This repository showcases my hands-on learning and implementation of Machine Learning models using real-world datasets.
It demonstrates my ability to build end-to-end ML pipelines — from data ingestion and preprocessing to model training, evaluation, and visualization — using Python and industry-standard libraries.

Focused on learning by implementing real ML workflows, not just theory.

• Practical understanding of Machine Learning fundamentals
• Ability to work with real-world datasets
• Experience with data preprocessing and feature engineering
• Model training, evaluation, and interpretation
• Clean, readable, and modular ML code

This repository is intended as a portfolio for internships and entry-level roles in Data Science / Machine Learning.

• Python
• Pandas – data manipulation
• NumPy – numerical computation
• Matplotlib – data visualization
• scikit-learn – ML models & evaluation
• KaggleHub – dataset integration

• Linear Regression (Regression problems)
• More models will be added as learning progresses

Each project includes:

• Dataset loading (CSV / Kaggle)
• Feature–target separation
• Categorical data encoding (One-Hot Encoding)
• Train–test split
• Model training
• Performance evaluation
• Visualization of results

Problem Statement:
Predict medical insurance charges based on personal and lifestyle attributes.

Dataset: US Health Insurance Dataset (Kaggle)
Target Variable: charges (continuous numerical value)

1. Loaded dataset using KaggleHub
2. Performed data preprocessing and encoding of categorical variables
3. Split dataset into training and testing sets
4. Trained a Linear Regression model
5. Evaluated model performance using regression metrics
6. Visualized actual vs predicted values

• Mean Absolute Error (MAE) – average prediction error
• Mean Squared Error (MSE) – penalizes large errors
• Root Mean Squared Error (RMSE) – error in original units
• R² Score – overall model fit

These metrics help assess both accuracy and reliability of predictions.

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git

2. Install dependencies:

pip install pandas numpy matplotlib scikit-learn kagglehub

3. Run the model script or notebook:

python model_file.py

Aspiring Machine Learning Engineer with a strong interest in applied ML and data-driven problem solving.

Kaggle for datasets

scikit-learn documentation

Open-source ML community