Uploaded June 2023 | Updated September 2026, 2 hours ago
Welcome to the second series of the Facebook data science project for predicting movie ratings! In this video, we explore building a high-performance classification algorithm to predict movie ratings. We merge two data sets, focusing on review sentiment to determine whether a movie is labeled as "Rotten" or "Fresh." Join us as we preprocess the data, analyze the distribution of review types, and implement a random forest model. Don't forget to subscribe and stay tuned for the next video in our series!
Go to the project through the link below and follow along with usπ
platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1
Watch the previous videos from the first series:
π [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: youtu.be/cM12QtrhdLo
π[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: youtu.be/Ih6G5hnn30Q
π [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings: youtu.be/J4rheMtvu6g
______________________________________________________________________
π Subscribe to my channel: bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: bit.ly/3jifw81
π Playlist for data science interview tips: bit.ly/2G5hNoJ
π Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
π 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+s2+p1
______________________________________________________________________
Timeline:
Intro: (0:00βββ)
Take-home assignment: (0:18)
Approaches review: (0:31)
Preparing the dataset: (1:10)
Using the head method for data review: (2:34)
Encoding the labels into integer representation: (3:45)
Splitting the dataset into train and test sets: (4:30)
Tokenization in NLP: (4:53)
Using a random forest classifier: (6:21)
Improving the model's performance: (7:33)
Take away: (9:24ββ)
______________________________________________________________________
About The Platform:
StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1) is 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+s2+p1. 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 the StrataScratch team, 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 us at team@stratascratch.com
______________________________________________________________________
#datascienceproject
Welcome to the second series of the Facebook data science project for predicting movie ratings! In this video, we explore building a high-performance classification algorithm to predict movie ratings. We merge two data sets, focusing on review sentiment to determine whether a movie is labeled as "Rotten" or "Fresh." Join us as we preprocess the data, analyze the distribution of review types, and implement a random forest model. Don't forget to subscribe and stay tuned for the next video in our series!
Go to the project through the link below and follow along with usπ
platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1
Watch the previous videos from the first series:
π [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: youtu.be/cM12QtrhdLo
π[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: youtu.be/Ih6G5hnn30Q
π [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings: youtu.be/J4rheMtvu6g
______________________________________________________________________
π Subscribe to my channel: bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: bit.ly/3jifw81
π Playlist for data science interview tips: bit.ly/2G5hNoJ
π Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
π 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+s2+p1
______________________________________________________________________
Timeline:
Intro: (0:00βββ)
Take-home assignment: (0:18)
Approaches review: (0:31)
Preparing the dataset: (1:10)
Using the head method for data review: (2:34)
Encoding the labels into integer representation: (3:45)
Splitting the dataset into train and test sets: (4:30)
Tokenization in NLP: (4:53)
Using a random forest classifier: (6:21)
Improving the model's performance: (7:33)
Take away: (9:24ββ)
______________________________________________________________________
About The Platform:
StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1) is 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+s2+p1. 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 the StrataScratch team, 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 us at team@stratascratch.com
______________________________________________________________________
#datascienceproject

![Rotten Tomatoes Movie Rating Prediction - Data Science Project
Join us in this captivating series as we delve into the world of Rotten Tomatoes Movie Rating Prediction. With two different approaches, we explore the art of building models that can determine if a movie is fresh or rotten based on various features. In the first approach, we dive into numerical and categorical features, while in the second series, we analyze sentiment in movie reviews.
Watch the full tutorial:
π [Part 1]: Rotten Tomatoes Movie Rating Prediction with Machine Learning: https://youtu.be/cM12QtrhdLo
Go to the project through the link below π
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+1
π Subscribe to my channel: https://bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
π Playlist for data science interview tips: https://bit.ly/2G5hNoJ
π Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
π 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
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 Rotten Tomatoes Movie Rating Prediction - Data Science Project](https://i.ytimg.com/vi/REGDQyCBY0I/mqdefault.jpg)




![Data Preparation for Modeling [DoorDash Data Science Project]
This video will walk you through the DoorDash Delivery Duration Prediction data project. Well cover all the stages of the preparation of data for modeling.
π§βπ» Go to the project through the link below and follow along with me: https://platform.stratascratch.com/data-projects/delivery-duration-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Watch the next parts:
π Part 2: Collinearity and Removing Redundancies: https://youtu.be/m3zEV10qvE8
π Part 3: Multicollinearity and Feature Selection: https://youtu.be/gh5JzALBQvU
π Subscribe to my channel: https://bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
π Playlist for data science interview tips: https://bit.ly/2G5hNoJ
π 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: (0:00βββ)
Take-home assignment from DoorDash: (0:10 )
Exploring and understanding the data: (0:47)
Coding the solution: (2:05ββ)
Feature Creation (3:10)
Data Preparation for Modeling (5:34)
Conclusion: (β10:16)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link), 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
#StrataScratch #DoordashDataProject #DataModeling Data Preparation for Modeling [DoorDash Data Science Project]](https://i.ytimg.com/vi/Sf6jn8QZHhc/mqdefault.jpg)

![Most Common Data Science SQL Interview Question from DoorDash [window functions & partitions]
This is the most common data science SQL interview question from DoorDash, a food delivery company. This question tests your ability to split your data into percentiles using window functions. Iβll walk you through solving the question like weβre in an interview and give you some tips on how to approach the solution.
Go to the question through the link below and follow along with me.
Link to the question: https://platform.stratascratch.com/coding/2036-lowest-revenue-generated-restaurants?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
π Subscribe to my channel: https://bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
π Playlist for data science interview tips: https://bit.ly/2G5hNoJ
π 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: (0:00βββ)
Interview Question: (0:35βββ)
Framework to solve the problem: (1:40βββ)
Understand your data: (3:58βββ)
Formulate your approach: (6:40βββ)
Code Execution: (8:55βββ)
Code Optimization: (14:35βββ)
Conclusion: (17:08βββ)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link), 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
#DataScience #SQLInterviewQuestion Most Common Data Science SQL Interview Question from DoorDash [window functions & partitions]](https://i.ytimg.com/vi/T1UhSuKqy3A/mqdefault.jpg)

