Introduction

In the dynamic and evolving field of Data Science, the ability to extract valuable insights from diverse data sources is pivotal. This project, developed as part of the Data and Web Mining course at Ca’ Foscari University of Venice, encapsulates a comprehensive exploration of key methodologies and techniques essential for data scientists.

Data Retrieval

The art of extracting data is then explored, delving into a variety of retrieval methods. This includes querying databases, leveraging APIs, web scraping, and adept handling of data import/export procedures. This module equips us with the skills to gather data from multiple sources, a necessity in the web-dominated era of information.

Data Preprocessing

We embark on our journey with data preprocessing, a critical step to ensure data quality and relevance. Our focus here is on techniques for data cleaning, normalization, and handling missing values, alongside advanced feature engineering methods to prepare datasets for effective analysis.

Exploratory Data Analysis

We undertake exploratory data analysis to uncover hidden patterns and relationships within data. Employing techniques like correlation analysis and visualizations, we develop an intuitive understanding of the data, guiding subsequent analytical efforts.

Supervised Learning

In the realm of supervised learning, we explore a spectrum of algorithms, evaluating their performance and applicability. Key concepts like overfitting, underfitting, model selection, and hyperparameter tuning are examined, highlighting the nuances of building and refining predictive models.

Time Series Analysis

We then navigate the intricacies of time series analysis, a crucial component for understanding trends and making forecasts. Through the study of ARIMA models and diagnostic tests, we gain insights into pattern recognition and prediction in time-dependent data, an invaluable skill in many real-world applications.

Reinforcement Learning

Shifting gears to a more algorithmic perspective, we delve into reinforcement learning. Here, we unravel the complexities of Markov Decision Processes, understand the intricacies of reward systems, and grapple with the delicate balance of exploration and exploitation, laying the foundation for autonomous decision-making systems.