END to End ML Project Energy Efficiency on Residual Buildings - Vishnu M

Problem Statement:

• Predict and understand the impact of building design features on both heating and cooling energy needs in residential buildings using both traditional and advanced machine learning approaches.

There are 8 Independent Variables.

• relative compactness: continuous.
• surface area: continuous.
• wall area: continuous.
• roof area: continuous.
• overall height: continuous.
• orientation: continuous.
• glazing area: continuous.
• glazing area distribution: continuous.

Target Varibale: Average Heating and Colling Load

Dataset Source Link : https://archive.ics.uci.edu/dataset/242/energy+efficiency

Approach of the project

EDA,FE and Model Traing Approach Notebook:

• In EDA, I have created an deep and clean realtions between every column and made some plots using seaborn and matplotlib.
• In FE, I have handled missing values,created a all categorical to numerical columns.
• I have trained the model using classification algorithms, the best model found is Logistic Regresssion with 85% accuracy.

Link: Model training Notebook

Modular Code steps:

1. Data Ingestion :

   • In Data Ingestion phase the data is first read as csv.
   • Then the data is split into training and testing and saved as csv file.

2. Data Transformation :

   • In this phase a ColumnTransformer Pipeline is created.
   • for Numeric Variables first SimpleImputer is applied with strategy median ,then Standard Scaling is performed on numeric data.
   • for Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler.
   • This preprocessor is saved as pickle file.

3. Model Training :

   • In this phase base model is tested . The best model found was Logistic Regression.
   • This model is saved as pickle file.

4. Prediction Pipeline :

   • This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python.

5. Flask App creation :

   • Flask app is created with User Interface to predict the energy efficiency on residual buildings inside a Web Application.

Deployment:

AWS Elastic Beanstalk link:

AWS ELastic Beanstalk link: (https://yxdzqmvtk6.us-west-2.awsapprunner.com/predictdata)

Note : Currently the Environment and code pipeline is terminated.

AWS CI/CD:

Docker image + Github action + AWS EC2 + ECR Repo

• steps:

1. Docker Build checked
2. Github Workflow
3. Iam User In AWS

• Docker Setup In EC2 commands to be Executed

  #optinal

  sudo apt-get update -y

  sudo apt-get upgrade

  #required

  curl -fsSL https://get.docker.com -o get-docker.sh

  sudo sh get-docker.sh

  sudo usermod -aG docker ubuntu

  newgrp docker

• Configure EC2 as self-hosted runner:

• Setup github secrets: AWS_ACCESS_KEY_ID=

  AWS_SECRET_ACCESS_KEY=

  AWS_REGION = us-east-1

  AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com

  ECR_REPOSITORY_NAME = simple-app