+
+## 🎯 AIM
+To detect anomalies in time-series data using Long Short-Term Memory (LSTM) networks.
+
+## 📊 DATASET LINK
+[NOT USED]
+
+## 📓 KAGGLE NOTEBOOK
+[https://www.kaggle.com/code/thatarguy/lstm-anamoly-detection/notebook](https://www.kaggle.com/code/thatarguy/lstm-anamoly-detection/notebook)
+
+??? Abstract "Kaggle Notebook"
+
+
+
+
+## ⚙️ TECH STACK
+
+| **Category** | **Technologies** |
+|--------------------------|---------------------------------------------|
+| **Languages** | Python |
+| **Libraries/Frameworks** | TensorFlow, Keras, scikit-learn, numpy, pandas, matplotlib |
+| **Tools** | Jupyter Notebook, VS Code |
+
+---
+
+## 📝 DESCRIPTION
+
+!!! info "What is the requirement of the project?"
+ - The project focuses on identifying anomalies in time-series data using an LSTM autoencoder. The model learns normal patterns and detects deviations indicating anomalies.
+
+??? info "Why is it necessary?"
+ - Anomaly detection is crucial in various domains such as finance, healthcare, and cybersecurity, where detecting unexpected behavior can prevent failures, fraud, or security breaches.
+
+??? info "How is it beneficial and used?"
+ - Businesses can use it to detect irregularities in stock market trends.
+ - It can help monitor industrial equipment to identify faults before failures occur.
+ - It can be applied in fraud detection for financial transactions.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+ - Understanding time-series anomaly detection methodologies.
+ - Generating synthetic data to simulate real-world scenarios.
+ - Implementing an LSTM autoencoder to learn normal patterns and detect anomalies.
+ - Evaluating model performance using Mean Squared Error (MSE).
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+ - Research paper: "Deep Learning for Time-Series Anomaly Detection"
+ - Public notebook: LSTM Autoencoder for Anomaly Detection
+
+---
+
+## 🔍 PROJECT EXPLANATION
+
+### 🧩 DATASET OVERVIEW & FEATURE DETAILS
+
+??? example "📂 Synthetic dataset"
+
+ - The dataset consists of a sine wave with added noise.
+
+ | Feature Name | Description | Datatype |
+ |--------------|-------------|:------------:|
+ | time | Timestamp | int64 |
+ | value | Sine wave value with noise | float64 |
+
+---
+
+### 🛤 PROJECT WORKFLOW
+
+!!! success "Project workflow"
+
+ ``` mermaid
+ graph LR
+ A[Start] --> B{Generate Data};
+ B --> C[Normalize Data];
+ C --> D[Create Sequences];
+ D --> E[Train LSTM Autoencoder];
+ E --> F[Compute Reconstruction Error];
+ F --> G[Identify Anomalies];
+ ```
+
+=== "Step 1"
+ - Generate synthetic data (sine wave with noise)
+ - Normalize data using MinMaxScaler
+ - Split data into training and validation sets
+
+=== "Step 2"
+ - Create sequential data using a rolling window approach
+ - Reshape data for LSTM compatibility
+
+=== "Step 3"
+ - Implement LSTM autoencoder for anomaly detection
+ - Optimize model using Adam optimizer
+
+=== "Step 4"
+ - Compute reconstruction error for anomaly detection
+ - Identify threshold for anomalies using percentile-based method
+
+=== "Step 5"
+ - Visualize detected anomalies using Matplotlib
+
+---
+
+### 🖥 CODE EXPLANATION
+
+=== "LSTM Autoencoder"
+ - The model consists of an encoder, bottleneck, and decoder.
+ - It learns normal time-series behavior and reconstructs it.
+ - Deviations from normal patterns are considered anomalies.
+
+---
+
+### ⚖️ PROJECT TRADE-OFFS AND SOLUTIONS
+
+=== "Reconstruction Error Threshold Selection"
+ - Setting a high threshold may miss subtle anomalies, while a low threshold might increase false positives.
+ - **Solution**: Use the 95th percentile of reconstruction errors as the threshold to balance false positives and false negatives.
+
+---
+
+## 🖼 SCREENSHOTS
+
+!!! tip "Visualizations and EDA of different features"
+
+ === "Synthetic Data Plot"
+ 
+
+
+??? example "Model performance graphs"
+
+ === "Reconstruction Error Plot"
+ 
+---
+
+## 📉 MODELS USED AND THEIR EVALUATION METRICS
+
+| Model | Reconstruction Error (MSE) |
+|------------------|---------------------------|
+| LSTM Autoencoder | 0.015 |
+
+---
+
+## ✅ CONCLUSION
+
+### 🔑 KEY LEARNINGS
+
+!!! tip "Insights gained from the data"
+ - Time-series anomalies often appear as sudden deviations from normal patterns.
+
+??? tip "Improvements in understanding machine learning concepts"
+ - Learned about LSTM autoencoders and their ability to reconstruct normal sequences.
+
+??? tip "Challenges faced and how they were overcome"
+ - Handling high reconstruction errors by tuning model hyperparameters.
+ - Selecting an appropriate anomaly threshold using statistical methods.
+
+---
+
+### 🌍 USE CASES
+
+=== "Financial Fraud Detection"
+ - Detect irregular transaction patterns using anomaly detection.
+
+=== "Predictive Maintenance"
+ - Identify equipment failures in industrial settings before they occur.
+
+
+
diff --git a/docs/projects/deep-learning/index.md b/docs/projects/deep-learning/index.md
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@@ -11,6 +11,15 @@
+