In this project, we analyzed the bookcrossing dataset. The dataset contains three CSV files which are ratings, users, and books. We explored the data and preprocessed it. We applied a dimesionaltiy reduction technique (PCA), three classification algorithms (Logistic Regression, Decision Tree, K-Nearest Neighbors), and two clustering algorithms (K-Means and Hierarchical) to build models from the dataset. Our project contains the following parts:

• Dataset

• Exploratory data analysis

• Visualization techniques

• Imbalanced data set

• Missing data imputation

• Multicollinearity

• Logistic Regression

• PCA

  • PCA with Logistic Regression

• Clustering

  • K-Means Clustering
  • Hierarchical Clustering
    • Missing Data with Hierarchical Clustering

• Classification

  • Decision Tree
    • Decision tree with imbalanced data
  • K-Nearest Neighbors (K-NN)
  
  

Ayşe Ceren Çiçek & Gizem Kurnaz