including its source, the number of features, the number of data points, and the types of variables it contains. Model Development (Python or R) Write a Python or R program to perform K-means clustering on the dataset. Your program should include the following steps: Import the necessary libraries (e.g., scikit-learn in Python, stats in R). Load the dataset into a data frame. Preprocess the data as needed, including handling missing values, scaling/normalizing features, and encoding categorical variables (if applicable). Implement the K-means algorithm to cluster the data. Determine the optimal number of clusters (k) using the elbow method, silhouette analysis, or another appropriate technique. If possible, visualize the clustering results (e.g., plot the data points in a reduced-dimensional space, with different colors representing different clusters). Evaluate the quality of the clustering (e.g., using the silhouette score Document your code with clear comments, explaining each step of the process. Interpretation and Documentation Interpret the results of your clustering analysis. Describe the characteristics of each cluster. Discuss any limitations of your analysis or potential sources of bias. Submission Guidelines Include the CSV dataset and dataset description in your submission Copy and paste your well-commented Python or R code and the documented results, interpretation, and discussion directly into a Word or Google Document.