Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly
Sign up
Appearance settings

ADD Diagrams/ Graphics on README #1467

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
alo7lika wants to merge 15 commits into recodehive:main from alo7lika:alo
Closed
Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
15 commits
Select commit Hold shift + click to select a range
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Binary file added ML Hub - Learning Resource(1)(1).png
View file Open in desktop
Copy link
Member

@sanjay-kv sanjay-kv Oct 17, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

can you remove this pic

Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
[フレーム]
Binary file added ML Hub - Learning Resource.jpg
View file Open in desktop
Copy link
Member

@sanjay-kv sanjay-kv Oct 17, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

can u remove this pic

Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
[フレーム]
Binary file added Machine Learning Workflow Flowchart(1)(1).png
View file Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
[フレーム]
Binary file added Machine Learning Workflow Flowchart.jpg
View file Open in desktop
Copy link
Member

@sanjay-kv sanjay-kv Oct 17, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

there is duplicate image remove this

Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
[フレーム]
113 changes: 113 additions & 0 deletions Prediction Models/Food Allergy Prediction/README.md
View file Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,113 @@
# 🍲 Food Allergy Prediction

Welcome to the **Food Allergy Prediction** project! This project aims to build a machine learning model that predicts food allergies based on various features such as ingredients, demographic details, and medical history. The model is designed to assist healthcare professionals and individuals in managing food allergies effectively.

## 📋 Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Dataset](#dataset)
- [Installation](#installation)
- [Model Architecture](#model-architecture)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)

## 🌟 Introduction

Food allergies are a major concern for millions of people worldwide. Accurate prediction and management of these allergies can significantly improve the quality of life. This project leverages **machine learning** techniques to identify and predict food allergies, providing insights based on historical data.

## ✨ Features

| Feature | Description |
|-----------------------|--------------------------------------------------------|
| 📊 **Data Analysis** | Exploratory Data Analysis (EDA) to identify patterns |
| 🤖 **Machine Learning**| ML model to predict food allergies based on inputs |
| 📈 **Visualization** | Graphical representation of data for better insights |
| 🏥 **Health Focus** | Tailored predictions for various demographics |

## 📚 Dataset

The dataset used in this project includes:
- **Demographic Details:** Age, gender, etc.
- **Food Details:** Ingredients and nutritional information.
- **Medical History:** Existing conditions, past allergic reactions.

> **Note:** The dataset is preprocessed and cleaned for accurate predictions. Make sure to review the data structure in the notebook for detailed insights.

## 🛠️ Installation

To run the project locally, follow these steps:

1. Clone the repository:
```bash
git clone https://github.com/alo7lika/food-allergy-prediction.git
```
2. Navigate to the project directory:
```bash
cd food-allergy-prediction
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## 🏗️ Model Architecture

The model leverages a combination of **Random Forest** and **XGBoost** algorithms for high accuracy. The architecture includes:

- **Data Preprocessing**: Handling missing values, feature scaling, and encoding categorical variables.
- **Model Training**: Using cross-validation to find the best hyperparameters.
- **Evaluation Metrics**: Accuracy, precision, recall, and F1 score.

| Step | Description |
|---------------------|---------------------------------------------|
| 1️⃣ Data Cleaning | Removing inconsistencies in the dataset |
| 2️⃣ Feature Engineering | Creating meaningful features |
| 3️⃣ Model Training | Training using Random Forest and XGBoost |
| 4️⃣ Evaluation | Measuring model performance |

## 🚀 Usage

1. Launch Jupyter Notebook:
```bash
jupyter notebook
```
2. Open the notebook file `Food Allergy Prediction.ipynb`.
3. Run the cells step by step to train the model and see the results.

> **Tip:** You can customize the dataset and re-train the model for better performance based on specific use cases.

## 📊 Results

| Metric | Value |
|--------------|---------|
| **Accuracy** | 95.2% |
| **Precision**| 92.8% |
| **Recall** | 93.5% |
| **F1 Score** | 93.1% |

The model achieved **high accuracy** and provides robust predictions across different demographic groups and food types.

## 🤝 Contributing

We welcome contributions to enhance the project! To contribute:

1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature-branch
```
3. Make your changes and commit:
```bash
git commit -m "Add a new feature"
```
4. Push to your branch:
```bash
git push origin feature-branch
```
5.Open a pull request.

## 📄 License

This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more details.

59 changes: 48 additions & 11 deletions README.md
View file Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
<a id="top"></a>
<h1 align="center">Machine Learning 🤖</h1>
<div align="center">

<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
Expand Down Expand Up @@ -26,8 +27,12 @@ If later found out, the points will be deducted. You can't be earning more than
<h1 align="center">Machine Learning 🤖</h1>

<details>
<summary><h2>:pushpin:Table of Contents</h2></summary>

<summary><h2>:pushpin: Table of Contents</h2></summary>

- [📘 Theory of Machine Learning Workflow](#theory-of-machine-learning-workflow)

</details>

- [Roadmap](roadmaps)
- [Machine Learning Roadmap](#machine-learning)
- [Roadmap.sh](#roadmapsh)
Expand Down Expand Up @@ -57,6 +62,43 @@ If later found out, the points will be deducted. You can't be earning more than
- [Interview](#interview)
- [Others](#others)
- [Conclusion](#conclusion)
---
## 📘 Theory of Machine Learning Workflow

Machine learning (ML) is a subset of artificial intelligence that focuses on building systems capable of learning from data, identifying patterns, and making decisions with minimal human intervention. The machine learning workflow is a structured approach that guides practitioners through the stages of developing effective models.

