Learn Data Science using Python

Another post starts with you beautiful people! We have learnt basic required algorithms in our previous posts. Now the time has come to apply all our learning in a real world machine learning problem. In the next coming posts you will see step by step how to work on a machine learning project and you will wonder all the previous posts you have read till now, have all the tools to achieve our goal. For all the new members who wants to get the dataset of a real world problem, just get those datasets from our beloved site- Kaggle . In this post we will work on a prediction challenged Titanic Machine Learning  organized in Kaggle and learn how to submit our model there. Competition Description : The sinking of the RMS Titanic is one of the most infamous shipwrecks in history One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sin...

Another post starts with you beautiful people! I hope after reading my previous post about  Linear and Logistic Regression   your confidence level is up and you are now ready to move one step ahead in Machine Learning arena. In this post we will be going over Decision Trees and Random Forests . In order for you to understand this exercise completely there is some required reading. I suggests you to please read following blog post before going further- A Must Read! After reading the blog post you should have a basic layman's (or laywoman!) understanding of how decision trees and random forests work.  A quick intro is as below- Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features . For instance, in the example below, decision trees learn from data to approximate a sin...