ML_linear_regression

Overview

This project demonstrates the application of Supervised Learning using Linear Regression to predict housing prices based on various features such as the number of rooms, crime rates, and more. The project uses the famous Boston Housing Dataset, which is often used as a benchmark dataset for regression problems.

Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data, meaning that the input data comes with corresponding output labels. The goal is to learn a mapping function that can predict the output labels for unseen data.

In this project, we use Linear Regression, a supervised learning algorithm that models the relationship between a dependent variable (target) and one or more independent variables (features). Linear regression assumes a linear relationship between the inputs and the target, and the model tries to find the best-fitting line (or hyperplane in higher dimensions) that minimizes the error between predicted and actual values.

Project Description

Problem:

Predict housing prices in Boston based on various features like the average number of rooms in the house, crime rates, and more. The goal is to build a regression model that predicts the price of a house based on these features.

Dataset:

The project uses the Boston Housing Dataset that is built into scikit-learn and contains 506 samples with 13 features. Each feature is a different characteristic of a house or neighborhood, and the target is the price of the house (in thousands of dollars).

Steps Involved:

1. Data Exploration and Visualization:

   • Load the dataset and explore its structure.
   • Visualize relationships between the features and target variable.

2. Data Preprocessing:

   • Split the data into training and testing sets.
   • Handle any missing or irrelevant data.

3. Model Training:

   • Apply Linear Regression using the scikit-learn library to train the model on the training data.

4. Model Evaluation:

   • Evaluate the model's performance using metrics like Mean Squared Error (MSE) and R-squared.

5. Visualization:

   • Visualize the results with scatter plots and regression lines to assess the model's performance.

Libraries Used:

• scikit-learn: For implementing the machine learning model.
• pandas: For data manipulation.
• numpy: For numerical operations.
• matplotlib, seaborn: For data visualization.

Installation

To get started with this project, you need to have Python 3 and the following libraries installed:

pip install numpy pandas scikit-learn matplotlib seaborn