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airfoil-regression-with-deployment

amitthakur4

ann

dash_probalility_density

demo

this is a demo repository

edelwiss_tst

fifa_2019_player_dataset

hackerrank

Solutions to HackerRank problems

housing

img_scrapper

ml_fd_trail

mlproject

neural-networks-and-deep-learning

Code samples for my book "Neural Networks and Deep Learning"

nyse-stock-prediction-and-eda-

Used Ensemble method to predict the moment of the stock . Used Logistic regression , Linear Regression , XG Boost , Random Forest to get the feature importance and then predict from it accordingly . Visulized many data point such as multicollearity , Feature importance , Tsne plots , Distribution of the data using Seaborn , plotly , Matplotlib libraries

nyse_stock_prediction

Nyse stock prediction test using ensemble methods

porto_seguro

Codes for Kaggle's Porto Seguro claim prediction competition. The k-fold model scores .285 Gini on the public lederboard

strategy_analysis

to_mail_or_not-_to-_mail

Direct mailings to a company’s potential customers – “junk mail” to many – can be a very effective way for them to market a product or a service. However, as we all know, much of this junk mail is really of no interest to the people that receive it. Most of it ends up thrown away, not only wasting the money that the company spent on it, but also filling up landfill waste sites or needing to be recycled. If the company had a better understanding of who their potential customers were, they would know more accurately who to send it to, so some of this waste and expense could be reduced.

website

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow