DEV Community: Gokila Narasimman

CREATE AN DOCKER IMAGE FOR ML PROJECT

Gokila Narasimman — Mon, 28 Apr 2025 16:51:01 +0000

Step1:

Create an Folder Structure and download the DataSet from Iris dataset from Kaggle.

Step2:

Create an Dockerfile for creating an docker image.

We can get the dependencies "FROM PYTHON:3.9" , it is used to get the dependencies.

We can run the required tools for running of the projects in this we have use pandas,matplotlib, scikit-learn for docker image to be run.

"RUN PIP INSTALL PANDAS MATPLOTLIB SCIKIT-LEARN"

We have to use Command "COPY . .", we have used it for source and destination.

We have used to start container by docker image command.

"CMD[python,"hello.py"]"

Step 3:

Create an docker image using
"docker build -t username/filename:latest ."

PREDICTION OF IRIS

Gokila Narasimman — Fri, 25 Apr 2025 17:56:02 +0000

Installed Key Python Libraries for Machine Learning

Checking the version of the pip installed:

PANDAS
Importing pandas for the machine learning code

SKICIT-LEARN

Importing skicit learn

MATPLOTLIB
Importing matplotlib

ALGORITHM
from pandas import read_csv
from matplotlib import pyplot
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import joblib

Step 1: Load dataset

filename = "Iris.csv"
data = read_csv(filename)

Step 2: Display data shape and preview

print("Shape of the dataset:", data.shape)
print("First 20 rows:\n", data.head(20))

Step 3: Plot and save histograms silently

data.hist()
pyplot.savefig("histograms.png")
pyplot.close() # Close the plot so it doesn't show up in prompt

Step 4: Plot and save density plots silently

data.plot(kind='density', subplots=True, layout=(3,3), sharex=False)
pyplot.savefig("density_plots.png")
pyplot.close()

Step 5: Convert to NumPy array and extract features/labels

array = data.values
X = array[:, 1:5] # Features: Sepal/Petal measurements
Y = array[:, 5] # Target: Species

Step 6: Split data into training (67%) and testing (33%)

test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)

Step 7: Create and train logistic regression model

model = LogisticRegression(max_iter=200)
model.fit(X_train, Y_train)

Step 8: Evaluate and display accuracy

result = model.score(X_test, Y_test)
print("Accuracy: {:.2f}%".format(result * 100))

Step 9: Save the trained model to a file

joblib.dump(model, "logistic_model.pkl")

IRIS DATASET

OUTPUT

DENSITY_PLOTS

HISTOGRAM

BRANCH CREATION IN GITHUB TO STORE THE CODE:

Dockerize a Python ML Model and push in GitHub(With Steps + ScreenShots)

Gokila Narasimman — Fri, 25 Apr 2025 17:36:02 +0000

1.Checkout to main
Command:
git checkout main

2.Create a new branch (e.g., feature branch)
Command:
git checkout main -b features-24mcr027

3.(Optional) Add a branch through GitHub UI

Go to GitHub → Your Repo → Branch Dropdown → Type branch name → Create branch

4.Check current branches

Command:
git branch

5.Check status of changes

Command:
git status

6.Add all changes

Command:
git add .

7.Commit your changes
Command:
git commit -m "Added hello.py"

8.Push your branch to GitHub

Command:
git push origin features-24mcr027

9.Create a Pull Request on GitHub

Go to GitHub → Pull Requests → New Pull Request → Select branch → Create Pull Request → Merge Pull Request → Confirm Merge

DOCKER COMMANDS

1.Check Docker version

Command:
docker --version

2.Create a Dockerfile in your project

3.Build Docker image

Command:
docker build -t gokila07/mca-24mcr042-ml:latest .

4.List Docker images

Command:
docker images

5.Push Docker image to DockerHub

Command:
docker push gokila07/mca-24mcr027

6.Open Docker Desktop to view images and containers

7.Run the Docker container

8.Verify the Docker image and running container in Docker Desktop (GUI)

PUSH DOCKER FILE INTO GITHUB

Main branch
First Branch: feature-24mcr027

Second Branch: dockerfile

DAY 1 ML

Gokila Narasimman — Wed, 23 Apr 2025 16:40:14 +0000

KONGU ENGINEERING COLLEGE
MASTER OF COMPUTER APPLICATIONS

DAY 1
Step-by-Step Git Commands Execution:

Initialize a Git repository:
git init
Creates a new Git repository in the folder 24MCR027.

Add a file to staging area:
git add 24MCR027.txt
Adds 24MCR027.txt to the staging area.

Commit the file:
git commit -m "Added Personal Details"
Creates a commit with the message "Added Personal Details".

Check Git status:
git status
Shows that 24MCR027.txt has been modified but not staged.

View commit log:
git log
Displays the commit history (one commit at this point).

Add remote GitHub repository:
git remote add origin-https://github.com/gokila0/24MCR027
Links the local repository to a remote GitHub repo.Check current branch:
git branch
Shows the current branch is master.

Rename branch from master to main:
git branch -M main
Renames the current branch to main.

Set Git global config for email and username:
git config --global user.email " gokigokin2@@gmail.com "
git config --global user.name "gokila0"
Sets your global Git identity.

Push code to remote repo for the first time:
git push -u origin main
Pushes the main branch to GitHub and sets upstream tracking.

Then , Again if we modify or add new file means we need to follow the same steps mentioned below.

git add .
git commit -m “message”
git push origin main