![Mastering Movie Classification: Rotten Tomatoes Data Project [Part 2] @stratascratch](https://i.ytimg.com/vi/kcTrK4xxhPk/maxresdefault.jpg)
Uploaded June 2023 | Updated September 2026, 5 hours ago
Welcome to the final episode of our Rotten Tomatoes Data Project series! In this video, we dive into the exciting world of movie rating predictions. We've built a high-performance classification algorithm to determine whether a movie is labeled as rotten, fresh, or certified fresh. Join us as we explore the steps involved in predicting a movie's status and discuss tips to enhance the performance of our model. From gathering and analyzing reviews to using random forest models, we'll guide you through the process. Don't forget to like, subscribe, and leave a comment with your thoughts and suggestions.
Go to the project through the link below and follow along with us👇
platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2
Don't miss the previous part:
📌 Data Project: Analyzing Review Sentiment for Accurate Ratings [Part 1]: youtu.be/Qh9ajFNeIEc
______________________________________________________________________
👉 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+s2+p2
______________________________________________________________________
Timeline:
Intro: (0:00)
Approach review: (0:44)
Predicting the Movies status: (2:25)
'Body of Lies' status prediction: (2:31)
'Angel Heart' status prediction: (4:09)
'The Duchess' status prediction: (5:37)
Improving the model's performance: (6:27)
Take away: (7:13)
______________________________________________________________________
About The Platform:
StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2) 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+p2. 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 final episode of our Rotten Tomatoes Data Project series! In this video, we dive into the exciting world of movie rating predictions. We've built a high-performance classification algorithm to determine whether a movie is labeled as rotten, fresh, or certified fresh. Join us as we explore the steps involved in predicting a movie's status and discuss tips to enhance the performance of our model. From gathering and analyzing reviews to using random forest models, we'll guide you through the process. Don't forget to like, subscribe, and leave a comment with your thoughts and suggestions.
Go to the project through the link below and follow along with us👇
platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2
Don't miss the previous part:
📌 Data Project: Analyzing Review Sentiment for Accurate Ratings [Part 1]: youtu.be/Qh9ajFNeIEc
______________________________________________________________________
👉 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+s2+p2
______________________________________________________________________
Timeline:
Intro: (0:00)
Approach review: (0:44)
Predicting the Movies status: (2:25)
'Body of Lies' status prediction: (2:31)
'Angel Heart' status prediction: (4:09)
'The Duchess' status prediction: (5:37)
Improving the model's performance: (6:27)
Take away: (7:13)
______________________________________________________________________
About The Platform:
StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2) 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+p2. 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
![Mastering Movie Classification: Rotten Tomatoes Data Project [Part 2]](https://i.ytimg.com/vi/kcTrK4xxhPk/mqdefault.jpg "Mastering Movie Classification: Rotten Tomatoes Data Project [Part 2]
Welcome to the final episode of our Rotten Tomatoes Data Project series! In this video, we dive into the exciting world of movie rating predictions. Weve built a high-performance classification algorithm to determine whether a movie is labeled as rotten, fresh, or certified fresh. Join us as we explore the steps involved in predicting a movies status and discuss tips to enhance the performance of our model. From gathering and analyzing reviews to using random forest models, well guide you through the process. Dont forget to like, subscribe, and leave a comment with your thoughts and suggestions.
Go to the project through the link below and follow along with us👇
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2
Dont miss the previous part:
📌 Data Project: Analyzing Review Sentiment for Accurate Ratings [Part 1]: https://youtu.be/Qh9ajFNeIEc
👉 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+s2+p2
Timeline:
Intro: (0:00)
Approach review: (0:44)
Predicting the Movies status: (2:25)
Body of Lies status prediction: (2:31)
Angel Heart status prediction: (4:09)
The Duchess status prediction: (5:37)
Improving the models performance: (6:27)
Take away: (7:13)
About The Platform:
StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2) 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 https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p2. 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")
![Top Data Science Interview Questions in 2021 [3 concepts tested on all interviews]](https://i.ytimg.com/vi/lG0PbUq4wkg/mqdefault.jpg "Top Data Science Interview Questions in 2021 [3 concepts tested on all interviews]
We’re going to go over the most common data science interview question for 2021 in this video. Really it’s 3 of the most common concepts tested on all coding interviews wrapped in one question. I see this question (or version of this question) on almost every interview I’ve been on or given. Follow along with me and see if you would be able to answer this question.
Link to the question: https://platform.stratascratch.com/coding/10300-premium-vs-freemium?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
This question is from Microsoft but versions of this has been found at other tech companies like Facebook, Google, Airbnb, Doordash, and others. The 3 concepts tested are SQL JOINs, CASE statements, and subqueries / common table expressions (SQL CTEs).
👉 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)
3 common technical concepts: (0:47)
Interview question: (1:20)
1st concept - Advanced JOINs: (1:42)
2nd concept - CASE statements: (4:52)
3rd concept - Subquery / CTE:: (7:46)
Conclusion: (10:41)
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.
I created this platform because I wanted to build a resource to specifically help prepare data scientists for their technical interviews and to generally improve their analytical skills. Over my career as a data scientist, I never was able to find a dedicated platform for data science interview prep. LeetCode and HackerRank were the closest but these platforms specifically serve the computer developer community so their questions focus more on algorithms that working with data.
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")
![Collinearity and Removing Redundancies [DoorDash Data Science Project]](https://i.ytimg.com/vi/m3zEV10qvE8/mqdefault.jpg "Collinearity and Removing Redundancies [DoorDash Data Science Project]
In this video, were going to learn how to use the corr() method to create data showing correlation. In the first part of this tutorial, we discussed how to prepare the data for modeling. In this part, well get acquainted with collinear features and the importance of removing redundancy in our data. By removing redundancy, well be able to improve our datas overall accuracy and make it easier to understand. This is an essential step in data analysis, and youll want to pay attention to it when working with data!
Watch our previous video:
📌 Part 1: Data Preparation for Modeling: https://youtu.be/Sf6jn8QZHhc
🧑💻 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&utm_content=collinearity+%26+removing+redundancies
👉 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)
Data project: (0:25 )
The approach: (1:51)
Creating a mask: (2:40)
Functions to test the correlations: (4:00)
Feature engineering: (7:15)
Conclusion: (8:00)
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")
