Whatcooking

(First Machine Learning Project)

Introduction

What's Cooking is a dataset available on Kaggle and provided by yummly. The Dataset include a train.json file containing 39774 recipes with 20 different cuisine types that results in a unbalanced dataset.

Task

The challenge is to find a model that predict cuisine types using the ingredients as features.

Approches

In This project i decide to implement three differents models:

• Naive Bayes
• Sofmax Regression
• Neaural Network

The first two models were implemented using scikit-learn library, while Neural Network was implement with TensorFlow 2.

Features extraction

The dataset contained only two features:

• id
• ingredients

Id is use just for identify predictions, while ingredients is processed for extract single ingredients for each recipe, and then i clean the ingredients by:

1. removing numbers
2. removing special chars
3. making every letters lowercase
4. removing extra whitespaces
5. removing units
6. applyng lemmatizer.

Libraries used

• re
• pandas
• scikit-learn.preprocessing
• ntlk.stem.WordNetLemmatizer

Exploratory Data Analisys

The eda results in a soft manner, checking the occurrency of each ingredient and plotting a bar chart that show how many ingredients have a range of n occurency, over differents ranges.

Libraries used

• re
• pandas
• matplotlib.pyplot

Charts

Model Tuning

Each model take in input words with a minimum document frequency of 50 units and a maximum document frequency of 6000 units, while for n-gram, 1-gram and 2-gram are choose as the best result. These statistics are choose using Naive Bayes, this model is used as a base model for check which words statistics perfom best over f1-micro-score, and they are use with the other models too.

Performance metrics

I use f1-micro-score as performance metric, because the dataset task is not to better predict a class respect to the others, but to predict all classes with the same probability.

Approaches

Each model is tune using grid search technique based on reguralization and learning-rate.

Performance

performance are calculated using f1-micro-score.

result	model	score	regularisation	regularisation factor*	learning rate
1°	Neural Network	0.77	n/a	n/a	0.0001
1°	Sofmax Regression	0.77	l2	1.0	n/a
3°	Naive Bayes	0.71	n/a	n/a	n/a

*regularisation factor is 1.0 because the model doesn't use regularisation.

Conclusion

The f1-micro-score that neural network and softmaxregression reached is good, but unfortunally the confusion matrix result with a lot of false positive, so the models do not perform well in all classes. All false positive predictions are check using confusion matrix and individual f1-score are check too. These results depend on the imbalance of the dataset, infact class with higher samples has higher f1-score.

Possible implementation

There are two possible implementation:

• Resampling: for manage the imbalace of the dataset it could be a good practice to apply undersampling to large classes such as italian and apply oversampling to smaller classes. It is not a one of the best practice, but sometimes can be really useful.
• different models: It could be interesting to try models like Support Vector Machine or to check if there is a way to adapt this dataset to Recurrent Neural Network.