![[Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings @stratascratch](https://i.ytimg.com/vi/cM12QtrhdLo/maxresdefault.jpg)
Uploaded May 2023 | Updated September 2026, 2 hours ago
Join us as we dive into a data science project from Facebook to predict Rotten Tomatoes movie ratings. We explored two different approaches. In the first approach, we focus on using numerical and categorical features, while in the second approach, we analyze the sentiment of movie reviews. Throughout the video, we compare our models' predictions to actual data to evaluate their performance. By the end of this series, you'll have a deeper understanding of how Rotten Tomatoes movie ratings can be predicted and gain valuable insights into machine learning techniques. Don't forget to subscribe to stay tuned and learn more about data science.
Watch the full tutorial:
📌[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: youtu.be/Ih6G5hnn30Q
📌 [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings:
youtu.be/J4rheMtvu6g
Go to the project through the link below and follow along with me👇
platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
______________________________________________________________________
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
______________________________________________________________________
Timeline:
Intro: (0:00)
Take-home assignment: (0:09)
Exploring the data: (1:48)
Summary of the data: (4:53)
Content rating variable visualization: (5:22)
Audience status visualization: (6:40)
Distribution of each audience status category: (7:04)
Take away: (10:03)
______________________________________________________________________
About The Platform:
I'm using StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from me, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
#datascienceproject
Join us as we dive into a data science project from Facebook to predict Rotten Tomatoes movie ratings. We explored two different approaches. In the first approach, we focus on using numerical and categorical features, while in the second approach, we analyze the sentiment of movie reviews. Throughout the video, we compare our models' predictions to actual data to evaluate their performance. By the end of this series, you'll have a deeper understanding of how Rotten Tomatoes movie ratings can be predicted and gain valuable insights into machine learning techniques. Don't forget to subscribe to stay tuned and learn more about data science.
Watch the full tutorial:
📌[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: youtu.be/Ih6G5hnn30Q
📌 [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings:
youtu.be/J4rheMtvu6g
Go to the project through the link below and follow along with me👇
platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
______________________________________________________________________
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
______________________________________________________________________
Timeline:
Intro: (0:00)
Take-home assignment: (0:09)
Exploring the data: (1:48)
Summary of the data: (4:53)
Content rating variable visualization: (5:22)
Audience status visualization: (6:40)
Distribution of each audience status category: (7:04)
Take away: (10:03)
______________________________________________________________________
About The Platform:
I'm using StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from me, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
#datascienceproject
![[Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings](https://i.ytimg.com/vi/cM12QtrhdLo/mqdefault.jpg "[Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings
Join us as we dive into a data science project from Facebook to predict Rotten Tomatoes movie ratings. We explored two different approaches. In the first approach, we focus on using numerical and categorical features, while in the second approach, we analyze the sentiment of movie reviews. Throughout the video, we compare our models predictions to actual data to evaluate their performance. By the end of this series, youll have a deeper understanding of how Rotten Tomatoes movie ratings can be predicted and gain valuable insights into machine learning techniques. Dont forget to subscribe to stay tuned and learn more about data science.
Watch the full tutorial:
📌[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: https://youtu.be/Ih6G5hnn30Q
📌 [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings:
https://youtu.be/J4rheMtvu6g
Go to the project through the link below and follow along with me👇
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
Timeline:
Intro: (0:00)
Take-home assignment: (0:09)
Exploring the data: (1:48)
Summary of the data: (4:53)
Content rating variable visualization: (5:22)
Audience status visualization: (6:40)
Distribution of each audience status category: (7:04)
Take away: (10:03)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from me, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#datascienceproject")
![Tricky Data Science Interview Question [By Facebook]](https://i.ytimg.com/vi/eC7MdwKCCOE/mqdefault.jpg "Tricky Data Science Interview Question [By Facebook]
Today well cover a tricky data science interview question asked by Facebook. Its not so much a tricky problem as it is a problem with a non-obvious solution. But these types of questions are asked all the time on interviews because theyre scenarios that youd have to handle everyday as a data scientist. Lets cover what this questions all about. Follow along with me by going to the question below.
Link to question: https://platform.stratascratch.com/coding/10064-highest-energy-consumption?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
This series is for both beginner and intermediate data scientists and analysts interested in learning how to solve common data science interview questions in SQL.
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timestamps:
Intro: (0:00)
Question: (1:40)
Exploring the datasets: (2:03)
The 1st trick!: (2:40)
Write out approach: (4:34)
The 2nd trick!: (5:35)
Coding the solution: (6:35)
Conclusion: (10:50)
About The Platform:
Im using StrataScratch, a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#sql #datascience #sqlinterviews")
![Working with APIs in Python [For Your Data Science Project]](https://i.ytimg.com/vi/fklHBWow8vE/mqdefault.jpg "Working with APIs in Python [For Your Data Science Project]
We’re going to be working with the Youtube API to collect video statistics from my channel using the requests python library to make an API call and save it as a pandas dataframe. Working with APIs is a necessary skillset for all data scientists and should be incorporated into your data science projects. I talk about the one data science project you’ll ever need in this video https://bit.ly/3rEt6WG so we’ll start with the first step and learn how to work with APIs in python to collect our data.
The python notebook and links to resources are located in this Github repo: https://github.com/Strata-Scratch/api-youtube/blob/main/README.md
Link to the video referred to in the Intro: https://www.youtube.com/watch?v=c4Af2FcgamA
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timeline:
Intro: (0:00)
Coding on Google Colab: (2:00)
Testing with the Requests Library: (4:16)
Working with the YouTube API: (6:32)
Response from Making API Call: (11:00)
Data is in the items Key: (12:22)
Parsing through the Data: (12:57)
Creating the Loop: (16:17)
Making a Second API Call: (18:30)
Saving to a Pandas DataFrame: (20:31)
Implementing Good Software Engineering Fundamentals: (22:40)
Conclusion: (27:03)
If you want data science interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT%20description%20link&utm_content=APIs%20in%20Python. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#PythonAPI")
