Solutions and Guides to various Kaggle Machine Learning Competitions
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Digit Recognizer
House Prices: Advanced Regression Techniques
Sentiment Analysis on Movie Reviews
Titanic: Machine Learning from Disaster
LICENSE
README.md

README.md

Kaggle Competitions Guides and Solutions

Solutions and Guides to various Kaggle Machine Learning Competitions.

Guides and solutions are present in Jupyter Notebook format.

Programming Language: Python

Datasets are taken from Kaggle Competitions.

Competitions

1. Titanic: Machine Learning from Disaster

Trying out the following classifiers:

  • Decision Tree
  • Random Forest
  • Support Vector Machine (SVM)
  • Logistic Regression
  • Linear SVC
  • Perceptron
  • k-Nearest Neighbor (KNN)
  • Naive Bayes
  • Stochastic Gradient Decent (SGD)

2. House Prices: Advanced Regression Techniques

Trying out the following regression models:

  • Lasso
  • Elastic Net
  • Kernel Ridge
  • Gradient Boost
  • XGBoost
  • LightGBM

3. Digit Recognizer

Using Deep Learning with Keras - the Neural Network Library written in Python.

The following Neural Network models are used for this problem:

  • Multi-layer Perceptron Model (MLP)
  • Convolutional Neural Network (CNN) Model

4. Sentiment Analysis on Movie Reviews

  • Using Logistic Regression Model

  • Using Multiple Models: Logistic Regression, SGD, Naive Bayes, OneVsOne Models

  • Using Long short-term memory (LSTM) recurrent neural network (RNN) model for IMDB dataset

  • Using Long short-term memory (LSTM) recurrent neural network (RNN) model for Kaggle dataset


My Kaggle Profile