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MOVIES ANALYSIS PROJECT - PYTHON (Click to view full python script here)

INTRODUCTION:

This project involves analyzing a dataset of movies to uncover insights about various aspects such as directors, release years, and more. The analysis was performed using Python, focusing on data cleaning and exploratory data analysis.

This project is a step-by-step walkthrough of the process used in analyzing movies dataset. Using the pandas library, the dataset was imported to Python (in Visual Studio Code) and found to contain 7789 rows of data and 11 field columns. 

                                                                     A LOOK AT DATASET

DATA CLEANING​:

Data cleaning is a crucial step in any data analysis project to ensure the dataset is accurate and reliable. Here are the key data cleaning steps I performed:

  1. Removing Duplicates: Duplicate entries can skew the analysis results. I used pandas to identify and remove any duplicate rows in the dataset.
  2. Looking Missing Values: Missing values were either filled with appropriate values or removed, depending on the context and the importance of the missing data.
  3. Converting Data Types: Ensured all columns had the correct data types. For example, converting columns like ‘Release Year’ from format ' August 14, 2020 ' to Date format ' 2020-08-14 '.

                                                                A LOOK AT DUPLICATED DATASET

DATA CLEANING​:

For this project, I used the following Python libraries:

  • pandas: For data manipulation and cleaning.
  • matplotlib: For data visualization.

DATA ANALYSIS:

After cleaning the data, I performed various analyses to gain insights into the movie dataset. Here are some of the key analyses:

  • Top Directors by Number of Movies: Identified directors who have directed the most movies.
  • Number of Movies Released Each Year: Total number of movies releases per year 
  • Country Wise Analysis: Looking at different country and their releases
  • Distribution of Movie Genres: Analyzed the distribution of different genres in the dataset.

VISUALIZATION:

To make the analysis more understandable, I used various visualizations:

Bar Charts: To show the number of movies by years, category etc.

CONCLUSION:

This project provided valuable insights into the movie industry by analyzing various aspects such as directors, release years, and genres. The data cleaning process ensured the dataset was accurate and reliable, and the analysis helped uncover trends and patterns that can be useful for stakeholders in the movie industry.

Click to view full python script here