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# 🤖 ML: My Machine Learning Portfolio

This repository serves as a dedicated collection of my machine learning projects, tutorials, and practical implementations. It is a working portfolio showcasing my skills in data cleaning, model building, hyperparameter tuning, and performance evaluation using Python and its popular ML libraries (Scikit-learn, Pandas, NumPy, Matplotlib).

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## 🚀 Projects & Implementations

Below is a list of the models and projects contained within this repository, demonstrating various machine learning concepts from fundamental linear models to advanced neural networks.

### 1. Simple Linear Regression: CO2 Emissions Prediction

**Key Takeaways:**
* **Methodology:** Implementation of OLS (Ordinary Least Squares) using `scikit-learn`.
* **Result:** Achieved an **R-squared ($R^2$) score of $0.7877$**, confirming a strong linear relationship between `ENGINESIZE` and `CO2EMISSIONS`.
* **Code Focus:** Data cleaning, train-test splitting, model fitting, and metric-based evaluation (MAE, MSE, $R^2$).

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## 🛠️ Technology Stack

This repository primarily utilizes the following tools and libraries:

* **Python:** The core programming language for all projects.
* **Scikit-learn (sklearn):** For model building, training, and evaluation (e.g., `LinearRegression`, `train_test_split`, `r2_score`).
* **Pandas & NumPy:** Essential libraries for data manipulation, loading, and numerical operations.
* **Matplotlib:** Used for data visualization, plotting distributions, and visualizing the final regression line.
* **Jupyter Notebooks (.ipynb):** The primary format for documenting code, results, and explanatory notes.

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## 🤝 Contribution & Contact

Feel free to fork this repository, explore the notebooks, and provide feedback!

* **Connect with me:** [https://www.linkedin.com/in/gautam-n-chipkar-348b092a5/]
* **GitHub:** [@gee-46](https://github.com/gee-46) 
## 📂 Repository Structure

The projects are organized logically to make navigation easy:

```bash
ML/
├── linear_regression_co2_emissions.ipynb 
├── data/                                 
└── README.md

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