Machine Learning

This repository contains various Jupyter Notebooks and datasets for practicing machine learning concepts, focusing on regression techniques and data preprocessing.

Repository Overview

Notebooks

1. Linear Regression Single Variable

• Implementation of linear regression using a single variable.
• Linear Regression Single Variable.ipynb

2. Joblib & Pickle

• Demonstrates how to save and load machine learning models using Joblib and Pickle.
• Joblib & Pickle.ipynb

3. Linear Regression Multi-Variable

• Implementation of linear regression using multiple variables.
• Linear Regression Multi-Variable.ipynb

4. Dummy Variable & One Hot Encoding

• Explanation and implementation of dummy variables and one-hot encoding for categorical data.
• Dummy Variable & One Hot Encoding.ipynb

5. Train_Test_Split

• Shows how to split datasets into training and testing sets using the train_test_split function.
• Train_Test_Split.ipynb

Datasets

1. Houseprice

• Dataset related to house prices for regression analysis.
• Houseprice.csv

2. Canada_per_capita_income - Single Variable

• Per capita income data for single-variable regression.
• canada_per_capita_income - Single Variable.csv

3. Hiring - MultiVari - LR

• Hiring data for multi-variable linear regression.
• hiring - MultiVari - LR.csv

4. Homeprices - One Hot Encoding

• Dataset used to demonstrate one-hot encoding techniques.
• Homeprices - One Hot Encoding.csv

Getting Started

Clone this repository:

Git Clone: https://github.com/your-username/Machine-Learning.git

Navigate to the project directory:

cd Machine-Learning

Prerequisites

Ensure you have the following installed:

• Python 3.x

• Jupyter Notebook

• Libraries: numpy, pandas, matplotlib, scikit-learn, etc.

Usage

Open the Jupyter Notebooks to understand and experiment with different machine learning concepts.

Modify datasets and code to explore variations.