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 […]
Visualisation of House Prices
Visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. This visualization of house prices is for the Kaggle dataset. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this […]
4 different ways to predict survival on Titanic
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 people have attempted the kaggle competition. I am going to compare and contrast different analysis to find similarity and difference in approaches to predict survival on Titanic. This Notebook […]
Evaluating Algorithms using MNIST
This post is evaluating Aagorithms using MNIST 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:UserspiushDesktopDatasetDigitRecognizertrain.csv’) train.head(10) Out[2]: label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 pixel8 … pixel774 pixel775 pixel776 pixel777 […]
Submission for Kaggle’s Titanic Competition
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:UserspiushDesktopDatasetTitanictrain.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 […]