🚗 == 🍋? 1 : 0

Link to competition: https://www.kaggle.com/c/DontGetKicked/

$sudo pip install -r requirements.txt
$python main.py

1. Read 'train.csv' and 'test.csv' files
2. Preprocess the files

• Feature Engineering found in preprocess.py

3. Perform Locality Sensitive Hashing (LSH) on the dataframe for test.csv

• Finds 2000 similar rows for a query vector

4. Perform K Nearest Neighbours on the LSH result

• Finds k=1000 similar neighbours

5. Pipeline of classification learning algorithms: Neural Network -> SVM