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Food Nutrition Recommendation System/README.md

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+# **Food Nutrition Recommendation System**
+
+## 🎯 Goal
+To develop a system that recommends foods based on their nutritional value, leveraging Logistic Regression for classification to provide accurate recommendations.
+
+## 🧵 Dataset 
+Link: https://www.kaggle.com/datasets/utsavdey1410/food-nutrition-dataset
+
+## 🧾 Description
+This project aims to classify and recommend foods based on their nutritional information using a Logistic Regression model. The dataset includes various nutritional attributes of different foods, which are used to train the model to predict and recommend foods that meet specific nutritional criteria.
+
+## 🚀 Model Implemented
+Logistic Regression: A statistical model used for binary classification tasks. In this project, it was employed to classify and recommend foods based on their nutritional attributes.
+
+## 📚 Libraries Needed
+- TensorFlow: For building and training deep learning models.
+- Keras: For simplifying the creation and training of neural networks.
+- NumPy: For numerical computations and array operations.
+- Pandas: For data manipulation and analysis.
+- Matplotlib: For plotting and visualizing data.
+
+## 📊 Exploratory Data Analysis Results
+![image](https://github.com/user-attachments/assets/769486a3-24a3-4316-9bb1-bd9faa2b6b71)
+![image](https://github.com/user-attachments/assets/b9e9e6d2-cf5c-4381-882a-7a71db1b5179)
+
+## 📈 Accuracy Results
+| Model | Accuracy |
+|-------|----------|
+|Logistic Regression   |  99% |
+
+## Recommended Foods
+![image](https://github.com/user-attachments/assets/2b51b359-529b-457e-9892-bbf954ac6fd9)
+
+## 📢 Conclusion
+The Logistic Regression model achieved an accuracy of 99%, demonstrating its effectiveness in recommending foods based on nutritional information.
+
+## ✒️ Contributor
+- Name : Khushi Kalra
+- LinkedIn: https://www.linkedin.com/in/kalrakhushi/