#### 1. Data Collection
The first step involves gathering relevant data from various sources, such as databases, APIs, or web scraping. Quality data is crucial, as it directly impacts the performance of the machine learning model.

#### 2. Data Preprocessing
Data preprocessing is essential for cleaning the data and preparing it for analysis. This step involves handling missing values, removing duplicates, and normalizing or standardizing features to ensure consistent scales across the dataset.

#### 3. Exploratory Data Analysis (EDA)
EDA involves analyzing data distributions and relationships through visualization techniques, such as histograms, scatter plots, and box plots. This step helps identify patterns, trends, and anomalies within the data.

#### 4. Feature Engineering
Feature engineering is the process of creating new features or transforming existing ones to improve the model's performance. This may involve techniques such as one-hot encoding for categorical variables, polynomial feature expansion, or domain-specific transformations.

#### 5. Model Selection
Choosing the right algorithm is critical to the success of the machine learning project. This step involves selecting algorithms based on the problem type (e.g., classification, regression) and the nature of the data.

#### 6. Model Training
In this stage, the selected model is trained using a portion of the dataset (training data). The model learns patterns and relationships in the data through various optimization techniques.

#### 7. Model Evaluation
Once the model is trained, it is evaluated using a separate portion of the dataset (validation/test data). Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC, which help assess the model's performance.

#### 8. Deployment
After validation, the model can be deployed into production, making it accessible for real-world applications. This step includes integrating the model into existing systems and ensuring it can handle live data.

#### 9. Monitoring & Maintenance
Post-deployment, continuous monitoring of the model's performance is necessary to ensure its effectiveness. This involves tracking model accuracy, updating it with new data, and retraining when necessary to adapt to changing conditions.

Through these stages, the machine learning workflow provides a systematic approach to building, validating, and deploying models that can yield valuable insights and drive decision-making across various domains.

<p align="center">
<img src="https://raw.githubusercontent.com/alo7lika/machine-learning-repos/refs/heads/main/Machine%20Learning%20Workflow%20Flowchart(1)(1).png" width="400" />
</p>

</details>

Expand Down Expand Up @@ -825,16 +867,15 @@ If later found out, the points will be deducted. You can't be earning more than
</td>
</tr>
</table>
</details>

<hr>


<div>
<h2><img src="https://raw.githubusercontent.com/Tarikul-Islam-Anik/Animated-Fluent-Emojis/master/Emojis/Travel%20and%20places/High%20Voltage.png" alt="High Voltage" width="35" height="35" />Conclusion</h2>
</div>

Machine Learning is an exciting and rapidly evolving field that offers endless opportunities for innovation and discovery. Its ability to analyze vast amounts of data and uncover patterns makes it indispensable for various applications, from predictive analytics and natural language processing to computer vision and autonomous systems. The wealth of libraries and frameworks available, such as TensorFlow, PyTorch, and scikit-learn, empowers developers and data scientists to build sophisticated models with relative ease. A strong community provides extensive resources, including tutorials, forums, and documentation, to support learners and professionals alike. To truly excel in Machine Learning, consistent practice is essential—engage in coding challenges, contribute to open-source projects, and apply your knowledge to real-world problems. This hands-on experience not only hones your skills but also opens doors to numerous career opportunities in tech, research, and beyond.
Machine Learning is an exciting and rapidly evolving field that offers endless opportunities for innovation and discovery. Its ability to analyze vast amounts of data and uncover patterns makes it indispensable for various applications, from predictive analytics and natural language processing to computer vision and autonomous systems. The wealth of libraries and frameworks available, such as TensorFlow, PyTorch, and scikit-learn, empowers developers and data scientists to build sophisticated models with relative ease. A strong community provides extensive resources, including tutorials, forums, and documentation, to support learners and professionals alike.

To truly excel in Machine Learning, consistent practice is essential—engage in coding challenges, contribute to open-source projects, and apply your knowledge to real-world problems. This hands-on experience not only hones your skills but also opens doors to numerous career opportunities in tech, research, and beyond.

<hr>

Expand All @@ -854,10 +895,6 @@ Machine Learning is an exciting and rapidly evolving field that offers endless o

**Never stop learning!**



<a href="#top" style="background-color: red; color: white; padding: 10px 15px; border-radius: 8px; text-align: center; font-size: 16px; text-decoration: none; display: inline-block;">⬆️ Go to Top</a>




</details>
Loading

AltStyle によって変換されたページ (->オリジナル) /