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course-resources-ml-with-experts-budgets

Further student resources for DrivenData's 'Machine Learning with the Experts: School Budgets' DataCamp course.

data-housing-kaggle-with-r

row data housing processing with R and compare regressions model forward, backward, ols, and lasso

data-housing-with-r

datacamp-statistical-thinking-part-1

Datacamp Review

divactory-competition

higgs-bosson-detection

house-prices-advanced-regression-techniques

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

nomad2018-predicting-transparent-conductors

Predicting Transparent Conductors

predicting-transparent-conductors---kaggle

python

python-xgb-sberbank-kaggle

Housing costs demand a significant investment from both consumers and developers. And when it comes to planning a budget—whether personal or corporate—the last thing anyone needs is uncertainty about one of their biggets expenses. Sberbank, Russia’s oldest and largest bank, helps their customers by making predictions about realty prices so renters, developers, and lenders are more confident when they sign a lease or purchase a building. Although the housing market is relatively stable in Russia, the country’s volatile economy makes forecasting prices as a function of apartment characteristics a unique challenge. Complex interactions between housing features such as number of bedrooms and location are enough to make pricing predictions complicated. Adding an unstable economy to the mix means Sberbank and their customers need more than simple regression models in their arsenal. In this competition, Sberbank is challenging Kagglers to develop algorithms which use a broad spectrum of features to predict realty prices. Competitors will rely on a rich dataset that includes housing data and macroeconomic patterns. An accurate forecasting model will allow Sberbank to provide more certainty to their customers in an uncertain economy.

random-forest

Random Forest for kaggle competition Sberbank Russian Housing Market

row-data-college---kaggle

build model from data college - kaggle

row-data-economy-from-kaggle

sberbank-kaggle-russian---kaggle-linear-model

statistical-modeling-row-data-housing-with-python

test

data housing model

titanic-data-from-kaggle

compare model with Titanic data from Kaggle

titanic-machine-learning-from-disaster

two-scale-bar-plot

you can create plot with two scale numeric