📊 Multiple Variable Linear Regression - Supervised Machine Learning

Welcome to my first step into Supervised Learning using Multiple Variable Linear Regression!
This project is part of my Machine Learning journey where I explore different algorithms through hands-on practice.

---

📌 Project Overview

Goal:
To predict a continuous target variable (like crop yield, house price, etc.) using multiple independent features (e.g., temperature, humidity, rainfall).

This is an extension of simple linear regression where the model can handle more than one input feature to improve prediction accuracy.

---

🧠 What I Learned

• Understanding how multivariate linear regression works.
• Implementing the model using scikit-learn in Python.
• Evaluating performance using R² Score, Mean Absolute Error, etc.
• Visualizing predictions vs actual data.
• Handling datasets and preprocessing where needed.

---

📁 Folder Structure

Multiple_Variable_Linear_Regression/
├── Multiple Variable Linear Regression.ipynb
├── README.md
└── hiring.csv

📌 Note: The dataset used is purely for practice/educational purposes. Feel free to replace it with your own for experimentation.

---

⚙️ How to Run

1. Clone this repository or download the ZIP.
2. Open the .ipynb file using Jupyter Notebook or VS Code.
3. Make sure scikit-learn, numpy, pandas, and matplotlib are installed.
4. Run all cells to train and evaluate the model.

pip install scikit-learn pandas matplotlib

---

📷 Sample Output

(Optional: Add a screenshot here if you visualize your predictions)

---

🚀 What's Next?

I'm continuing my learning path through other supervised ML models like:

• Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Random Forest
• Naive Bayes

Stay tuned!

---

📬 Connect with Me

Feel free to reach out or follow my ML journey:

• 💼 LinkedIn
• 📧 Email: gomesrohit92@gmail.com

---

⭐ If you found this helpful or interesting, leave a ⭐ on the repo!