ml day 2

Balakumaravel K S 24MCR009 — Fri, 25 Apr 2025 16:55:28 +0000

FROM python:3.12-slim

WORKDIR /app

RUN cd /app

COPY . .

RUN pip install pandas scikit-learn matplotlib

CMD ["python", "ml-model.py"]

Build the Docker image using the following command in the terminal:

docker build -t balakumaravel/24mcr009-hello_world:latest .

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")

Day 1 ML

Balakumaravel K S 24MCR009 — Wed, 23 Apr 2025 17:06:05 +0000

by-Step Git Commands Execution:
Initialize a Git repository:
git init
Creates a new Git repository in the folder 24MCR009.

Add a file to staging area:
git add 24MCR009.txt
Adds 24MCR009.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 24MCR009.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/bala-2210/24MCR009.git
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 "balakumaravel2210@gmail.com"
git config --global user.name "bala-2210"
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.

Step-

Then , Again We will Modify or add new file means we need to follow the same steps, like

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

DEV Community: Balakumaravel K S 24MCR009