Machine Learning Lab Practicals

This repository contains Machine Learning laboratory practical assignments implemented in Python using Google Colab.

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Repository Structure

.
├── Assignment_01_LinearRegression/
│   ├── Assignment_01.ipynb
│   ├── data/
│   │   └── dataset.csv          # Medical Cost Personal Datasets (Insurance)
│   └── outputs/                 # Plots & correlation heatmap
│
├── Assignment_02_LogisticRegression/
│   ├── Assignment_02.ipynb
│   ├── data/
│   │   ├── binary_dataset.csv     # Diabetes Prediction Dataset
│   │   └── multiclass_dataset.csv # Mobile Price Classification Dataset
│   └── outputs/                 # Confusion matrices, ROC curves & boxplots
│
├── generate_notebook.py
├── generate_assignment_02.py
└── README.md

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Assignment Overview

Assignment 01: Linear Regression

• Objective: Predict continuous medical insurance costs (charges) based on demographic and lifestyle attributes (age, bmi, smoker, etc.).
• Dataset: Medical Cost Personal Datasets (Kaggle)
• Metrics: MAE, MSE, RMSE, $R^2$ Score.
• Visualizations: Feature Correlation Heatmap, Age vs. Charges Scatter Plot, Actual vs. Predicted Plot, Residual Distribution.

Assignment 02: Logistic Regression (Binary & Multinomial)

• Part A (Binary Logistic Regression):
  • Objective: Predict patient diabetes status (0 vs 1).
  • Dataset: Diabetes Prediction Dataset (Kaggle)
  • Metrics: Accuracy, Precision, Recall, F1-Score, Classification Report, Confusion Matrix, ROC-AUC Curve.
• Part B (Multinomial Logistic Regression):
  • Objective: Classify mobile phones into 4 distinct price ranges (0: Low, 1: Medium, 2: High, 3: Very High).
  • Dataset: Mobile Price Classification (Kaggle)
  • Metrics: Accuracy, Weighted Precision, Recall, F1-Score, Classification Report, Multiclass Confusion Matrix.
• Part C: Binary vs. Multinomial Comparison Table.

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How to Run in Google Colab

1. Upload the project folder to Google Drive under MyDrive/ML/.
2. Open Assignment_01.ipynb or Assignment_02.ipynb in Google Colab.
3. Execute the Colab Drive mount cell right after the introduction:

from google.colab import drive
import os

# Mount Google Drive
drive.mount('/content/drive')

# Change working directory to assignment folder
os.chdir('/content/drive/MyDrive/ML/Assignment_01_LinearRegression')  # or Assignment_02_LogisticRegression
print("Current Working Directory:", os.getcwd())

4. Select Runtime > Run all.

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Requirements

• Python 3.8+
• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn