Womens Health Risk Assessment

Modeling Women’s Health Risk Assessment

Posted on Leave a commentPosted in Competition Notes, Machine Learning, scikit-learn

Women’s Health Risk Assessment is a multi-class classification competition for finding an optimized machine learning a solution that allows a young woman (age 15-30 years old) to be accurately categorized for their particular health risk. Based on the category a patient falls within, healthcare providers can offer appropriate education and training programs to help reduce […]

Comprehensive Guide Feature Engineering

The Comprehensive Guide for Feature Engineering

Posted on 2 CommentsPosted in Data Analysis Resources, Machine Learning

Feature Engineering is the art/science of representing data is the best way possible. This is the comprehensive guide for Feature Engineering for myself  but I figured that they might be of interest to some of the blog readers too. Comments on what is written below are most welcome! Good Feature Engineering involves an elegant blend […]

Evaluating Algorithms using Kaggle’s Digit Recognizer Data

Posted on Leave a commentPosted in Kaggle, Machine Learning, scikit-learn

In [1]: import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import warnings warnings.filterwarnings(‘ignore’) In [2]: # importing the train dataset train = pd.read_csv(r’C:\Users\piush\Desktop\Dataset\DigitRecognizer\train.csv’) train.head(10) Out[2]: label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 … pixel774 pixel775 pixel776 pixel777 pixel778 pixel779 pixel780 pixel781 pixel782 pixel783 0 […]

Submission for Kaggle's Titanic Competition

Submission for Kaggle’s Titanic Competition

Posted on 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 […]


AdaBoost (Python 3)

Posted on Leave a commentPosted in Machine Learning

AdaBoost The AdaBoost (adaptive boosting) algorithm was proposed in 1995 by Yoav Freund and Robert Shapire as a general method for generating a strong classifier out of a set of weak classifiers . AdaBoost works even when the classifiers come from a continuum of potential classifiers (such as neural networks, linear discriminants, etc.) AdaBoost Pros: […]