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1. StackOverflow Q&A.ipynb
1. StackOverflow Q&A.ipynb
 
 
10. Stackoverflow.ipynb
10. Stackoverflow.ipynb
 
 
11. Stackoverflow.ipynb
11. Stackoverflow.ipynb
 
 
12. Stackoverflow.ipynb
12. Stackoverflow.ipynb
 
 
13. Stackoverflow.ipynb
13. Stackoverflow.ipynb
 
 
2. StackOverflow Q&A - Copy.ipynb
2. StackOverflow Q&A - Copy.ipynb
 
 
2. StackOverflow Q&A.ipynb
2. StackOverflow Q&A.ipynb
 
 
2020 Stack Overflow survey.ipynb
2020 Stack Overflow survey.ipynb
 
 
3. Stackoverflow.ipynb
3. Stackoverflow.ipynb
 
 
31AEC98E-397F-40B3-842C-E1C178730543.jpeg
31AEC98E-397F-40B3-842C-E1C178730543.jpeg
 
 
4. Stackoverflow.ipynb
4. Stackoverflow.ipynb
 
 
5 times sin 1.5 times x plus pi divided 4.ipynb
5 times sin 1.5 times x plus pi divided 4.ipynb
 
 
5. Stackoverflow.ipynb
5. Stackoverflow.ipynb
 
 
6. Stack Overflow.ipynb
6. Stack Overflow.ipynb
 
 
7. Stackoverflow.ipynb
7. Stackoverflow.ipynb
 
 
8. Stackoverflow - Copy.ipynb
8. Stackoverflow - Copy.ipynb
 
 
8. Stackoverflow.ipynb
8. Stackoverflow.ipynb
 
 
9. Stackoverflow.ipynb
9. Stackoverflow.ipynb
 
 
AAPL.csv
AAPL.csv
 
 
AMZN.csv
AMZN.csv
 
 
ANSUR 2 - Army - Dimension reduction.ipynb
ANSUR 2 - Army - Dimension reduction.ipynb
 
 
ANSUR_2_M.csv
ANSUR_2_M.csv
 
 
Abalone Age Prediction.ipynb
Abalone Age Prediction.ipynb
 
 
Accident Prediction.ipynb
Accident Prediction.ipynb
 
 
Admission_Predict.csv
Admission_Predict.csv
 
 
Airline Comfort Ratings.ipynb
Airline Comfort Ratings.ipynb
 
 
Alice in Wonderland - Summarize.ipynb
Alice in Wonderland - Summarize.ipynb
 
 
Allstate dividends.ipynb
Allstate dividends.ipynb
 
 
BHP.csv
BHP.csv
 
 
BHP_Dividend.csv
BHP_Dividend.csv
 
 
Bangladesh well switch (GLM).ipynb
Bangladesh well switch (GLM).ipynb
 
 
Baseball stats.ipynb
Baseball stats.ipynb
 
 
CCI.csv
CCI.csv
 
 
CC_data
CC_data
 
 
CGNX.csv
CGNX.csv
 
 
CNN image classification.ipynb
CNN image classification.ipynb
 
 
CNN Cats and Dogs recogition.ipynb
CNN Cats and Dogs recogition.ipynb
 
 
CO2 timeseries analysis.ipynb
CO2 timeseries analysis.ipynb
 
 
CSCO.csv
CSCO.csv
 
 
CSCO_dividends.csv
CSCO_dividends.csv
 
 
CUBE.csv
CUBE.csv
 
 
CUBE_Dividend.csv
CUBE_Dividend.csv
 
 
Candy Power Ranking.ipynb
Candy Power Ranking.ipynb
 
 
Chicago Crimes Index.ipynb
Chicago Crimes Index.ipynb
 
 
Chicago%20Crimes%20Index.ipynb
Chicago%20Crimes%20Index.ipynb
 
 
Chicago_IUCR__Codes.csv
Chicago_IUCR__Codes.csv
 
 
College Graduate Admission SVC.ipynb
College Graduate Admission SVC.ipynb
 
 
Counter.txt
Counter.txt
 
 
Crab Weight~Sat OLS and GLM.ipynb
Crab Weight~Sat OLS and GLM.ipynb
 
 
Credit Card Approval Logistic Regression.ipynb
Credit Card Approval Logistic Regression.ipynb
 
