Diet Planner with unsupervised learning and LLMs

This repository contains code for predicting weight status using the K-means clustering algorithm and generating a diet plan based on the predicted weight status.

Introduction

This project aims to help individuals understand their weight status and generate a personalized diet plan to maintain or achieve a healthy weight. It utilizes machine learning techniques to predict weight status and recommends a diet plan accordingly.

Files

• weight_status_prediction.ipynb: Jupyter Notebook containing the code for K-means clustering to predict weight status.
• diet_plan_generation.ipynb: Jupyter Notebook containing the code for generating a diet plan based on the predicted weight status.
• data.csv: Dataset used for weight status prediction.
• food_data.csv: Dataset containing food information for generating the diet plan.

Functions and Modules

1. euclidean_distance(point1, point2)

• Calculates the Euclidean distance between two points in a multidimensional space.
• Used within the K-means clustering algorithm to measure the distance between data points and cluster centroids.

2. assign_points_to_clusters(clusters, data)

• Assigns each data point to the nearest cluster centroid based on Euclidean distance.
• Returns the labels indicating the assigned cluster for each data point.

3. compute_clusters(data, labels, n_clusters)

• Computes the new centroids of the clusters based on the mean of the points assigned to each cluster.
• Returns the updated cluster centroids.

4. kmeans(data, n_clusters, max_iters=1000)

• Implements the K-means clustering algorithm to group data points into clusters.
• Returns the cluster labels and centroids.

5. predict_weight_status(user_data, clusters, cluster_majority_class)

• Predicts the weight status of a user based on their input data.
• Utilizes the K-means clustering model and majority class labels within clusters.

6. diet_plan()

• Generates a diet plan based on the predicted weight status.
• Calculates the average daily calories for each weight status category.
• Selects food items from a dataset based on calorie content and weight status.
• Uses a LLM to help make a diet plan.

Usage

1. Install the required packages using pip install -r requirements.txt.
2. Open and run the Diet_Planner.ipynb notebook to predict weight status and make a diet plan.
3. Ensure that the Dataset.csv file is in the same directory as the notebook.
4. Ensure that the Food and Calories - Sheet 1.csv file is in the same directory as the notebook.

Requirements

• Python 3
• Jupyter Notebook
• Pandas
• Numpy (along with other used libraries)

Contributors

• Muhammad Hisan Usman

Feel free to contribute by opening issues or submitting pull requests.

Disclaimer: The use of this code complies with all governing bodies; however, it should not be used as a substitute for professional medical advice, diagnosis, or treatment. We are not liable for any warranty or liability for your use of this information. (USE IN THE PRESENCE OF A DIETICIAN)!