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y = df['Price'] here the single square bracket symbolizes the data in 'array format' whereas in y = df[['Price]] the double square bracket symbolizes data in 'data frame format'. And traditionally the target column/feature needs to be in array format and not in data frame. (That's a good practice)

YOLOv8 (You Only Look Once Version 8) - For object detection. XML tree - For Extracting object information from xml file OpenCV - For image processing. TensorFlow - For model handling and integration

You're waiting for what??

I'll soon make a video on that.

@@InsightsByRish thanx.. pls make early if possible. Thanx

Yes, this playlist is ideal for learning various time series implementations, assuming you have a basic understanding of key time series terms.

So according to you, how should the missing values from target col be handled?

@@InsightsByRish in this case, we can consider dropping those rows

@@hoangha6680 It can only be done when you have a large volume of data. If you're dropping rows from a dataset that's already small, you'll lose a significant portion of data, which can eventually hamper your model's performance.

Hey, there are many novel ideas that complement sentiment analysis, such as emotion detection from chats, sarcasm detection, and analyzing political inclinations from tweets. I’d also recommend checking out my 'Building Multi-Task NLP Model' video, where I analyzed various aspects of sentiment using a single model. Hope that helps!

@@InsightsByRish yes.. but there is lots of work done in finding emotions from text,audio etc. sarcasm is also done still not finding any certain things to go. In case of model improvements what we can do? Pls suggest

@@InsightsByRish can u pls elovrate sentiment,emotion are different in their granularity. But is opinion polarity subjectivity and intensity what is the difference between them

@@nishah4058 For Model Improvements, you can perform Data Augmentation where you generate more training data by paraphrasing existing samples or try to perform back-translation to improve the diversity of dataset or try to experiment with different word embeddings and even the model's architecture.

@@nishah4058 Sentiment basically refers to the attitude (broader perspective) expressed in a text. Like positive, negative or neutral. But Emotion focusses on underlying expressions (narrower or granule perspective) from a text. Like happy, sad, angry, confused etc. They capture the specific feeling. 1. Opinion Polarity basically means same as that of sentiment. That whether the expressed opinion is positive, negative or neutral. 2. Subjectivity is a factual opinion that is true for everyone. Like 'I like dessert' may not be true for everyone as some people may not like desserts. But 'Desserts are sweet' is true for everyone. So that's a subjective opinion. 3. Intensity refers to the degree or strength or depth of emotion/opinion. Like the statement 'I love chocolates very much' has higher intensity than the statement 'I like chocolate'.

Hi! I'd love to help with your research, but due to my hectic schedule, I may not always be available for consistent support. However, I'll do my best to assist whenever possible. Just to clarify, I don't provide any classes.

@@InsightsByRish oh… so sad to hear bcz there is no one in research which guide you through the process. Anyway can u pls suggest me some deeper topic in sentiments analysis from text? Like normal sentiments emotions are outdated.. like what can we do in sentiment for textual data.. I can’t get it where to move next.. till I did basic selection technique and voting etc but not got stuck what can I do further here . Thanx in advance.

​@@nishah4058 You can perform 'implicit sentiment detection` where the goal is to detect sentiments that are not expressed explicitly through common words or statements but are implied through context, tone, or specific phrasing. For eg in the statement 'I guess the restaurant service was fine' traditional model will consider this as a positive or neutral statement but rather it has an underlying unsatisfied or slightly disappointed tone. So the goal is to detect this underlying emotion.

I downloaded the dataset from GeeksforGeeks : media.geeksforgeeks.org/wp-content/uploads/20240905183434/HousePricePrediction.xlsx

Thanks, will make soon!

Hi Arjun, It sounds like the issue might be related to the input shape not being correctly set for the embedding layer, which can sometimes cause the output shape to show as "?" and the parameters to be zero. Make sure the sentence_length variable (used in pad_sequences) matches the input length specified in the embedding layer.

@@InsightsByRish i tried to ensure those but still it didnt worked for me ..

Thank you for sharing! With proper fine-tuning, Random Forest can also reach a similar level of accuracy

Thank you so much! Glad you found it helpful!

Mine also your problem Solved

I can't see your code, so it's hard to pinpoint the exact issue, however I would suggest you to crosscheck all you paths.

drive.google.com/file/d/1SQylAN1a14NBC1WU6pJlFWeisDoomEd3/view?usp=sharing

Thanks for the quick needful, i'm trying to learn Deep Learning

It looks like you're using Keras 3, which is causing the error.Try downgrading to Keras 2 version. It might help.

Yes you can mail me on insightswithrish@gmail.com

Yes

drive.google.com/drive/folders/1qHmfr5iH7jFwW4aeTQx6WGIs5sZvLNxo?usp=sharing

mam it is not showing download option

@@Cricexplain07 Do one thing, send me your email id. I'll attach and send the zip folders to you.

Yes, very soon!

Hey, I made this project for complete beginners, so I didn't cover the image annotation part. But, I'll soon make a video explaining how to annotate images and create your own custom dataset for object detection. Stay tuned!

@@InsightsByRish Thank You We are working on this it's our final Year project

The thing that you're saying will only happen if we are making predictions on new data or for further intervals of time. But here we are making predictions for existing data that was already present in X_test. So the red line (predicted values) will remain on top of blue line (actual values) as the predicted values are getting aligned with the existing targets present in y_test.

Thanks for appreciation!

Thanks for appreciation!

Ok, will make.

@@InsightsByRish I AM WAITING FOR 100 DAYS DEEP LEARNING PLAYLIST

Hello! When I was learning deep learning, I referred to many research papers and articles. They provide a great understanding of the concepts.

Ok, will make soon!

I already have a deep learning playlist that covers multiple projects. You can check it out here ruclips.net/p/PLIIcfxDiNOqDO8JDUKQ0wnyAcLc9C0hTA

Thanks for the appreciation! Yes, it is possible to implement this using the Databricks DataFrame method.

Hi @Anthony-o1b2j, thank you for your kind words! I'm glad you found the video helpful. To answer your question: Yes, when using an LSTM for multivariate time series forecasting, the model considers all features together. It doesn’t predict each feature independently. Instead, it takes into account the relationships and interdependencies between all the features to make predictions.

Glad you liked it!

Glad to hear that!

@@InsightsByRish but may I ask something? I used another data sample and when I tried the prediction result obtained was a straight line at point 0, far from the previous data. May I know why this could happen? thank you

Is there a minimum amount of data required to perform this forecast? or something? sorry for asking, im so clueless

@@indahwira1155 That straight line prediction mostly arises when : 1. Either your data is not stationary 2. Parametric Values of p, d, q, are selected inappropriately 3. Or the dataset has lot of missing/null values 4. Dataset is too small Please ensure all this factors and let me know.

@@indahwira1155 Forecasting doesn't depend upon any such factor but the only thing you should consider is that the dataset shouldn't be too small. It should be large enough to learn the patterns from.

Glad you liked it!

Glad to hear that!

Thanks for appreciation!

Yes sure, I'll keep that in mind! I've actually included that in my 'Text Summarisation' video.

Okay, Will make soon!

Yes, I'll surely make more on that topic soon. (Btw I already have one on my channel using SARIMAX).

Welcome, will make 👍🏻

You're welcome! I'm glad you found it helpful. Your kind words mean a lot.

Model architecture is it type of transformer or neural networks ?

@@MuhammadEhtisham-cm5vr BERT is a 'Transformer model'.