My solutions to some of Kaggle Machine Learning competitions. The repository contains 3 sections that divides the problems to 3 difficulties (Easy, Medium, Hard) according to the difficulty i faced during understanding or implementing the problem solution.

Python Packages Used:

• numpy
• matplotlib
• seaborn
• scipy
• scikit-learn
• tensorflow
• keras

Problems Solved:

• [San Francisco Crime Classification] (https://www.kaggle.com/c/sf-crime)
• [Digit Recognizer] (https://www.kaggle.com/c/digit-recognizer)
• [Iris Classification] (https://www.kaggle.com/uciml/iris)
• [Poker Rule Induction] (https://www.kaggle.com/c/poker-rule-induction)
• [What's Cooking] (https://www.kaggle.com/c/whats-cooking)
• [Bike Sharing Demand] (https://www.kaggle.com/c/bike-sharing-demand)
• [Denoising Dirty Documents] (https://www.kaggle.com/c/denoising-dirty-documents/)
• [SMS Spam Collection Dataset] (https://www.kaggle.com/uciml/sms-spam-collection-dataset)