NOTEPAD-STEPS.txt

project on truck delay classification is a comprehensive end-to-end machine learning pipeline with a focus on logistics. Here's an analysis of the project based on the provided details:

Project Overview
Objective:

Improve Operational Efficiency: Allocate resources more effectively.
Enhance Customer Satisfaction: Provide more reliable delivery schedules.
Optimize Route Planning: Reduce delays caused by traffic or adverse weather conditions.
Reduce Costs: Minimize penalties or compensation for delayed shipments.
Approach

Start with Data Cleaning: Handle any missing values, remove duplicates if present, and correct inconsistencies.


Data Retrieval from Hopsworks:

Connect to Hopsworks using Python.

Retrieve relevant data directly from the feature store.
Data Processing:

Train-Validation-Test Split: Split the dataset into training, validation, and test sets.
One-Hot Encoding: Convert categorical variables into numerical format.
Scaling Numerical Features: Normalize numerical features to improve model performance.
Model Building:

Logistic Regression
Random Forest
XGBoost
Hyperparameter Tuning:

Explore hyperparameter tuning with grid search and random search to optimize model performance.
Streamlit Application Development:

Develop a Streamlit application to interact with the model and visualize results.
Deployment:

Deploy the Streamlit application on an AWS EC2 instance.
Folder Structure
Data:

Contains CSV files with data related to city weather, drivers, routes, traffic, trucks, and schedules.
Backup SQL files for MySQL and PostgreSQL.
A PDF for data description.
Deployment:

Contains the Streamlit application files (app.py), configuration (app_config.yaml), requirements (requirements.txt), and data encoder/ scaler files.
Notebooks:

Jupyter notebooks for the different stages of the machine learning pipeline.
References:

Contains a readme file and a solution methodology document.
Execution Instructions
Python Version: 3.10
Virtual Environment: Instructions for setting up a virtual environment and installing dependencies.
Streamlit Application Deployment on AWS EC2
Launching EC2 Instance:

Use Ubuntu 22.04 LTS and instance type t2.medium.
Open port 8501 for Streamlit access.
Download and secure a PEM key for SSH access.
Accessing EC2 Instance:

SSH into the instance and gain superuser access.
Update packages and verify Python installation.
Install Python packages using apt.
Transferring Files to EC2:

Use SCP to transfer application code to the instance.
Setting Up and Running Streamlit:

Install dependencies from requirements.txt.
Run the Streamlit application using streamlit run app.py.
Use nohup for permanent running of the application.
Next Steps
Share CSV Files:

Share the CSV files you have, and we can start with the data retrieval and preprocessing steps.
Initial Data Analysis:

Perform exploratory data analysis (EDA) to understand the dataset, identify missing values, and determine feature importance.
Feature Engineering:

Create feature groups with Hopsworks and process features for model building.
Model Development:

Build and evaluate models with different algorithms, tune hyperparameters, and select the best-performing model.
Streamlit Application:

Develop the application to interact with the model and visualize predictions.