Classification & Time Series Analysis of Spotify Data using Grid Searched Random Forest & LSTM.

This project is based on Deep learning ......

Combines CNN trained on data from OpenCV and Mediapipes and a LSTM trained on ultrasonic sensor data to allow for combined interactions to control Spotify API

Perform sentiment analysis based on reviews from the play store for the spotify application using several scenarios such as TF-IDF with SVM, Word2Vec with LSTM and GRU.

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Time series analysis and forecasting of Spotify streaming data (2015–2025) using LSTM neural networks. Includes global, genre, and artist-level insights with visualizations and business recommendations.

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LSTM-based song lyric generator trained on Spotify and poetry data.

Spotify kullanıcı yorumlarını pozitif veya negatif olarak sınıflandıran LSTM tabanlı duygu analizi modeli. Keras, tokenizasyon ve etiket kodlama kullanır.

a simple PyTorch project that predicts the popularity score of songs using Spotify audio features and an LSTM regression model.

Deep learning–based music recommendation and classification system — includes emotion-based song recommender, Spotify emotion classifier, and genre classifier using ResNet, CNN, and LSTM models

Emotion-based music recommender that detects facial emotions using Vision Transformer and Bi-LSTM ensemble models and recommends songs from Spotify playlists using cosine similarity.

Using historical data from the billboard top 100 and the Spotify API, conduct time series analysis,  to forecast future aggregated trend feature values for tending songs, using LSTM architecture. 

The Enhanced LSTM-Based Music Recommendation System combines genre and audio data from Spotify to predict popularity and offer personalized song suggestions. Utilizing LSTM neural networks, it enhances music streaming with real-time updates and responsive design, marking a significant leap in music analytics and recommendation technology.

Leveraging LLMs (GPT-4, BERT) and ML models (Facebook Prophet, LSTM) to analyze Spotify data for music trend predictions. Utilizing Spotipy, Pandas, VADER for sentiment analysis and Streamlit, Plotly for interactive visualizations.

A customer support chatbot built with Seq2Seq, LSTM/GRU RNNs, and attention mechanisms. Trained on Twitter data from top brands, providing interactive, real-time assistance for Apple, Amazon, Uber, Delta, and Spotify. 🤖💬

A collection of NLP projects featuring Spotify App Review Sentiment Analysis and Depression Detection from social media posts. Implements Text Preprocessing, TF-IDF, and models ranging from Naive Bayes to LSTM/BiLSTM and BERT.

End-to-end NLP pipeline for sentiment analysis on Spotify app reviews. Combines classical ML (LogReg, SVM, RF) with deep learning & transformer models (LSTM, BERT, RoBERTa) for sentiment classification, opinion mining & aspect-based sentiment analysis, with visualizations & insights.

This project detects a user’s emotion from face and voice using deep learning (CNN for facial expressions & Bi-LSTM for voice). The results are fused to predict the final emotion, and the system recommends music genres (e.g., happy → pop, sad → blues, angry → rock) by opening Spotify suggestions.