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Data Exploration and Visualization for Streaming Platform

NetflixIntroGIF (2)

Streaming Platform Data: Cleaning, Analysis and Visualization

Data Analysis and Visualization on Netflix Data to provide insights and recommendations to improve their userbase.

Column #Description
Show_id Unique ID for every Movie / Tv Show
Type Identifier - A Movie or TV Show
Title Title of the Movie / Tv Show
Director Director of the Movie
Cast Actors involved in the movie/show
Country Country where the movie/show was produced
Date_added Date it was added on Netflix
Release_year Actual Release year of the movie/show
Rating TV Rating of the movie/show
Duration Total Duration - in minutes or number of seasons
listed_in Genre
description The summary description

Netflix is one of the most popular media and video streaming platforms. They have over 10000 movies or tv shows available on their platform, as of mid-2021, they have over 222M Subscribers globally. This tabular dataset consists of listings of all the movies and tv shows available on Netflix, along with details such as - cast, directors, ratings, release year, duration, etc.

Aim while exploring this dataset is to analyze the data and generate insights that could help in deciding which type of shows or movies to produce and how they can grow the business in different countries.

We are interested in increasing the revenue of Netflix, hence our main objective is to figure out which all shows and movies performed the best.

Performed following Tasks

  1. Data Cleaning
  2. Analysis
  3. Visualization

  • Observations on the shape of data, data types of all the attributes, conversion of categorical attributes to 'category', missing value detection, statistical summary

  • Non-Graphical Analysis: Value counts and unique attributes ​

  • Visual Analysis - Univariate, Bivariate after pre-processing of the data

  • For continuous variable(s): Distplot, countplot, histogram for univariate analysis

  • For categorical variable(s): Boxplot

  • For correlation: Heatmaps, Pairplots

  • Missing Value & Outlier check

  • Insights based on Non-Graphical and Visual Analysis

    . Comments on the range of attributes

    . Comments on the distribution of the variables and relationship between them

    . Comments for each univariate and bivariate plot

  • Business Insights & Recommendations : Includes patterns observed in the data along with what can infer from it

Description about files in repository Backhand Index Pointing Down Light Skin Tone:

Netflix_Business_Case_Study.ipynb - Colaboratory notebook containing the code for analysis