In this blog post, I will describe the project I completed to a non-technical audience. For the code, I would encourage you to look at my GitHub repository.
In this blog post, I will describe the project I completed to a non-technical audience. For the code, I would encourage you to look at my
Executives of a new movie studio are after actionable insights to maximise their return on investment and ensure successful movies are produced.
“Avengers: Endgame’s $1.2 billion opening weekend is the biggest in movie history” — Vox, April 2019.
“Box office cats-tastrophe: Cats projected to lose $70m” — The Guardian, December 2019.
From these two contrasting headlines, we see that entering the movie industry can be viewed as a high risk/ high reward venture for our stakeholders. There is potential but need to ensure the “right” movie is made. Through data analysis we will seek to provide recommendations to maximise the chance of success.
The main data used for this project came from two sources.
Data from IMDB consisted of 146,144 entries with start year, runtime and genres as key features.
Data from the-numbers consisted of 5,782 entries with release_date, production_budget, domestic_gross and worldwide_gross as key features.
We also scrapped data from Wikipedia relating to Netflix Original Movies.
The first stage focussed on data preparation including:
Reading and cleaning provided data
Dealing with missing values
Scraping additional data and cleaning it
The second stage focussed on visualisations and insights including:
Conducting feature engineering where applicable
In this tutorial, we'll learn How Are Data analysis and Data science Different From Each Other. Many tend to get confused between Big data analysis and data science and often misuse one in place of the other. Here is the difference between data analysis and data science are different from each other.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.
EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.