Regression-Project

Table of Contents

1. Description
2. Installation
3. Usage
4. Project Structure
5. Contributing
6. License
7. Workflows

Description

Regression is a fundamental supervised machine learning technique used to predict continuous numerical outcomes based on input features. This project focuses on applying regression to [The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.] using a structured dataset. The analysis is designed to provide insights into the relationships between input features and the target variable while also delivering an accurate predictive model.

Key Concepts Covered

1.Exploratory Data Analysis (EDA) Understand the data through visualization, summary statistics, and correlation analysis.

2.Data Preprocessing

• Handle missing values and outliers.
• Transform and encode categorical variables.
• Standardize or normalize numerical features.

3.Modeling and Evaluation

• Experiment with different regression techniques (Linear Regression, Decision Trees, Gradient Boosting, etc.).
• Use metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) to evaluate model performance.

4.Feature Importance and Interpretability

• Understand which features influence predictions the most.
• Visualize model predictions versus actual values.

Project Goals

1.Build a robust regression model to predict [target variable, e.g., house prices].
2.Explore and visualize patterns in the data.
3.Highlight practical insights for stakeholders, such as the most influential factors affecting the target variable.
4.This project provides three distinct workflows to enable deployment and environment setup across various platforms. Each is designed for specific use cases

Technologies Used

• Python for data processing and modeling.
• Pandas and NumPy for data manipulation.
• Matplotlib and Seaborn for visualizations.
• Scikit-learn for machine learning algorithms and metrics.
• EC2 AWS
• Streamlit Cloud
• Ngrok

Installation

1. Clone this repository:

   git clone https://github.com/oscar/Regression-Project.git
   

Usage

jupyter notebook notebooks/exploratory_analysis.ipynb

Project Structure

Regression-Project/
│
├── data/
│ ├── raw/
│ ├── processed/
│
├── notebooks/
│ ├── exploratory_analysis.ipynb
│ ├── regression_model.ipynb
│
├── scripts/
│ ├── data_preprocessing.py
│ ├── model_training.py
│ ├── evaluation.py
│
├── visuals/
│
├── README.md
├── requirements.txt
├── LICENSE
├── .gitignore
│ ├── workflows
│ ├── workflows_aws
│ ├── workflows_ngrok

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Workflows

Workflows: Automating Deployment and Setup
This project provides three distinct workflows to enable deployment and environment setup across various platforms. Each is designed for specific use cases, as detailed below.

1. Workflows/streamlit.yml: Deploying to Streamlit Cloud
   This workflow facilitates automatic deployment of the application to Streamlit Cloud, a free hosting service specifically designed for Streamlit-based applications.

   Key Features:

   • Core File: .github/workflows/streamlit.yml.
   • Automatically installs dependencies from requirements.txt.
   • Configures environment variables and prepares necessary files, such as kaggle.json, for data access.
   • Ideal for simple and rapid deployments of Streamlit applications.

   Benefits:

   • Free hosting with public access.
   • Streamlined management of dependencies and environment setup.

2. workflows_ngrok/streamlit_ngrok_solution: Running with Ngrok This workflow is intended to expose the local Streamlit application to the internet using Ngrok, a tool for creating secure HTTPS tunnels to localhost.

   Key Features:

   • Core File: workflows_ngrok/streamlit_ngrok_solution.
   • Configures Ngrok to create a tunnel and expose the application.
   • Currently Non-Functional due to recent changes in Ngrok's tunneling and endpoint APIs.

   Current Limitations:

   • Ngrok updates have broken compatibility with the current implementation.
   • Ongoing efforts aim to adapt this workflow to the latest Ngrok standards. Future Use:
   • This workflow has potential for temporary public exposure of local applications without a dedicated hosting platform.

3. workflows_aws/ec2_AWS.txt: Deploying to AWS EC2 This workflow outlines the steps for deploying the application on an AWS EC2 instance. It is suited for scalable and customizable environments.

   Key Steps:

   • Launch an EC2 Instance: Use the AWS Management Console or AWS CLI to create an instance.

   • User Data File: Copy the commands in workflows_aws/ec2_AWS.txt and paste them into the "User Data" field during the instance setup. These commands:

     • Install Python, pip, and Streamlit.
     • Install dependencies from requirements.txt.
     • Launch the application on the configured port.

   • Benefits:

     • Full control over the runtime environment.
     • Scalability to handle varying levels of traffic and usage.

Summary

Each workflow serves a distinct purpose:

• Streamlit Cloud: For quick and easy deployment.
• Ngrok: For temporary public testing (under development).
• AWS EC2: For robust and scalable deployments.

Follow the instructions in each workflow file to implement the desired deployment strategy.