Boyd Malaika

Movie Dataset Analysis

Overview

This project involves analyzing a movie dataset obtained from Kaggle. The analysis aims to extract meaningful insights related to movie ratings, revenue, actors, and genres. The dataset includes various attributes, such as titles, genres, actors, release years, and revenue.

Dataset Description

• Source: Kaggle
• Columns:
  • Title: The title of the movie.
  • Genre: The genre(s) of the movie (comma-separated).
  • Actors: The main actors in the movie (comma-separated).
  • Release Year: The year the movie was released.
  • Revenue: The revenue generated by the movie.

Analysis Questions

The following questions were explored in this analysis:

1. What is the highest rated movie in the dataset?
2. What is the average revenue of all the movies in the dataset?
3. What is the average revenue from 2015 to 2017 in the dataset?
4. How many movies were released in the year 2016?
5. HOw many movies where directed by Christopher Nolan?
6. How many movies in the data set have a rating higher than 8.0?
7. What is the mean rating of movies directed by Christopher Nolan?
8. Find the year with the highest rating.
9. What is the percentage increase in the number of movies made between 2006 and 2016?
10. Find the most common actor in all the movies.
    • Note, the "Actors" column has multiple actor names. You must find a way to search for the most commin actor in all the movies.
11. ** How many uniqure genre are there in the dataset?**
    • Note, the "Genre" column has multiple genres per movie. You must find a way to identify them individually.

Steps Taken

1. Data Cleaning:

   • Converted necessary columns to string types for processing.
   • Handled missing or malformed entries in the dataset.

2. Data Transformation:

   • Split multi-valued fields (e.g., actors and genres) into individual entries.
   • Exploded lists into separate rows for analysis.

3. Data Analysis:

   • Used pandas for data manipulation and calculations.
   • Extracted insights such as averages, counts, and unique values.

Usage

To replicate this analysis, ensure you have Python installed with the following libraries:

• pandas
• numpy
• (Any other libraries you might have used)

Installation

pip install pandas numpy