 
Credit Card Default usings Keras Default Prediction.ipynb
Credit Card Default usings Keras Default Prediction.ipynb
 
 
Credit Card Defaults - hyperparameter.ipynb
Credit Card Defaults - hyperparameter.ipynb
 
 
Credit Loan Risk - Copy.ipynb
Credit Loan Risk - Copy.ipynb
 
 
Credit Loan Risk .ipynb
Credit Loan Risk .ipynb
 
 
Crime analysis (SAS).ipynb
Crime analysis (SAS).ipynb
 
 
DIS.csv
DIS.csv
 
 
DPZ.csv
DPZ.csv
 
 
DUK.csv
DUK.csv
 
 
DUK_dividend.csv
DUK_dividend.csv
 
 
Datetime.ipynb
Datetime.ipynb
 
 
Deep Learning - blood donations.ipynb
Deep Learning - blood donations.ipynb
 
 
Deep learning and matrix determinants.ipynb
Deep learning and matrix determinants.ipynb
 
 
Deep learning with Keras using resnet50.ipynb
Deep learning with Keras using resnet50.ipynb
 
 
Dense Network Sin segments.ipynb
Dense Network Sin segments.ipynb
 
 
Dimensions reduction PCA.txt
Dimensions reduction PCA.txt
 
 
Disease Prediction Dense Layer.ipynb
Disease Prediction Dense Layer.ipynb
 
 
Drug Text Analysis.ipynb
Drug Text Analysis.ipynb
 
 
Duke Energy.ipynb
Duke Energy.ipynb
 
 
ECG LSTM Prediction.ipynb
ECG LSTM Prediction.ipynb
 
 
ECG5000_TRAIN.arff
ECG5000_TRAIN.arff
 
 
EDC4EFC6-6E62-4CF8-988A-684BFE1E9619.jpeg
EDC4EFC6-6E62-4CF8-988A-684BFE1E9619.jpeg
 
 
EQIX.csv
EQIX.csv
 
 
EQIX_Dividend.csv
EQIX_Dividend.csv
 
 
ESI-WSR - 9_18_2020.numbers
ESI-WSR - 9_18_2020.numbers
 
 
EVs_data_base.csv
EVs_data_base.csv
 
 
EXR.csv
EXR.csv
 
 
EXR_Dividend.csv
EXR_Dividend.csv
 
 
Earthquakes 5 plus.ipynb
Earthquakes 5 plus.ipynb
 
 
Electric Vehicle trends.ipynb
Electric Vehicle trends.ipynb
 
 
Equinix data center.ipynb
Equinix data center.ipynb
 
 
Face recognition - edge detection.ipynb
Face recognition - edge detection.ipynb
 
 
GSS2018.csv
GSS2018.csv
 
 
General Social Survey.ipynb
General Social Survey.ipynb
 
 
Google timeseries.ipynb
Google timeseries.ipynb
 
 
Happiness and Depression LR multi-label.ipynb
Happiness and Depression LR multi-label.ipynb
 
 
Happiness and Depression Logistic Regression.ipynb
Happiness and Depression Logistic Regression.ipynb
 
 
Healthcare costs by DRG Codes.ipynb
Healthcare costs by DRG Codes.ipynb
 
 
IMG_3593.JPG
IMG_3593.JPG
 
 
Icecream sales revenue.ipynb
Icecream sales revenue.ipynb
 
 
Idaho Job Growth Analysis.ipynb
Idaho Job Growth Analysis.ipynb
 
 
Idaho Weather in Sep 2020.ipynb
Idaho Weather in Sep 2020.ipynb
 
 
ImageDataGenerator.txt
ImageDataGenerator.txt
 
 
Importing Data from the Web.ipynb
Importing Data from the Web.ipynb
 
 
Indian Liver Patient .ipynb
Indian Liver Patient .ipynb
 
 
KC Housing Tensorflow.ipynb
KC Housing Tensorflow.ipynb
 
 
Keras shampoo sales conv1d.ipynb
Keras shampoo sales conv1d.ipynb
 
 
KidCreative.csv
KidCreative.csv
 
 
LSTM Hungarian Chickpox.ipynb
LSTM Hungarian Chickpox.ipynb
 
 
LSTM Netflix.ipynb
LSTM Netflix.ipynb
 
 
LSTM Predict demand of bike share.ipynb
LSTM Predict demand of bike share.ipynb
 
 

Repository files navigation

python-deep-learning

python deep learning notebooks. Open the juptyer projects in a new window to avoid the "something is wrong" error loading from github.

  • market basket analysis: support, conviction, leverage, confidence, lift

https://github.com/dnishimoto/python-deep-learning/blob/master/grocery%20market%20basket%20analysis.ipynb

  • abalone age prediction with 9 features lstm

https://github.com/dnishimoto/python-deep-learning/blob/master/Abalone%20Age%20Prediction.ipynb

  • nets group classification using lstm with a confusion matrix for measuring performance

https://github.com/dnishimoto/python-deep-learning/blob/master/Nets%20Group%20Classification%20LSTM.ipynb

  • Curve fitting the dividend earnings of AllState

https://github.com/dnishimoto/python-deep-learning/blob/master/Allstate%20dividends.ipynb

  • Dense layer network multiple input and output disease prediction

https://github.com/dnishimoto/python-deep-learning/blob/master/Disease%20Prediction%20Dense%20Layer.ipynb

  • Dense layer network to predict concatenated sin segments

https://github.com/dnishimoto/python-deep-learning/blob/master/Dense%20Network%20Sin%20segments.ipynb

  • ECG dense layer predictor

https://github.com/dnishimoto/python-deep-learning/blob/master/ECG%20LSTM%20Prediction.ipynb

  • Bidirectional lstm classifier for equinex - risk management

https://github.com/dnishimoto/python-deep-learning/blob/master/Equinix%20data%20center.ipynb

  • Extraspace vs rio

https://github.com/dnishimoto/python-deep-learning/blob/master/RIO%20vs%20BHP%20Uranium.ipynb

  • Duke Energy portfolio strategy

https://github.com/dnishimoto/python-deep-learning/blob/master/Duke%20Energy.ipynb

  • LSTM binary classifier

https://github.com/dnishimoto/python-deep-learning/blob/master/LSTM%20label%20binary%20classification.ipynb

  • analysis of apply stock - linear regression

https://github.com/dnishimoto/python-deep-learning/blob/master/stock%20apple%20vs%20vmware.ipynb

  • Airline customer satisification - Decision boundary analysis

https://github.com/dnishimoto/python-deep-learning/blob/master/airline%20logistic%20regression.ipynb

  • Text summerization with GRU neural machine translation

https://github.com/dnishimoto/python-deep-learning/blob/master/Text%20Summerization%20GRU.ipynb

  • Computer vision using Conv2D with Keras

https://github.com/dnishimoto/python-deep-learning/blob/master/CNN%20Cats%20and%20Dogs%20recogition.ipynb

  • Shampoo sales using keras conv1d

https://github.com/dnishimoto/python-deep-learning/blob/master/Keras%20shampoo%20sales%20conv1d.ipynb

  • I added weight training with random forest for 56 features

https://github.com/dnishimoto/python-deep-learning/blob/master/Weight%20Lifting%20Random%20Forest.ipynb

