04-9000+-Movies-Dataset-Analysis

Medical Appointment No Show Dataset Analysis

Contents
Dataset Description
Columns Descreption
EDA Questions
Data Wrangling
Data Cleaning
Data Visualization
Conclusion
Built with

Dataset Description:

This data set contains information about +9000 movies extracted from TMDB API.

Columns Descreption:

1. Release_Date: Date when the movie was released.
2. Title: Name of the movie.
3. Overview: Brief summary of the movie.
4. Popularity: It is a very important metric computed by TMDB developers based on the number of views per day, votes per day, number of users marked it as "favorite" and "watchlist" for the data, release date and more other metrics.
5. Vote_Count: Total votes received from the viewers.
6. Vote_Average: Average rating based on vote count and the number of viewers out of 10.
7. Original_Language: Original language of the movies. Dubbed version is not considered to be original language.
8. Genre: Categories the movie it can be classified as.
9. Poster_Url: Url of the movie poster.

EDA Questions:

• Q1: What is the most frequent genre in the dataset?
• Q2: What genres has highest votes?
• Q3: What movie got the highest popularity? what's its genre?
• Q4: Which year has the most filmmed movies?

Data Wrangling:

Our data can be found on mymoviedb.csv file provided on this repository, downloaded from Kaggle.

Data Cleaning:

Exploration Summery

• we have a dataframe consisting of 9827 rows and 9 columns.
• our dataset looks a bit tidy with no NaNs nor duplicated values.
• Release_Date column needs to be casted into date time and to extract only the year value.
• Overview, Original_Languege and Poster-Url wouldn't be so useful during analysis, so we'll drop them.
• there is noticable outliers in Popularity column
• Vote_Average bettter be categorised for proper analysis.
• Genre column has comma saperated values and white spaces that needs to be handled and casted into category.

We endded up with a datafram of a total of 6 columns and 25551 rows to dig into during our analysis after comleting our cleaning.

Data Visualization

Using Matplotlib and Seaborn, we made several meaningful visuals and charts to help us gain informative insights regarding any correlation between attributes in our dataset, that'll be discussed in the next section.

Conclusion

These are derived conclusions after completing our data visualisation phase.

Q1: What is the most frequent genre in the dataset?

Drama genre is the most frequent genre in our dataset and has appeared more than 14% of the times among 19 other genres.

Q2: What genres has highest votes?

we have 25.5% of our dataset with popular vote (6520 rows). Drama again gets the highest popularity among fans by being having more than 18.5% of movies popularities.

Q3: What movie got the highest popularity? what's its genre?

Spider-Man: No Way Home has the highest popularity rate in our dataset and it has genres of Action, Adventure and Sience Fiction.

Q4: Which year has the most filmmed movies?

year 2020 has the highest filmming rate in our dataset.

Built with:

• JupyterLab
• Python3
• Pandas
• Numpy
• Matplotlib
• Seaborn