poojapbabar/Salary_price_predication

End-to-End ML Project README

This repository contains the code for an end-to-end machine learning project. Below are the steps to set up and run the project.

Steps to Set Up the Project

Step 1: Create a New Environment

conda create -p venv python==3.8
conda activate venv/

Step 2: Create a .gitignore File

Create the file by right-clicking and include the venv directory in it.

Step 3: Create a requirements.txt File

pip install -r requirements.txt

Step 4: Create a setup.py File

This file is used to install the entire project as a package. Additionally, it contains a function to read the packages from requirements.txt.

Step 5: Create a src Folder

Include exception.py, logger.py, and utils.py files in this folder. Make this folder a package by including an init.py file. The src folder will also include another folder named components.

Step 5.1: Create a components Folder

Include data_ingestion.py, data_transformation.py, model_trainer.py, and an init.py file. These components are interconnected.

Step 5.2: Create a pipeline Folder

Create two Python files, training_pipeline.py and prediction_pipeline.py, with an init.py file.

Step 6: Create a notebook Folder

Create a folder named data and include the dataset. Additionally, create an EDA.ipynb file to perform exploratory data analysis and model training.ipynb.

Step 7: Create an app.py File

This file will contain the Flask application for serving the machine learning model.

Step 8: Create a templates Folder

Create a folder named templates to store HTML templates for the Flask application.