Calorie Predictor

Project Overview

This project aims to predict whether a food item is high in calories based on its nutritional information. It uses machine learning techniques to build a classification model that identifies high-calorie foods. The model is built using Python, with a focus on data cleaning, imputation, visualization, and classification.

Features of the Project

• Data Imputation: Missing values were handled using advanced imputation techniques, such as filling in missing values based on neighboring data points.

• Data Visualization: Visualizations were created to explore the dataset and identify trends in the relationship between calorie content and macronutrient levels.

• Machine Learning: Logistic Regression was used to classify foods as high or low calorie based on their nutritional content. Model accuracy, confusion matrix, and coefficient analysis were included to evaluate model performance.

Technologies Used

• Python: Primary programming language for data processing and model development.
• Pandas: Data manipulation and cleaning.
• scikit-learn: Machine learning algorithms and evaluation metrics.
• Matplotlib & Seaborn: Data visualization libraries for creating static plots.
• Altair/Plotly (Optional): Can be used for interactive visualizations.

Project Files

• calorie_predictor.ipynb: Jupyter notebook containing all code for data cleaning, visualization, and model training.

Instructions to Run the Project

1. Clone the repository:

   git clone <repository-link>

2. Install the required packages:

   pip install -r requirements.txt

3. Run the Jupyter notebook:

   jupyter notebook calorie_predictor.ipynb

Results

The logistic regression model achieved an overall accuracy of 98.36%, with high-calorie foods being identified correctly most of the time. Improvements were made through data imputation and feature engineering.

Future Work

• Try additional models: Implement models like Random Forest, SVM, or XGBoost and compare their performance.
• Hyperparameter tuning: Use GridSearchCV to find the optimal hyperparameters for the models.
• Feature engineering: Explore additional features that could improve the model's performance.

Conclusion

This project showcases the end-to-end process of building a machine learning model, from data cleaning and imputation to model evaluation and visualization. It highlights skills in data preprocessing, visualization, and classification using logistic regression.