HEARTBEAT Detection: LSTM Autoencoder, Isolation Forest & Time Series Analysis // Hands-on Tutorial

This is the 3rd video in a series on End to End Data Science Projects with Machine Learning and Deep Learning. Transform your Data Science skills using this End to End Data Science Project. I used Kaggle dataset ECG heartbeat data for Time Series Analysis & Time Series Anomaly Detection. By leveraging Tensorflow Keras 1. Autoencoders, 2. LSTM Autoencoder and 3. LSTM Autoencoder with Convolutional Neural Network ( CNN ) feature extraction in Keras and comparing it with 4. Isolation Forest in ScikitLearn, this project showcases advanced techniques in Machine learning and Deep learning for anomaly detection in Python. Perfect for your data science portfolio, this deep learning autoencoder project also explores Area Under Precision and Recall metrics for evaluating model performance.

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📚 Link to Python Code ➡︎
https://colab.research.google.com/dri...

📚 Link to Kaggle HeartBeat ECG Data ➡︎
https://www.kaggle.com/datasets/shaya...
📺 Data Science Projects ➡︎    • Data Science Projects End to End  





















⏰ Timecodes ⏰

0:00 Introduction
0:49 Description of Kaggle Data electrocardiogram (ECG) of Heartbeats for Time Series Analysis Python
1:16 Python Libraries Keras Tensorflow, StatsModel, ScikitLearn, Pandas, NumPy, Matplotlib, Seaborn
2:15 Exploratory Data Analysis
3:04 Time Series Analysis using Statsmodel Python Library using Visualization
4:04 Time Series Analysis using Decomposition (Trend, time series Seasonality and Residuals)
5:21 Rolling Statistics & how to check Data stationary or not
6:42 Augmented Dickey-Fuller test
7:17 Autocorrelation Function (ACF) & Partial Autocorrelation Function (PACF)
8:28 Anomaly Detection Isolation Forest | Isolation Forest Implementation (incl. sklearn minmaxscaler & forward fill NA) & Results Analysis using Confusion matrix in machine learning
12:06 Deep Learning Autoencoder Anomaly Detection (Keras Autoencoder), incl. encoder and decoder deep learning with tensorflow keras, building tensorflow keras sequential model and sigmoid function, calculating the reconstruction error
14:48 Long Short Term Memory LSTM Autoencoder (comparable to LLMs) relu activation function with adam optimizer and mean square error loss function. Using area under precision and recall we compare
the true positive and true negative and used classification threshold
19:40 Get My Free Guide: 10X Your AI Solutions for Real-World with 10 Steps Data Science Road Map + 100 Python Libraries for Impactful Machine Learning and Deep Learning
20:30 LSTM Autoencoder anomaly detection Keras plus Convolutional Neural Network ( CNN ) Feature Extractor using Convolutional Neural Network and comparing deep learning models using auc pr due imbalanced data
22:00 Max pooling Convolutional Neural Network
23:54 Comparison of Isolation Forest, AutoEncoder, LSTM Autoencoder and LSTM AutoEncoder + Convolutional Neural Network Feature Extraction
24:47 Huperparameter Tuning (Comment if you want a follow up)
24:58 Outro

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#timeseries #lstm #anomalydetection #datascienceprojects
#ai #python #datascience #datasciencetutorial #convolutionalneuralnetwork #tensorflow #deeplearning #keras