tokens = ["linear regression"]
best_kernels(tokens, 10)

tokens = ['logistic regression', "logistic"]
best_kernels(tokens, 10)

"""
Explanation: Data Science Glossary on Kaggle
Kaggle is the place to do data science projects. There are so many algorithms and concepts to learn. Kaggle Kernels are one of the best resources on internet to understand the practical implementation of algorithms. There are almost 200,000 kernels published on kaggle and sometimes it becomes diffcult to search for the right implementation. I have used the Meta Kaggle database to create a glossary of data science models, techniques and tools shared on kaggle kernels. One can use this kernel as the one place to find other great kernels shared by great authors. Hope you like this kernel.  
Contents
<ul>
  <li>1. Regression Algorithms
    <ul>
    <li>1.1 Linear Regression</li>
    <li>1.2 Logistic Regression</li>
    </ul>
  </li>
    <li>2. Regularization Algorithms
    <ul>
    <li>2.1 Ridge Regression Regression</li>
    <li>2.2 Lasso Regression</li>
    <li>2.3 Elastic Net</li>
    </ul>
  </li>
  </li>
    <li>3. Tree Based Models
    <ul>
    <li>3.1 Decision Tree</li>
    <li>3.2 Random Forests</li>
    <li>3.3 Lightgbm</li>
    <li>3.4 XgBoost</li>
    <li>3.5 Cat Boost</li>
    </ul>
  </li>
<li>4. Neural Networks and Deep Learning
    <ul>
    <li>4.1 Neural Networks</li>
    <li>4.2 AutoEncoders</li>
    <li>4.3 DeepLearning</li>
    <li>4.4 Convolutional Neural Networks</li>
    <li>4.5 LSTMs</li>
    <li>4.6 GRUs</li>
    <li>4.7 MxNet</li>
    <li>4.8 ResNet</li>
    <li>4.9 CapsuleNets</li>
    <li>4.10 VGGs</li>
    <li>4.11 Inception Nets</li>
     <li>4.12 Computer Vision</li>
     <li>4.13 Transfer Learning</li>
     </ul>
  </li>
<li>5. Clustering Algorithms
    <ul>
    <li>5.1 K Means Clustering </li>
    <li>5.2 Hierarchial Clustering</li>
    <li>5.3 DB Scan</li>
    <li>5.4 Unsupervised Learning </li>
    </ul>
  </li>
  <li>6. Misc - Models
    <ul>
    <li>6.1 K Naive Bayes </li>
    <li>6.2 SVMs</li>
    <li>6.3 KNN</li>
    <li>6.4 Recommendation Engine </li>
    </ul>
  </li>
  <li>7.1 Data Science Techniques - Preprocessing
    <ul>
    <li>a. EDA, Exploration </li>
    <li>b. Feature Engineering </li>
    <li>c. Feature Selection </li>
    <li>d. Outlier Treatment</li>
    <li>e. Anomaly Detection</li>
    <li>f. SMOTE</li>
    <li>g. Pipeline</li>
    <li>g. Missing Values</li>
    </ul>
  </li>
  <li>7.2 Data Science Techniques - Dimentionality Reduction
    <ul>
    <li>a. Dataset Decomposition </li>
    <li>b. PCA </li>
    <li>c. Tsne </li>
    </ul>
  </li>
  <li>7.3 Data Science Techniques - Post Modelling
    <ul>
    <li>a. Cross Validation </li>
    <li>b. Model Selection </li>
    <li>c. Model Tuning </li>
    <li>d. Grid Search </li>
    </ul>
  </li>
  <li>7.4 Data Science Techniques - Ensemblling
    <ul>
    <li>a. Ensembling </li>
    <li>b. Stacking </li>
    <li>c. Bagging</li>
    </ul>
  </li>
  <li>8. Text Data 
    <ul>
    <li>8.1. NLP </li>
    <li>8.2. Topic Modelling </li>
    <li>8.3. Word Embeddings </li>
    </ul>
  </li>
 <li>9. Data Science Tools 
    <ul>
    <li>9.1 Scikit Learn </li>
    <li>9.2 TensorFlow </li>
    <li>9.3 Theano </li>
    <li>9.4 Kears </li>
    <li>9.5 PyTorch </li>
    <li>9.6 Vopal Wabbit </li>
    <li>9.7 ELI5 </li>
    <li>9.8 HyperOpt </li>
    <li>9.9 Pandas </li>
    <li>9.10 Sql </li>
    <li>9.11 BigQuery </li>
    </ul>
  </li>
<li>10. Data Visualizations 
    <ul>
    <li>10.1. Visualizations </li>
    <li>10.2. Plotly </li>
    <li>10.3. Seaborn </li>
    <li>10.4. D3.Js </li>
    <li>10.5. Bokeh </li>
    </ul>
  </li>
  <li>11. Time Series  
    <ul>
    <li>11.1. Time Series Analysis </li>
    <li>10.2. ARIMA </li>
    </ul>
  </li>
</ul>

<br><br>
1. Regression Algorithms
End of explanation
"""


tokens = ['Ridge']
best_kernels(tokens, 10)

tokens = ['Lasso']
best_kernels(tokens, 10)

tokens = ['ElasticNet']
best_kernels(tokens, 4)

"""
Explanation: 2. Regularization Algorithms
End of explanation
"""


tokens = ['Decision Tree']
best_kernels(tokens, 10)

tokens = ['random forest']
best_kernels(tokens, 10)

tokens = ['lightgbm', 'light gbm', 'lgb']
best_kernels(tokens, 10)

tokens = ['xgboost', 'xgb']
best_kernels(tokens, 10)

tokens = ['catboost']
best_kernels(tokens, 10)

