## 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 […]

## 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 […]

## Submission for Kaggle’s Titanic Competition

Posted 1 CommentPosted in Kaggle, Machine Learning, Predictive Analysis

Following is my submission for Kaggle’s Titanic Competition In [361]: import pandas as pd import numpy as np In [362]: df_train = pd.read_csv(r’C:\Users\piush\Desktop\Dataset\Titanic\train.csv’) In [363]: df_train.head(2) Out[363]: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 1 2 […]

## Submission for Predicting Red Hat Business Value

In this competition, a classification algorithm is supposed to accurately identify which customers have the most potential business value for Red Hat based on their characteristics and activities. For more information, please visit: https://www.kaggle.com/c/predicting-red-hat-business-value In [2]: import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import warnings […]

## Predictive Analysis , Binary Classification (Cookbook) – 7

This notebook contains my notes for Predictive Analysis on Binary Classification. It acts as a cookbook. It is a continuation from the previous post on assessing performance of Predictive Models. For Deployment  Retrain the model on the full data set and pull out the coefficients corresponding to the best alpha—the one determined to minimize out-of-sample […]

## Predictive Analysis , Binary Classification (Cookbook) – 6

This notebook contains my notes for Predictive Analysis on Binary Classification. It acts as a cookbook. It is a continuation from the previous post on Pearson’s Correlation. This notebook discusses assessing performance of Predictive Models. One of the most used is the misclassification error—that is, the fraction of examples that the function pred() predicts incorrectly. […]

## Predictive Analysis , Binary Classification (Cookbook) – 5

This notebook contains my notes for Predictive Analysis on Binary Classification. It acts as a cookbook. It is a continuation from the previous post on visualizing. This notebook discusses Pearson’s Correlation. Pearson’s Correlation Calculation for Attributes 2 versus 3 and 2 versus 21 In [21]: from math import sqrt #calculate correlations between real-valued attributes dataRow2 = […]

## Predictive Analysis , Binary Classification (Cookbook) – 4

This notebook contains my notes for Predictive Analysis on Binary Classification. It acts as a cookbook. It is a continuation from the previous post on using pandas. Visualizing Parallel Coordinates Plots In [15]: for i in range(208): #assign color based on color based on “M” or “R” labels if rocksVMines.iat[i,60] == “M”: pcolor = “red” else: […]