FilmAffinity

FilmAffinity project

FilmAffinity Web Scraping Project

Overview

This project was developed to explore web scraping techniques using BeautifulSoup. It focuses on extracting data from the FilmAffinity website to analyze Oscar-nominated actors and actresses, identifying their most iconic films by genre.

Objective

The primary goal of this project is to:

• Identify the Oscar-nominated actors and actresses.
• Categorize their most famous films by genre.
• Analyze trends in film genres related to Oscar-nominated performances.

Key Features

• Data Extraction: The project scrapes film data from FilmAffinity using BeautifulSoup, focusing on actors/actresses who have been nominated for Oscars.
• Genre Classification: Automatically classifies the films based on their genre.
• Oscar-Nominated Performers: Isolates actors and actresses who have been candidates for an Oscar, providing insights into their most significant works across different genres.

Technologies Used

• Python: Core programming language for the project.
• BeautifulSoup: Web scraping library used to extract and parse data from HTML.
• Pandas: For data manipulation and analysis.
• Requests: To fetch HTML content from FilmAffinity.
• Streamlit: For creating the interactive web application.

Results

• Comprehensive List: A comprehensive list of Oscar-nominated actors and actresses, along with their most iconic films categorized by genre.

• Nominations by Genre: The analysis showed that the drama genre received the highest number of nominations over the years, followed by action, comedy and horror

  Figure 2: Nominations by Genre over the Years

• Trend Analysis: A linear regression analysis was performed to identify trends in nominations by genre. The results indicated an increasing trend in nominations for the drama genre and action, while horror and comedy genre showed a decline in nominations.

  Figure 3: Linear Regression of Nominations by Genre

• Top Performers: The analysis identified the top-performing movies based on the number of nominations. For instance, “As Good As It Gets” received the most nominations with 10 nominations, and the actor with the most nominations is Jack Nicholson with 18 nominations.

  Movie Title	Year	Genre	Nominations
  As Good As It Gets	1997	Comedy	10
  The Dark Knight	2008	Action	10

  Actor	Nominations
  Jack Nicholson	18

Learnings

This project provided hands-on experience with:

• Web Scraping: Gained practical skills in web scraping using BeautifulSoup, understanding how to navigate HTML structures and extract relevant data.
• Data Processing and Cleaning: Learned how to process and clean large datasets using Pandas, ensuring the data is ready for analysis.
• Statistical Analysis: Developed a foundational understanding of statistical analysis techniques, including linear regression, to identify trends in data.
• Data Visualization: Enhanced skills in visualizing data using libraries like Matplotlib and Seaborn, making it easier to interpret results and communicate findings effectively.
• Insight Extraction: Gained insights into the relationships between film genres and Oscar nominations, helping to identify trends and patterns in the film industry.

How to Use the Streamlit App

Streamlit Website

1. Clone the Repository:

   
   git clone https://github.com/GabrielFersPin/FilmAffinity.git
   cd FilmAffinity
   

2. Set up a virtual environment (optional but recommended): ````bash python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate
3. Install the required packages: ````bash pip install -r requirements.txt
4. Run the Streamlit app:

   streamlit run app.py
   

[GitHub FilmAffinity Repository]