This is a machine learning project pipeline that uses multiple algorithms to make predictions and has a front-end interface for users to interact with. The project is designed to predict a target variable based on a set of input features.
To set up the project, follow these steps:
Clone the repository to your local machine.
Install the required dependencies by running pip install -r requirements.txt in your terminal.
Set up a virtual environment for the project (optional but recommended).
To use the project, follow these steps:
Run the app.py file to start the front-end interface.
Enter the input features in the interface and click on the "Predict" button.
The predicted value will be displayed on the interface.
The project uses multiple algorithms to make predictions. The algorithms currently implemented are:
Linear Regression
Random Forest Regression
Support Vector Regression
The algorithms are implemented using the scikit-learn library in Python.
The project uses a dataset of [insert name of dataset] to train the algorithms. The dataset is preprocessed and cleaned before being used in the pipeline.
The front-end of the project is built using [insert name of front-end library or framework]. It allows users to input the values of the input features and displays the predicted value.
This machine learning project pipeline is designed to predict a target variable using multiple algorithms and a front-end interface. It can be extended and customized to suit different use cases and datasets.