"""
Explanation: 3. Tree Based Models
End of explanation
"""


tokens = ['neural network']
best_kernels(tokens, 10)

tokens = ['autoencoder']
best_kernels(tokens, 10)

tokens = ['deep learning']
best_kernels(tokens, 10)

tokens = ['convolutional neural networks', 'cnn']
best_kernels(tokens, 10)

tokens = ['lstm']
best_kernels(tokens, 10)

tokens = ['gru']
ignore = ['grupo']
best_kernels(tokens, 10, ignore)

tokens = ['mxnet']
best_kernels(tokens, 10)

tokens = ['resnet']
best_kernels(tokens, 10)

tokens = ['Capsule network', 'capsulenet']
best_kernels(tokens, 5)

tokens = ['vgg']
best_kernels(tokens, 5)

tokens = ['inception']
best_kernels(tokens, 5)

tokens = ['computer vision']
best_kernels(tokens, 5)

tokens = ['transfer learning']
best_kernels(tokens, 5)

tokens = ['yolo']
best_kernels(tokens, 5)

"""
Explanation: 4. Neural Networks and Deep Learning Models
End of explanation
"""


tokens = ['kmeans', 'k means']
best_kernels(tokens, 10)

tokens = ['hierarchical clustering']
best_kernels(tokens, 3)

tokens = ['dbscan']
best_kernels(tokens, 10)

tokens = ['unsupervised']
best_kernels(tokens, 10)

"""
Explanation: 5. Clustering Algorithms
End of explanation
"""


tokens = ['naive bayes']
best_kernels(tokens, 10)

tokens = ['svm']
best_kernels(tokens, 10)

tokens = ['knn']
best_kernels(tokens, 10)

tokens = ['recommendation engine']
best_kernels(tokens, 5)

"""
Explanation: 6. Misc - Models
End of explanation
"""


tokens = ['EDA', 'exploration', 'exploratory']
best_kernels(tokens, 10)

tokens = ['feature engineering']
best_kernels(tokens, 10)

tokens = ['feature selection']
best_kernels(tokens, 10)

tokens = ['outlier treatment', 'outlier']
best_kernels(tokens, 10)

tokens = ['anomaly detection', 'anomaly']
best_kernels(tokens, 8)

tokens = ['smote']
best_kernels(tokens, 5)

tokens = ['pipeline']
best_kernels(tokens, 10)

tokens = ['missing value']
best_kernels(tokens, 10)

"""
Explanation: 7. Important Data Science Techniques
7.1 Preprocessing
End of explanation
"""


tokens = ['dataset decomposition', 'dimentionality reduction']
best_kernels(tokens, 2)

tokens = ['PCA']
best_kernels(tokens, 10)

tokens = ['Tsne', 't-sne']
best_kernels(tokens, 10)

"""
Explanation: 7.2 Dimentionality Reduction
End of explanation
"""


tokens = ['cross validation']
best_kernels(tokens, 10)

tokens = ['model selection']
best_kernels(tokens, 10)

tokens = ['model tuning', 'tuning']
best_kernels(tokens, 10)

tokens = ['gridsearch', 'grid search']
best_kernels(tokens, 10)

"""
Explanation: 7.3 Post Modelling Techniques
End of explanation
"""


tokens = ['ensemble']
best_kernels(tokens, 10)

tokens = ['stacking', 'stack']
best_kernels(tokens, 10)

tokens = ['bagging']
best_kernels(tokens, 10)

"""
Explanation: 7.4 Ensemblling
End of explanation
"""


tokens = ['NLP', 'Natural Language Processing', 'text mining']
best_kernels(tokens, 10)

tokens = ['topic modelling']
best_kernels(tokens, 8)

tokens = ['word embedding','fasttext', 'glove', 'word2vec']
best_kernels(tokens, 8)

"""
Explanation: 8. Text Data
End of explanation
"""


tokens = ['scikit']
best_kernels(tokens, 10)

tokens = ['tensorflow', 'tensor flow']
best_kernels(tokens, 10)

tokens = ['theano']
best_kernels(tokens, 10)

tokens = ['keras']
best_kernels(tokens, 10)

tokens = ['pytorch']
best_kernels(tokens, 10)

tokens = ['vowpal wabbit','vowpalwabbit']
best_kernels(tokens, 10)

tokens = ['eli5']
best_kernels(tokens, 10)

tokens = ['hyperopt']
best_kernels(tokens, 5)

tokens = ['pandas']
best_kernels(tokens, 10)

tokens = ['SQL']
best_kernels(tokens, 10)

tokens = ['bigquery', 'big query']
best_kernels(tokens, 10)

tokens = ['gpu']
best_kernels(tokens, 10)

"""
Explanation: 9. Data Science Tools
End of explanation
"""


tokens = ['visualization', 'visualisation']
best_kernels(tokens, 10)

tokens = ['plotly', 'plot.ly']
best_kernels(tokens, 10)

tokens = ['seaborn']
best_kernels(tokens, 10)

tokens = ['d3.js']
best_kernels(tokens, 4)

tokens = ['bokeh']
best_kernels(tokens, 10)

"""
Explanation: 10. Data Visualization
End of explanation
"""


tokens = ['time series']
best_kernels(tokens, 10)

tokens = ['arima']
best_kernels(tokens, 10)

"""
Explanation: 11. Time Series
End of explanation
"""


tokens = ['tutorial']
best_kernels(tokens, 10)

"""
Explanation: 12. Some of the Best Tutorials on Kaggle
End of explanation
"""


