This function could take the column like genres, cast or director then count the values of these columns to find out more filmed genres or the cast or director more filmed in this time of period.We are going to write a function to find out the most filmed genres, cast or director.The splint_count_data function takes a column with the information which we want to count and find out the most being one in a given column then make it bar plot and pie chart with the percentage.We also look for popularity and vote count column using the top_10 function to see the most popular film and most counted film.Let’s explore the popularity using the top_10 function, and the also investigate the vote_count to find out most voted movies in TMDB website.Let’s try the found out if there is any correlation between this variable.We analysis the TMDB dataset which is collected between 1960 to 2015. 8-According to TMDB dataset… 7- The most profitable mounts are June, December, and May. 2. Juzer Shakir • updated 2 years ago (Version 1) Link: The movie dataset contains 4803 rows and 20 columns.The first part of data cleaning involves removal of spurious characters (Ã) from a the movie title, genre and plot keyword columns. 2282. Ritayan Dhara • updated 3 months ago (Version 2) By using Kaggle, you agree to our use of cookies. Dataset. Some points that we can make by looking at the plots and charts we plotted are as follows : This movie also has the lowest profit. Got it. You can try it for yourself here.
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This dataset contains various details about movies for our analysis. I want to analyse the given dataset to answer questions about the film industry like which movies have the highest average vote (IMDB rating), top highest grossing movie, movies with highest budget etc.The dataset has been scraped from Kaggle and manipulated according to the questions we want to answer in our analysis.After obtaining cleaned dats, we perform exploratory data analysis on our dataset. Learn more.
Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The original data source comes from Kaggle. Our goal here finding the answer utilizing this dataset.
Post this, the count of each genre was calculated and a wordcloud made it clear which genre is the most popular.Added a new column for year of release since we want to categorize movies according to yearHeat map of number of movies by year and country : This shows us number of movies released segregated by years in various countries. We will create the function to facilitate the answer the questions before going into exploratory data analysis.This function is to find out the min and the max value of any given column. The goal of this project is to derive insights about the dataset : TMDB movie dataset taken from Kaggle. 名称：5000 TMDB Movie Dataset（来自Kaggle数据分析竞赛平台） 目标：假如你作为一名业务分析顾问，你的客户（某电影公司）想知道他们制作的电影在上映之前是否‘成功’，需要你帮他们了解： 电影类型随时间的变化？ 哪些类型电影拍摄次数多？哪些类型电影赚钱？ We can see that United States witnesses the largest number of releases in the recent years.Let us also look at the boxplot of the average votes received and the movie release month.What good is our analysis if we cannot extract meaningful insights about the data columns ! Individual analysis is presented further in the report along with codes.I have used the TMDB Movie Dataset available on Kaggle. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. This product uses the TMDb API but is not endorsed or certified by TMDb. We could summaries this analysis result in the following items.#’duplicated()’ function return the duplicate row as True and othter as False#Let's drop these row using 'drop_duplicates()' function#Changing Format Of Release Date Into Datetime Formatdf[['budget','revenue']] = df[['budget','revenue']].replace(0,np.NAN)del_col = ['imdb_id', 'homepage','tagline', 'keywords', 'overview','vote_average', 'budget_adj','revenue_adj']df_related = df[['profit','budget','revenue','runtime', 'vote_count','popularity','release_year']]
So I tried creating different dataframes for extracting data from the json object. By using Kaggle, you agree to our use of cookies. TMDB Box Office Prediction Can you predict a movie's worldwide box office revenue?
Dataset. 22. Kaggle平台上下载2个原始数据集：tmdb_5000_movies.csv和tmdb_5000_credits.csv，前者存放电影的基本信息，后者存放电影的演职员名单.
Duplicate data will skew our analysis hence needs to be removed.When I glimpsed through the data file, I could see that some of the colums were in JSON format. See the part 2 Investigating Dataset contains information about 10k+ movies collected from TMDb
本项目数据来源于kaggle上的TMDB 5000 Movie Dataset数据集，共计4803条电影数据。本项目主要目的是通过对历史电影数据的分析研究，为电影的制作提供数据支持。. 从电影市场趋势，受众喜好，电影票房等三个方面主要研究以下几个问题： That reason you probably didn’t hear the movie… We are going to write another function to answer the following question. Q&A for Work.
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