I recently took part in a challenge by Hostelworld. The challenge proposed by Hostelworld is to build a recommendation engine for users. Recommendations can save… Read More »Exploratory Data Analysis for Hostelworld Challenge
Visualisation is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp… Read More »Visualisation of House Prices
These are mainly notes for myself to win a Data Science competition , but I figured that they might be of interest to some of the… Read More »How to win a Data Science competition ?
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… Read More »The Comprehensive Guide for Feature Engineering
continued from part 3 4. Way to predict survival on Titianic These notes are taken from this link In [2]: import matplotlib.pyplot as plt %matplotlib inline… Read More »4 different ways to predict survival on Titanic – part 4
These are my notes from various blogs to find different ways to predict survival on Titanic using Python-stack. This is continued from part 2 3. Way… Read More »4 different ways to predict survival on Titanic – part 3
These are my notes from various blogs to find different ways to predict survival on Titanic using Python-stack. I am interested to compare how different… Read More »4 different ways to predict survival on Titanic – part 1
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()))… Read More »4 different ways to predict survival on Titanic – part 2
