I would like to conduct a binary detection task using a Long Short-Term Memory (LSTM) model implemented in Google Colab. I have the dataset The workflow should include the following steps: 1. Data Preprocessing – Perform necessary preprocessing on the dataset before model training. 2. Feature Selection – Apply Chi-square feature selection to identify the most relevant features. 3. Handling Class Imbalance – Use SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset. 4. Data Splitting – Split the dataset into: • 70% training set • 15% validation set • 15% test set 5. Model Development – Train a binary classification LSTM model in Google Colab. The model performance should be evaluated using the following metrics: • Accuracy • Precision • Recall • F1-score • AUC-ROC Additionally, generate: • A Confusion Matrix to visualize classification performance. Finally, prepare a technical report documenting: • Data preprocessing steps • Feature selection method • Model architecture • Training process • Evaluation results • Interpretation of the confusion matrix and performance metrics.