📌 What I Did:
- Loaded and explored the Iris.csv dataset.
- Plotted histograms and density plots for visual exploration of feature distributions.
- Split the data into training and testing sets.
- Trained a Logistic Regression model to classify iris species based on flower measurements.
- Evaluated the model’s accuracy on unseen data.
- Saved the trained model as logistic_model.pkl for reuse or deployment.
🧠 ML Libraries Used & How I Installed Them:
✅ Pandas
pip install pandas
For easy data manipulation and analysis.
✅ Scikit-learn
pip install scikit-learn
The ML powerhouse! Used for splitting data, training the model, and evaluation.
✅ Matplotlib
pip install matplotlib
For visualizing data with plots and charts.
Snippet :
Output :
Histogram :
Density_Plots :
GitHub Repository :
✅ First Push Attempt (Everything up-to-date):
git push origin feature-24MCR115
- Everything up-to-date
- This means you had already pushed the feature-24MCR115 branch earlier, and no new commits were made since then.
✅ Staging and Committing New Files :
git status
git add .
git add .gitignore hello_world_ml.py logistic_model.pkl
git commit -m "[feature-24MCR115 b890daa] Added ML model, script, and gitignore"
- You staged all files using git add ..
- You explicitly staged .gitignore, hello_world_ml.py, and logistic_model.pkl again (not harmful, just redundant since they were already added with git add .).
The commit was successfully created and shows the files added:
.gitignore
hello_world_ml.py
logistic_model.pkl
