Binary Detection LSTM Task via Colab

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.