14. Project - 9 | Data Analysis | IMDB Movie Dataset | Python Pandas Project | Kaggle Dataset

[@faisalshaikh4480]

WOW, Man!! you have done such amazing work for the Data science community. May God bless you for the wonderful work that you are doing. I just completed this playlist and now jumping on to Data visualization. Looking forward for Machine learning playlist

[@DataThinkers]

Thank you so much for your comment. Glad you like this video.thanks

[@TonderayiKanoz]

Visualizing the location of missing data at 7:55 has been a great one. Many analyst videos have never showed me this. Awesome!!

[@Mars7822]

Many people can jump into this field because of your videos... Keep it up

[@DataThinkers]

Thanks for your comment. Glad you like this video. Sure. Thanks

[@jetspray3]

This is the best of all the others I have seen. I just like how you show the column you are working on and then pull everything together to get the result you want and in that way I start understanding what you are doing. Thumbs up to you, I wish there were other videos on KZbin like that.

[@DataThinkers]

Glad you liked it. You can watch other videos from this play list. kzbin.info/aero/PL_1pt6K-CLoDMEbYy2PcZuITWEjqMfyoA

[@muralikumaar9456]

One of the best Pandas Project playlist you have made. Your channel is so much underrated. Create more machine learning playlist .Wishing you all the best

[@DataThinkers]

Thanks for your comment. because of subscribers like you, our channel is growing. Sure I will create. Keep watching. Thanks.

[@surbhirautaray6872]

You are awesome. Doing amazing work.

[@leolee7884]

Amazing job my friend! You are amazing! Thank you for a wonderful job!

[@shakilahammed1887]

I correctly answered 20 question of 22. Thanks

[@DataThinkers]

Excellent!

[@sketchytv1321]

very informative and easy to understand.Subscribed

[@DataThinkers]

Thanks for your comment. Glad you liked it. Keep watching.

[@l.kennethwells2138]

Thank you so much for this. This tutorial just raised my confidence level.

[@shubhammeshram8504]

Thank you. Great teaching.😀

[@petroliumengineer]

I Really like the video you make, and i learned a lot from it I would like to suggest an easier way to solve the latest 4 questions: question number 20 : bin_edges = [data['Rating'].min() ,data['Rating'].quantile(0.25) ,data['Rating'].quantile(0.75) ,data['Rating'].max() ] bin_names = ['Average', 'Good', 'Excellent'] data['Rating_cat_my_method'] = pd.cut(data['Rating'], bin_edges, labels=bin_names) Question 21,22 and 23 : data['Genre_list']=data['Genre'].apply(lambda x:x.split(',')) data_exploded=data.explode('Genre_list') data_exploded['Genre_list'].value_counts()

[@DataThinkers]

Thanks for your comment.Glad you like this video.keep watching.thanks

[@abhishekpatil492]

Excellent, great work .....

[@DataThinkers]

Thanks for your comment. Glad you like this video. Keep watching. Thanks

[@riyaz8072]

Please create more EDA videos please.. Your videos are so good.

[@DataThinkers]

Thanks for your comment. Glad you like this video. Sure i will. Thanks

[@piyushpathak7311]

Sir we want more projects like this it's awesome plz 🙏🙏 upload sir..

[@DataThinkers]

Thanks for comment dear. I'm working on it. Upload it shortly.Thanks

[@piyushpathak7311]

@@DataThinkers thanks sir upload it soon..

[@petroliumengineer]

Thank you very much.. i see that it is easier to use EXPLODE function to answer Genre questions instead of multiple for loops

[@DataThinkers]

Thanks for your comment.Glad you like this video.keep watching.thanks

[@miguelandrade982]

Thank you, Sir. I did really enjoyed this video. Keep up this great work My solution for the last question: from collections import Counter Counter(data.Genre.apply(lambda x: pd.Series(x.split(','))).stack().values)

[@DataThinkers]

Thanks for your comment. Glad you like this video. Thanks

[@arfanariyanto297]

Thx u very much, its help a lot

[@AustralianYoutuber]

Hello Dear, Great way to teach!! I am getting error for below queries. 1. Create a new column profit in df_movie by subtracting the variables gross and budget. 2. Create a new categorical column sequelcat in df_movie which takes the value sequel if the movie is a sequel, and original otherwise. 3. Find the five most profitable original movies and print their movie title and profit to the console (sorted as highest profit first). 4. Find the five least profitable sequel movies and print these movie title and profit to the console (sorted as lowest profit first).

[@DataThinkers]

check my code : github.com/DataThinkers/Data-Analytics-Projects-Code

[@prabhavsingh654]

"let me" sir. Thank You...

[@cloykorea9874]

fabulous broooooooooooooo

[@DataThinkers]

Thanks for your comment. Glad you like this video. Keep watching. Thanks

[@janakiyeluripati6368]

Sir, can you make videos on regular expressions in pandas. Amazing tutorials

[@DataThinkers]

Thanks for your comment. Sure i will.

[@kishanmaurya422]

nice explanation sir , can make a video on web scraping using selenium and converting it in data frame and then excel

[@DataThinkers]

Thanks for your comment. Glad you like this video.sure i will try.thanks

[@jalego800]

Hello sir, @09:44, it seems that we didn't drop those NAN rows...so the row of 'Grindhouse' is still included in our dataset, right?

