• Stars
    star
    1
  • Language
    Jupyter Notebook
  • Created over 6 years ago
  • Updated over 6 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

More Repositories

1

ActiveUserPrediction

predict app users that will be active in some period of time given log information of the users
Python
46
star
2

lstm_stock_price_prediction

LSTM神经网络预测沪深300指数及其涨跌
Jupyter Notebook
40
star
3

dnn_house_price_prediction_scratch

build a deep neural network from scratch for boston house price prediction
Jupyter Notebook
19
star
4

CNN_from_scratch

从零开始无框架python实现卷积神经网络
Jupyter Notebook
12
star
5

lstm_next_sequence_prediction

implement recurrent neural network and long short-term memory network from scratch without frameworks
Jupyter Notebook
4
star
6

Stacking

this is a stacking classifier with built-in parameter-tuning and feature selection featues
Python
3
star
7

SantanderProductRecommendation

In this competition, you are provided with 1.5 years of customers behavior data from Santander bank to predict what new products customers will purchase. The data starts at 2015-01-28 and has monthly records of products a customer has, such as “credit card”, “savings account”, etc. You will predict what additional products a customer will get in the last month, 2016-06-28, in addition to what they already have at 2016-05-28. These products are the columns named: ind_(xyz)_ult1, which are the columns #25 – #48 in the training data. You will predict what a customer will buy in addition to what they already had at 2016-05-28. So we have to select which among the 24 products a user will buy in the 2016-06-28 with respect to what he already has in the previous month.
Python
1
star