AutoReel

Project Overview:

This program explores the intersection of vehicles and cinema by analyzing trends, providing recommendations, and leveraging machine learning models to classify and predict relationships between movies and cars. It identifies popular vehicles across decades, correlates genres with vehicle types, and offers interactive visualizations to showcase data insights. The project combines technical skills with creative data storytelling, enabling users to engage with movie car data in innovative ways.

View the Project here: https://github.com/azrabano23/autoreel

Technical Overview:

Features:

Trend Analysis

  • Identifies the most popular vehicles in movies by decade.

  • Analyzes correlations between movie genres and vehicle types.

Interactive Visualizations

  • Builds interactive graphs using Plotly to showcase trends in vehicle usage in movies.

  • Utilizes Folium (if location data exists) for geospatial analysis and mapping insights.

Recommendation System

  • Recommends movies based on selected car models using car-to-movie mapping.

  • Suggests cars appearing in specific movies using movie-to-car mapping.

Machine Learning Models

  • Trains a Random Forest Classifier to predict movie genres based on vehicle data such as car model, year, and brand.

  • Evaluates model performance and accuracy for data-driven genre prediction.

Interactive Dashboard

  • A Dash-based web interface allows users to explore trends by selecting specific car models.

  • Visualizes decade-wise trends for selected vehicles using dynamic bar graphs.

Programming Insights:

  • Coded in Python

    • Dash & Plotly: For creating interactive dashboards and visualizations.

    • Scikit-Learn: For machine learning tasks, including training and evaluating models.

    • Pandas & Matplotlib: For data manipulation, cleaning, and static plotting.

    • Flask: Provides a server for hosting the Dash app.

  • Machine Learning Pipeline

    • Encodes categorical variables (e.g., car models and brands) for machine learning.

    • Splits the data for training and testing a Random Forest Classifier.

    • Measures accuracy to ensure reliable genre predictions.

  • User Interaction

    • Dropdown menus and interactive graphs for exploring trends.

    • Recommendations tailored to user inputs, such as car models or movie titles.

  • Integration

    • Combines data analytics, visualization, and predictive modeling to deliver a seamless experience.

Key Applications:

  1. Data Exploration: Helps analyze trends in car appearances in movies over decades.

  2. Personalized Insights: Provides tailored recommendations for movie enthusiasts and car fans.

  3. Industry Analysis: Assists filmmakers and automotive marketers in understanding the cultural impact of vehicles in cinema.

This project demonstrates expertise in data visualization, machine learning, and interactive web app development, blending technical skills with creative storytelling to deliver a unique perspective on cars in movies.