[@ranjithraghunathan1267]

Amazing

[@DataThinkers]

Thanks for your comment. Glad you like this video.

[@ranjithraghunathan1267]

@@DataThinkers can you also do videos similar python projects for 1) consolidating multiple excel files in a folder. 2) using loops, nested loops with multiple examples etc

[@DataThinkers]

@@ranjithraghunathan1267 First of all thanks for your comment. sure I will do that

[@fakerrain]

I smashed that like button so very hard. This was amazing content and I learned so much from this video. I will be for sure working through all the playlist. Are there any books you recommend to read for this info? Thank you again for this video.

[@DataThinkers]

😄😄 Thanks for your comment. Glad you like this video. Actually I'm not following any book. You can follow my video tutorials. Thanks.

[@mohseenmohammed46]

excellent

[@DataThinkers]

Thanks for your comment. Glad you like this video. Thanks

[@mohseenmohammed46]

@@DataThinkers please make more videos on complex datasets

[@DataThinkers]

Sure 👍. Thanks

[@riyaz8072]

Please share the playlist of these EDA

[@DataThinkers]

Data Analysis Projects | Pandas Projects | Data Analysis With Python Pandas | Case Studies: kzbin.info/aero/PL_1pt6K-CLoDMEbYy2PcZuITWEjqMfyoA

[@shailesh_jain_]

sir, what is code of visualization of sum of votes based on year in barplot or countplot

[@hairavyadav6579]

if you show how to replace null value using statistics its more better then dropping the null value.

[@mohamad5005]

Thank you but I think there is an easy way instead of the for loop, if there is , could you illustrate what it is?

[@Zelalem-k1c]

Wow, you crashed. Thak you. you helped me so much to work on my project.

[@himanshushorts7143]

Question 22 Another way list1 = df[‘Genre’].str.split(‘,’) list2 = [] for i in list1: list2 += i list2 = list(set(list2)) #typecast Print(len(list2))

[@syedabaduruunnisa3099]

If I want to take top 10 movies which is successfull and even after bad reviews how to get this output.. Pls help

[@NangiMugira]

What about finding the genre with the highest rating.

[@maurocruz1824]

37:00

[@akashyeole2258]

Please tell me uniqueness of this project ?? My teacher is asking me ....please tell me uniqueness of this project

[@DataThinkers]

Based on the list of data analytics questions for the IMDB movie dataset, the uniqueness of your project could stem from the following aspects: Comprehensive Data Analysis: Your project appears to cover a wide range of data analysis tasks, including data exploration, data cleaning, descriptive statistics, data aggregation, and data visualization. This comprehensive approach allows for a holistic analysis of the IMDB movie dataset, providing a thorough understanding of the data and its various aspects. Deep Dive into Movie Attributes: Your project involves examining various attributes of movies, such as title, runtime, revenue, rating, genre, director, and voting, among others. This multifaceted analysis allows for a detailed investigation of different movie characteristics, enabling insights into different dimensions of the movie industry and audience preferences. Data Cleaning and Pre-processing: Your project includes steps to check for missing values, drop them, and identify duplicate data. This demonstrates a meticulous approach to data cleaning and pre-processing, which is crucial for obtaining accurate and reliable analysis results. Time-based Analysis: Your project involves analyzing movies on a yearly basis, such as finding the highest average voting and revenue by year, as well as counting the number of movies per year. This time-based analysis provides insights into how movie-related metrics have evolved over time, revealing trends, patterns, and changes in the industry. Genre Analysis: Your project includes examining movie genres, such as finding unique genre values, counting the number of films per genre, and classifying movies based on genre. This genre-based analysis adds a unique dimension to your project, allowing for insights into popular genres, genre trends, and genre-specific characteristics of movies. Rating and Revenue Relationship: Your project involves investigating the relationship between movie ratings and revenue, as well as finding the average rating for each director. This analysis provides insights into whether ratings affect revenue and sheds light on the interplay between critical acclaim and commercial success in the movie industry. Presentation of Results: Your project may involve presenting the analysis results in a visually appealing and informative manner, such as displaying top 10 movies, creating visualizations, and summarizing statistics. This approach enhances the interpretability and usability of your project, making it more engaging and accessible to stakeholders.

[@akashyeole2258]

@@DataThinkers Plz reply in short 2-3 line ...... Uniqness of this project

[@DataThinkers]

The uniqueness of the project analyzing the IMDB movie dataset lies in its comprehensive data exploration, holistic data cleaning and pre-processing, and diverse analysis tasks, including time-based, director-wise, revenue and rating, movie classification, and genre analysis. This approach provides valuable insights into various aspects of the movie industry, such as trends over time, director performance, factors impacting movie success, and genre preferences, making the project distinct and informative.

[@ishan358]

Can i get code link

[@DataThinkers]

github.com/PRIYANG-BHATT/Data-Analytics-Projects-Code

[@safanaazshaikh2823]

U r not checking for outliers

[@sarveshpande5478]

You have not used train and test models

[@DataThinkers]

This project focused on data analysis using pandas.

[@apurvavarude7945]

Can I get source code

[@DataThinkers]

Sure, from here:github.com/DataThinkers/Data-Analytics-Projects-Code

[@Arabduur]

U deservs more subscripe, i subscriped, shares, and liked the video, and i addiction if i get some packets, i will denote for u free work,

[@abdikarimgedi7442]

Can you do the: tn.movie_budget.csv dataset