https://github.com/pspanoudakis/data-mining-techniques-projects

Data Visualization 📊 Clustering and Classification 🗂️ techniques on Customer 🛍️ & Book 📖 datasets
https://github.com/pspanoudakis/data-mining-techniques-projects

customer-personality-analysis goodreads-data matplotlib numpy pandas python-typing seaborn sklearn-classifier sklearn-clustering wordcloud

Last synced: 4 days ago
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Data Visualization 📊 Clustering and Classification 🗂️ techniques on Customer 🛍️ & Book 📖 datasets

• Host: GitHub
• URL: https://github.com/pspanoudakis/data-mining-techniques-projects
• Owner: pspanoudakis
• Created: 2023-04-04T19:16:27.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-01-03T17:42:42.000Z (12 months ago)
• Last Synced: 2024-01-03T18:40:30.956Z (12 months ago)
• Topics: customer-personality-analysis, goodreads-data, matplotlib, numpy, pandas, python-typing, seaborn, sklearn-classifier, sklearn-clustering, wordcloud
• Language: Jupyter Notebook
• Homepage:
• Size: 9.97 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Mining Techniques Projects

This is a series of projects for the Spring 2023 **Data Mining Techniques** course on [DIT@UoA](https://www.di.uoa.gr/en).

## Project 1 - Customer Personality Analysis

Given a dataset which describes the customers of a company, we try to draw deductions on 

- The profile of the customers who are more likely to spend more

- The campaign channels which bring more revenue

- The purchase channels which bring more revenue

To reach such conclusions, we use common data mining techniques:

- Data **preprocessing & cleaning**

- **Generation** of new data features using given ones

- Elimination of **outliers**

- Data **Visualization**, e.g. using heatmaps, histograms and bar plots

- **Principal Component Analysis**, to reduce the number of features of the data to extract clusters from

- Cluster extraction using **Agglomerative Clustering** & **K-Means**

## Project 2 - Book Recommendation & Classification

Given a [Goodreads](https://www.goodreads.com/) books dataset:

- We visualize our data and extract deductions using the techniques mentioned in project 1. We also emphasize on extensive Pandas `DataFrame` manipulation, to collect various metrics and statistics on our data.

- We develop a **Book Recommendation System** which can recommend similar dataset books given a specific book id:

    - We vectorize the description of each book, using **TF-IDF**

    - The recommender caclulates the **cosine similarity** for all book descriptions in an efficient way (see [`Pairwise Calculator`](https://github.com/pspanoudakis/Data-Mining-Techniques-Projects/blob/master/project2/modules/recommender.py#L19))

    - We can then query the recommender to return the most similar books for the given one

- We develop a **Book Genre Classifier**, which estimates the Genre for a book given the description of it:

    - We vectorize each description using the mean of the included [**Word2Vec**](https://radimrehurek.com/gensim/models/word2vec.html) vectors, to create the training & test data

    - We use an scikit-learn base classifier such as **Naive Bayes**, **Random Forest** and **Support Vector Classifier**  to perform **K-Fold Cross-Validation**, calculate metrics (accuracy, f-score, precision & recall) and measure the performance of our classifier.

## Technologies & Tools used for development

- **Pandas** & **NumPy**

- **matplotlib** & **seaborn**

- **scikit-learn**

- VS Code & Google Colab

## Repository content

Both projects include the following:

- `hw[x].pdf` file describing the corresponding project tasks in detail

- `.ipynb` and `.py` implementation files