ML Project for Evaluating the Best Model README

Description:

This repository comprises the code for data pipelines and model training in a machine learning project. The system is designed to ingest data, perform data transformation and feature engineering, train a model, and evaluate the model's performance.

System Architecture:

The system architecture is structured as follows:

• Data Ingestion: Data is ingested from a source (e.g., database, CSV file) and loaded into a data pipeline.

• Data Transformation: The data undergoes transformation into a format suitable for model training. This may involve tasks such as cleaning, normalization, and scaling.

• Feature Engineering: Features are extracted from the data to be utilized in training the model.

• Model Training: The data is used to train a machine learning model. The specific model architecture employed depends on the task.

• Model Evaluation: The trained model is evaluated on a test dataset to assess its performance.

Pipelines:

The pipelines are designed to be modular and scalable.

Models:

The models are implemented using Sci-kit Learn. The specific model architecture will vary based on the task.

Evaluation:

The models are evaluated using accuracy.

Getting Started:

To initiate this project, follow these steps:

1. Install the required dependencies.
2. Configure the pipelines and models.
3. Run the pipelines to train and evaluate the models.

Dependencies:

The following dependencies are necessary to run this project:

• numpy
• pandas
• python-dotenv
• mysql-connector-python
• pymysql
• scikit-learn
• seaborn
• catboost
• xgboost
• Flask
• dill
• mlflow

Running the Pipelines:

Execute the pipelines using the following commands:

python app.py

I hope this helps! Feel free to reach out if you have any further questions or concerns.