Kanellaman / Data-Mining-2

Data Analysis and Classification

This code performs data analysis and classification tasks on a dataset of books. It includes the following main steps:

Data Cleaning and Preprocessing

• Reads data from a CSV file (books_1.Best_Books_Ever.csv).
• Extracts and converts the bookId column to integers.
• Drops rows with missing values in specific columns.
• Extracts and preprocesses data for analysis, including handling genres and authors.

Data Analysis

• Generates various insights and statistics about the dataset.
• Creates histograms and displays information about book ratings and page counts.

Recommendation System

Bigrams and Unigrams

• Utilizes the TF-IDF vectorization technique for bigrams and unigrams.
• Calculates cosine similarities between book descriptions.
• Provides book recommendations based on cosine similarity.

Classification

Random Forest, Naive Bayes, and Support Vector Classifier (SVC)

• Preprocesses textual data using Word2Vec.
• Performs classification using Random Forest, Naive Bayes, and Support Vector Classifier.
• Evaluates model performance with precision, recall, F1-score, and accuracy metrics.