Welcome to the "model_hub/courses/practical_projects/machine_learning/housing_prediction" section! This practical project will guide you through the process of building a housing price prediction model using machine learning techniques.
Project Overview
In this project, you will:
- Learn about the housing dataset and its features.
- Preprocess the data to make it suitable for machine learning.
- Choose a suitable machine learning algorithm for regression.
- Train and evaluate the model.
- Optimize the model for better performance.
Dataset
The housing dataset is a widely used dataset for regression tasks. It contains information about houses in the Boston area, including the price of the house, the number of rooms, the age of the house, and other features.
Preprocessing
Before training the model, you need to preprocess the data. This includes:
- Handling missing values
- Encoding categorical variables
- Scaling numerical variables
Machine Learning Algorithms
There are several machine learning algorithms you can use for regression tasks. Some popular ones include:
- Linear Regression
- Decision Trees
- Random Forest
- Gradient Boosting
Training and Evaluation
Once you have chosen a machine learning algorithm, you need to train and evaluate the model. This involves:
- Splitting the data into training and testing sets
- Training the model on the training set
- Evaluating the model on the testing set
Optimization
After evaluating the model, you can optimize it for better performance. This involves:
- Tuning hyperparameters
- Trying different algorithms
- Feature engineering
Further Reading
For more information on machine learning and housing price prediction, check out the following resources: