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