## Guide for Linear Regression using Python – Part 2

Guide for Linear Regression using Python – Part 2 This blog is the continuation of guide for linear regression using Python from this post. There must be no correlation among independent variables. Multicollinearity is the presence of correlation in independent variables. If variables are correlated, it becomes extremely difficult for the model to determine the […]

## Guide for Linear Regression using Python – Part 1

Regression is the first algorithm we need to master if we are aspiring to become a data scientist. It is one of the easiest algorithms to learn yet requires understanding and effort to get to the master it. In this blog is a guide for linear regression using Python. It will focus on linear and multiple […]

## Predicting NBA winners with Decision Trees and Random Forests in Scikit-learn

In this blog, we will be predicting NBA winners with Decision Trees and Random Forests in Scikit-learn.The National Basketball Association (NBA) is the major men’s professional basketball league in North America and is widely considered to be the premier men’s professional basketball league in the world. It has 30 teams (29 in the United States and […]

## Modeling Bitcoin’s Market Capitalization

Bitcoin has been in news quite a bit lately with the price soaring. It was named the top performing currency four of the last five year. And it’ price has the potential to hit over \$100,000 in 10 years, which would mark a 3,483 percent rise from its recent record high. In this post, we are […]

## 10 groups of Machine Learning Algorithms

In this article, I grouped some of the popular machine learning algorithms either by learning or problem type. There is a brief description of how these algorithms work and their potential use case. Regression How it works: A regression uses the historical relationship between an independent and a dependent variable to predict the future values […]

## Time-Series Predictive Analysis of DAX 30

In this blog post we’ll examine some common techniques used in time-series analysis of DAX 30 by applying them to a data set containing daily closing values from 1990 up to present day. The DAX (Deutscher Aktienindex (German stock index)) is a blue chip stock market index consisting of the 30 major German companies trading […]

## Features selection for determining House Prices ?

Home values are influenced by many factors. Basically, there are two major aspects: The environmental information, including location, local economy, school district, air quality, etc. The characteristics information of the property, such as lot size, house size and age, the number of rooms, heating / AC systems, garage, and so on. When people consider buying […]

## 4 different ways to predict survival on Titanic – part 2

continued from part 1 Classification KNeighborsClassifier In [16]: from sklearn.neighbors import KNeighborsClassifi alg_ngbh = KNeighborsClassifier(n_neighbors=3) scores = cross_validation.cross_val_score(alg_ngbh, train_data_scaled, train_data_munged[“Survived”], cv=cv, n_jobs=-1) print(“Accuracy (k-neighbors): {}/{}”.format(scores.mean(), scores.std())) Accuracy (k-neighbors): 0.7957351290684623/0.011110544261068086 SGDClassifier In [17]: from sklearn.linear_model.stochastic_gradient import SGDClassifier alg_sgd = SGDClassifier(random_state=1) scores = cross_validation.cross_val_score(alg_sgd, train_data_scaled, train_data_munged[“Survived”], cv=cv, n_jobs=-1) print(“Accuracy (sgd): {}/{}”.format(scores.mean(), scores.std())) Accuracy (sgd): 0.7239057239057239/0.015306601231185043 SVC In [18]: from sklearn.svm […]