A house price prediction project is a data-driven approach to estimating the future value of a residential property using statistical and machine learning techniques with the goal of providing insight and forecasting capabilities.
A house price prediction project is a predictive modeling project that uses statistical and machine learning techniques to predict the future sale price of a residential property based on historical data and various other factors. The project typically involves gathering and cleaning real estate data, developing a model, evaluating model performance, deploying the model, and maintaining the model over time.
This dataset contains information about previously sold houses. This data can be used for a lot of purposes such as price prediction to exemplify the use of linear regression in Machine Learning.
The columns in the given dataset are as follows:
- Avg_Area_Income
- Avg_Area_House_Age
- Avg_Area_Number_of_Rooms
- Avg_Area_Number_of_Bedrooms
- Area_Population
- Price
- Address
The process of feature selection in machine learning is used to identify and select the most relevant features from a dataset to improve the performance and efficiency of a machine learning model. It is an important step in the model building process as it can help to reduce overfitting, increase model interpretability, and improve the accuracy of predictions.
A correlation matrix can be used to identify highly correlated features, which can then be removed or consolidated. Highly correlated features can cause a problem in machine learning models as they can introduce multicollinearity, which can lead to unstable and unreliable model estimates.
Outlier treatment is the process of identifying and handling extreme values or observations that are significantly different from other observations in a dataset. Outliers can have a significant impact on the results of data analysis and modeling, and can skew the mean and standard deviation of a dataset.
-
Visualization: Outliers can be identified by creating visualizations such as box plots, scatter plots, and histograms to identify observations that fall outside of the typical range.
-
Interquartile range (IQR): Outliers can be identified by calculating the interquartile range (IQR), which is the difference between the first and third quartile. Values that fall outside of 1.5 * IQR above the third quartile or 1.5 * IQR below the first quartile are considered outliers.
- Imputing the missing values: This method can be used to replace outliers with the mean, median, or mode of the dataset.
Feature transformation is the process of applying mathematical functions to the features in a dataset in order to change their distribution or to extract additional information from the data. Feature transformations are often used to improve the performance of machine learning models by making the features more suitable for the model.
-
Label encoding is a method of converting categorical variables, represented as text values, into numerical values. It assigns a unique numerical value to each category or level of a categorical feature. This is often used as a preprocessing step before training a machine learning model.
-
Target encoding is a technique used in machine learning to encode categorical variables. It replaces each category with the average value of the target variable for that category. This can improve the performance of a model by allowing it to better handle categorical variables. It is also known as mean encoding or probability encoding.
Feature scaling is the process of normalizing the range of values for each feature in a dataset. This is often done to ensure that all features are on a similar scale and to prevent some features from having a greater impact on the outcome of a machine learning model than others.
- StandardScaler is a pre-processing method in machine learning used to standardize a dataset by subtracting the mean and scaling to unit variance. It is commonly used for feature scaling before applying a supervised learning algorithm to a dataset.
-
Linear regression is a supervised machine learning algorithm that is used to predict a continuous dependent variable (i.e. a real value) based on one or more independent variables. It assumes a linear relationship between the independent variables and the dependent variable, and uses this relationship to fit a line through the data points.
-
Decision Tree Regressor is a type of supervised machine learning algorithm that is used for regression tasks. It is a decision tree algorithm where each internal node represents a feature and each leaf node represents a predicted value. The tree is constructed by recursively splitting the feature space into smaller regions, with each split chosen to minimize a certain impurity criterion such as mean squared error.
-
Random Forest Regressor is an ensemble machine learning algorithm that is used for regression tasks. It is based on the decision tree algorithm, but instead of building a single decision tree, it builds multiple decision trees and combines their predictions to produce a final result.
-
Polynomial Regression is a type of supervised machine learning algorithm that is used for regression tasks. It is similar to linear regression, but instead of fitting a straight line through the data points, it fits a polynomial equation of the form
The R-squared (R2) is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It ranges between 0 and 1, where a value of 1 indicates that all variance in the dependent variable is predictable from the independent variable(s) and a value of 0 means that no variance in the dependent variable can be predicted from the independent variable(s). R2 is often used as a measure of how well a model fits the data, with a higher R2 indicating a better fit.
R-squared values of Algorithms:
- Linear Regression: 0.5721618289970026
- Decision Tree Regressor: 0.7465886696289258
- Random Forest Regressor: 0.8794610139823527
- Polynomial Regression: 0.9110287620218143
We chose the Random Forest Regressor because the highest R-squared value after polynomial regression.
Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model. Hyperparameters are parameters that are not learned from the data, but are set before training the model.
Random search: This method randomly samples hyperparameter values from a predefined distribution. It is more efficient than grid search because it doesn't evaluate all possible combinations, but it still requires a large number of evaluations to find a good set of hyperparameters.
Hyperparameters for Random Forest Regressor :
- n_estimators: 800,
- min_samples_split: 2,
- min_samples_leaf: 1,
- max_features: 'auto',
- max_depth: 10,
- bootstrap: True
R-squared value of Random Forest After Hyper Tuning: 0.8802638029848904
So, here we are getting better results with polynomial regression as compared to random forest regressor after hyperparameter tuning. Hence we are selecting a polynomial regressor as the main model for the web app.
Flask is a web framework for building web applications using the Python programming language. It is a micro-framework that provides the basic functionality needed to build web applications, such as routing and request handling, without including a lot of additional features or libraries. This makes it lightweight and easy to use, but also allows for flexibility and customization.
After inserting values for the attribute such as the address of the location, area population, average ihncome, number of rooms, age of the house our flask web app will predict the selling price of the house.
To download a house price project, you can use the git clone command. This command creates a copy of the repository in a new directory on your local machine.
git clone https://github.com/nileshparab42/USA-Housing-Selling-Price-Prediction.git
To set up the project, you can use the pip command to install the required packages specified in the requirements.txt file.
pip install -r requirements.txt
This will install all of the required packages for the project. If you are using a virtual environment for the project, you should activate the environment before running this command.
pip install virtualenv
virtualenv myenv
source myenv/bin/activate
pip install -r requirements.txt
This will create a new virtual environment called myenv, activate it, and then install the required packages.
You can also specify the notebook file location or the working directory, after the command "jupyter notebook" or "jupyter lab" like:
jupyter notebook /path/to/notebook/directory
or
jupyter lab /path/to/notebook/directory
It will open the Jupyter notebook or lab in the specified directory, making it easier to navigate to the notebook you want to open.
To install a Flask project, you can use the following commands in your command line interface:
Create a new directory for your project and navigate to it in the command line:
mkdir myproject
cd myproject
Create and activate a virtual environment using virtualenv:
virtualenv venv
source venv/bin/activate
or conda:
conda create --name myenv
conda activate myenv
Install Flask and any other required dependencies by running:
pip install flask
and
pip install -r requirements.txt
the project has a requirements file.
Create a new file named app.py or main.py in the project directory:
Copy code
touch app.py
In the app.py or main.py file, import Flask, create an instance of the Flask class, and define the routes and functions that will handle the requests.
Run the application by executing:
flask run
or
python app.py
or
python main.py
This will start the development server and make the application accessible at "http://localhost:5000"
If you want to deploy your application to a production environment, you can use a web server like Gunicorn or uWSGI to serve the application. It's worth noting that you can also use frameworks like Flask-CLI or Flask-Script to create more advanced command-line scripts for your application.
- Nilesh Parab (Project Lead) - Website
- This project was inspired by the work of the CodeWithHarry.
- We also used resources and tools from the GeeksforGeeks to develop and test our project.