This repository contains Python code for analyzing a movie dataset using popular data analysis and visualization libraries. The dataset includes information about movies, such as their titles, ratings, genres, release years, scores, votes, directors, budgets, gross earnings, production companies, and more.

You need to find features affecting the ratings of any particular movie and build a model to predict the movie ratings. Create a new dataset [Master_Data] with the following columns MovieID Title UserID Age Gender Occupation Rating. (Hint: (i) Merge two tables at a time. (ii) Merge the tables using two primary keys MovieID & UserId)

Mar 12, 2023 · This post demonstrates how to build a movie feature dataset using Python and the OMDb API. It will do this using a list of scraped movie titles demonstrated in a previous post…

Aug 4, 2023 · In this article, we’ll take a journey into a movie dataset using Python and the Pandas library. We’ll learn about preparing the data, creating visualizations, and even building a movie ...

Since you have just learned Python, you decided to use your Python skills to analyze a movie dataset and explore the ratings of the movies. In our dataset, we have the details of the movies in more than 50 languages, but your friend is interested only in watching English movies.

Mar 29, 2025 · Concretely, the tutorial will a Python library called surprise that contains handy implementations of matrix factorization algorithms to build recommender systems. We will also consider the MovieLens 100K datasets: a popular dataset for movie recommendations, ideal for getting familiar with recommender systems from a practical standpoint.

Feb 14, 2025 · In this project, we perform an exploratory data analysis (EDA) on a dataset of movies to uncover how variables like budget, ratings, and production companies impact box office revenue. Using...

This project analyzes movie data from 2021 to 2023, focusing on various aspects such as ratings, view counts, and title word frequencies. The analysis includes: Data collection from the OMDB API; Data cleaning and preprocessing; Exploratory Data Analysis (EDA) with visualizations; Statistical analysis of movie ratings and view counts

In this data analysis example, you will analyze a dataset of movie ratings to draw various conclusions. You will learn how to: This tutorial teaches you to perform all of the above tasks using Python and its popular pandas and matplotlib libraries. You can download and run the Jupyter Notebook used in this data analysis example here.

Nov 13, 2017 · Identifying the important columns and cleaning the data. Now we will identify the important columns that we will work with in this project. We can extract meaningful data from following columns, adult; budget; revenue; popularity; title; vote_count; vote_average; release_date; runtime