IMDb verilerini toplayarak en popüler filmleri ve türleri analiz edip gösteren prototip bir web projesi.

A collection of movies dataset for your ML project or any other task.

This repository contains business-related data analytics projects using Excel, Python, Tableau, and StatPlus. The projects cover tasks like survey data analysis, brochure demand forecasting, and movie data visualization, showcasing data processing, analysis, and visualization techniques.

This repository hosts an interactive Tableau dashboard designed for a deep-dive analysis of "The Movie Dataset." It allows users to visualize and explore trends in film genres, revenue, ratings, and production details to uncover insights into the world of cinema.

Academic data science project on the IMDB 5000 movie dataset featuring EDA, visualization, and ML (Regression & Classification) using R language.

Movies & TV Show Data / Content for SERP Media

Fetch TMDB movie data and export to CSV for NLP and Data Engineering

This project is a robust data scrapper that collects comprehensive information about movies from The Movie Database (TMDB) API. The dataset spans from the earliest recorded films (1874) up to recent releases (2024), providing a rich historical record of cinema evolution, production details, popularity metrics, and financial data.

Fetch movie data from multiple APIs (OMDB, TMDB, IMDB) to CSV, JSON, SQL and Excel.

A Movie Recommendation System is an intelligent application that suggests movies to users based on their interests, preferences, and past interactions

This project explores a comprehensive movie dataset to uncover trends and insights related to runtime, genre popularity, IMDb ratings, director performance, and box office earnings. Using Python (Pandas, Matplotlib, Seaborn), we visualize patterns across thousands of movies, highlighting key factors influencing commercial and critical success.

This project scrapes the IMDb Top 250 Movies and performs data cleaning and exploratory data analysis (EDA) to uncover patterns across ratings, genres, runtimes, release years, and directors. The scraping workflow handles pagination and dynamic content using Beautiful Soup and Selenium.

This project focuses on analyzing movie ratings data using matrix factorization techniques and clustering algorithms. The goal is to identify meaningful segments of users and movies, and to evaluate the clustering results through various metrics. We apply both hierarchical and K-means clustering methods.

Machine learning pipeline for predicting movie box office success. Includes web scraping, data pipelines, feature engineering, ML models, MLflow tracking, and a web interface for predictions.