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https://github.com/enayar478/nomad_machine_learning_dash_app

An interactive Machine Learning app built with Dash and Plotly, developed as part of the Data Analytics Bootcamp at Le Wagon Bordeaux. It allows users to visualize data, make real-time predictions, and explore various model insights.
https://github.com/enayar478/nomad_machine_learning_dash_app

analytics cachetools dash dashboard-application data-analysis data-science deployment gunicorn interactive-visualization machine-learning pandas plotly plotly-dash prediction-model python python3 render scikit-learn web-application

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An interactive Machine Learning app built with Dash and Plotly, developed as part of the Data Analytics Bootcamp at Le Wagon Bordeaux. It allows users to visualize data, make real-time predictions, and explore various model insights.

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# Nomad Agency - Machine Learning and Dash App



![Nomad Agency Banner](https://github.com/Enayar478/nomad_machine_learning_dash_app/blob/main/assets/img/banner_nomad_agency.jpg)



We are Nomad Agency, a team of five Data Analysts, and we present a web application project that we developed to deploy an interactive Machine Learning model through Dash. This project integrates several tools and libraries within a Python environment, with a focus on the user interface and predictive interactivity.



## Project Description



The goal of this application is to provide a simple user interface to interact with a real-time prediction model based on a dataset. This project leverages visualization libraries like Plotly and uses Dash to structure the interface and manage interactive callbacks.



## Technologies Used



- **Dash**: to build the interactive web application.

- **Plotly**: to create dynamic data visualizations.

- **Pandas** and **Numpy**: for data manipulation.

- **Scikit-learn**: for predictive modeling (classification, regression, etc.).

- **Pickle**: to serialize and deserialize Machine Learning models.

- **Cachetools**: to manage caches, optimizing the performance of frequent model calls.

- **Gunicorn**: for deploying the application on Render.com.



## Key Features



- **Data Visualization**: Users can explore the data used to train the model through interactive charts.

- **Dynamic Predictions**: A form allows users to input specific data and instantly receive a model prediction.

- **Performance Optimization**: Through cache management, response times for frequent queries are reduced.

- **Deployment on Render.com**: The application is publicly accessible, facilitating demonstration and interaction.



## User Interface



### Before (Without Input Data)

Here is a screenshot of our application's interface before entering any data:



![User Interface - Before](https://github.com/Enayar478/nomad_machine_learning_dash_app/blob/main/assets/img/homepage_dash_app.jpg)



### After (With Results)

Here is a screenshot of our application's interface after submitting data and displaying the results:



![User Interface - After](https://github.com/Enayar478/nomad_machine_learning_dash_app/blob/main/assets/img/homepage_results_dash_app.jpg)



### Project Link



You can explore the live project via this link: [Nomad Machine Learning Dash App](https://nomad-machine-learning-dash-app.onrender.com/).



## Areas for Improvement



- **Security**: Future implementation of authorization and authentication management to restrict access to certain parts of the application.

- **Scalability**: Testing other deployment services and configuring the project to support a higher number of concurrent users.



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This project complements the analysis conducted as part of another project:



# Client Nomad Project



## Project Context



As part of our Data Analyst Bootcamp, we collaborated as Nomad Agency to explore and analyze a dataset provided by a client. Our mission was to address a commercial operations optimization problem.



## Problem Statement



*To understand the key factors behind the success of a commercial operation, in terms of customer acquisition and new customer recruitment.*



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## Methodology



To achieve this, we worked with multiple files containing crucial information on products, sales, advertising investments, and user interactions. By analyzing this data, we identified the factors influencing operational performance and proposed recommendations based on our findings.



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## Project Setup & Tracking



[Project Tracking](https://www.notion.so/9302c505c7b04fb7b5e3ce8a8a5a4e17?pvs=21)