Submission for Kaggle's Titanic Competition

Submission for Kaggle’s Titanic Competition

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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

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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

Predictive Analysis , Binary Classification (Cookbook) – 7

Posted on 1 CommentPosted in Data Analysis Resources, Machine Learning, Predictive Analysis

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-6

Predictive Analysis , Binary Classification (Cookbook) – 6

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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

Predictive Analysis , Binary Classification (Cookbook) – 5

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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)

Predictive Analysis , Binary Classification (Cookbook) – 4

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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: […]



Predictive Analysis, binary Classification

Predictive Analysis , Binary Classification (Cookbook) – 3

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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 the summary statistics. Using Python Pandas to Read Data In [12]: import pandas as pd from pandas import DataFrame import matplotlib.pyplot as plot %matplotlib inline target_url = (“https://archive.ics.uci.edu/ml/machine-learning-” “databases/undocumented/connectionist-bench/sonar/sonar.all-data”) #read rocks […]



Predictive Analysis, binary Classification

Predictive Analysis , Binary Classification (Cookbook) – 2

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This notebook contains my notes for Predictive Analysis on Binary Classification. It acts as a cookbook. It is a continuation from the previous post. Summary Statistics for Numeric and Categorical Attributes In [6]: import numpy as np #generate summary statistics for column 3 (e.g.) col = 3 colData = [] for row in xList: colData.append(float(row[col])) colArray […]