Kaggle Competition: Pet.my

We divide the Competition in 4 parts:

• Data Preparation : This is the first part of the competition i.e. downloading data, extracting features and creating features.

• Data Description : Analyze graphically and statistically the impact of each features on Adoption Speed.

• Multi Class Models : Implement XGboost, LightGBM, Adaboost, SVM, SGD, Nearest Neight and combine all the models together for more robust prediction.

• Image Multi Class : Implement Convolutional Neural Network (CNN) to class images (pending)

Requirements

You need Python 3.1 or later to run the file Pet.my.ipynb.

The following packages are necessary for this notebook.
For features extraction:

• zipfile
• PIL
• pandas
• io
• os
• json
• glob
• collections
• functools
• cv2
• objectpath

For visualisation:

• numpy
• seaborn
• matplotlib
• wordcloud

For modelisation:

• lightgbm
• sklearn
• xgboost

Files used

All training file of the Kaggle Competition are included:

• Train.csv
• breed_labels.csv
• color_labels.csv
• state_labels.csv
• train_metadata.zip (`for storage reason zip file are not saved in this GitHub``)
• train_images.zip
• train_sentiment.zip

Conclusion on Data Description Analysis

The category 0 (pet was adopted on the same day as it was listed) concerns only 3% of the Dataset. The prediction will be hard for this category.
Features that have an impact on Adoption Speed:

• Type: cats are adopted faster than dogs
• Mixed gender are adopted slower certainly due to the obligation to adopt more than one pet.
• An animal with more fur is adopted faster
• Younger pets are adopted faster
• Small pets are adopted faster
• Mixed Breed seem to be adopted faster
• Free cats seem to have an impact on the Adoption
• Most Pets are adopted faster in Selangor and slower in Pulan Pinang and Kuala Lumpur. Selangor is the suburb of Kuala Lumpur.
• The bigger rescuer seem to have faster adoption
• Higher Sentiment Score -> fast adoption
• If the Pet Name is missing than the adoption is slower
• Topicality and the image description seem to have an impact
• SVD and NMF variables are important features for Adoption Speed
• Open source like the GDP per region have an impact on Adoption Speed

Features with no impact or less impact on Adoption Speed:

• Vaccinated, Dewormed and Sterilized seem to have no impact on Adoption Speed?
• Colors seem to have no impact

Conclusion on Multi Class Models

The data are ordinal in this case we can implement regression models and optimized the boundaries. Thus, if the prediction is between:

• y_pred < 0.5 then class{0}
• y_pred in [0.5-1.7[ then class{1}
• y_pred in [1.7-2.6[ then class{2}
• y_pred in [2.6-3.6[ then class{3}
• y_pred >3.6 then class{4}