  • I added deep learn dense network for mnist

https://github.com/dnishimoto/python-deep-learning/blob/master/deep%20learning%20mnist.ipynb

  • I added an Text keras lstm network for multi classification labels

https://github.com/dnishimoto/python-deep-learning/blob/master/Text%20LSTM%20multi-classification.ipynb

  • I added multiple variable lstm prediction of bike share

https://github.com/dnishimoto/python-deep-learning/blob/master/LSTM%20Predict%20demand%20of%20bike%20share.ipynb

  • I added Credit risk of default (logistic regression, xgboost, keras)

https://github.com/dnishimoto/python-deep-learning/blob/master/Credit%20Loan%20Risk%20.ipynb

  • I added Electric vehicle trends

https://github.com/dnishimoto/python-deep-learning/blob/master/Electric%20Vehicle%20trends.ipynb

  • I added kc housing price prediction using keras network

https://github.com/dnishimoto/python-deep-learning/blob/master/deep%20learning%20housing%20price.ipynb

  • I added allstate dividend payments using cumsum

https://github.com/dnishimoto/python-deep-learning/blob/master/Allstate%20dividends.ipynb

  • I add meat consumption trends time series analysis

https://github.com/dnishimoto/python-deep-learning/blob/master/Meat%20volume%20timeseries.ipynb

  • I added co2 time series analysis

https://github.com/dnishimoto/python-deep-learning/blob/master/CO2%20timeseries%20analysis.ipynb

  • I added probability analysis of class room size for different schools in 1938

https://github.com/dnishimoto/python-deep-learning/blob/master/School%20Size%20Probablity.ipynb

  • I added Earthquake 5+ analysis

https://github.com/dnishimoto/python-deep-learning/blob/master/Earthquakes%205%20plus.ipynb

  • I added Google timeseries analysis

https://github.com/dnishimoto/python-deep-learning/blob/master/Google%20timeseries.ipynb

  • I added shampoo sales using rolling window and pytorch conv1d

https://github.com/dnishimoto/python-deep-learning/blob/master/Pytorch%20Shampoo%20sales%20conv1d.ipynb

  • I added college admittance for Graduate school using support vector machine

https://github.com/dnishimoto/python-deep-learning/blob/master/College%20Graduate%20Admission%20SVC.ipynb

  • I added a Happiness and Depression logistic Regression

https://github.com/dnishimoto/python-deep-learning/blob/master/Happiness%20and%20Depression%20Logistic%20Regression.ipynb

  • I added a Random Forest Classifer for playing tennis based on the weather

https://github.com/dnishimoto/python-deep-learning/blob/master/Random%20Forest%20Tennis.ipynb

  • I added Olympic history using heatmap

https://github.com/dnishimoto/python-deep-learning/blob/master/Olympics%20events.ipynb

  • I added liver disease analysis using glm

https://github.com/dnishimoto/python-deep-learning/blob/master/Indian%20Liver%20Patient%20.ipynb

  • I added crab satelite ~ weight and width ols and glm

https://github.com/dnishimoto/python-deep-learning/blob/master/Crab%20Weight~Sat%20OLS%20and%20GLM.ipynb

  • I added Bangladesh well switch using ols and glm

https://github.com/dnishimoto/python-deep-learning/blob/master/Bangladesh%20well%20switch%20(GLM).ipynb

  • I added baseball stats using a heatmap to analyze the logistic regression predictions

https://github.com/dnishimoto/python-deep-learning/blob/master/Baseball%20stats.ipynb

  • I added an airline comfort rating a logistic regression pipeline

https://github.com/dnishimoto/python-deep-learning/blob/master/Airline%20Comfort%20Ratings.ipynb

  • I added a credit card approval using a logistic regression pipeline

https://github.com/dnishimoto/python-deep-learning/blob/master/Credit%20Card%20Approval%20Logistic%20Regression.ipynb

  • i added face recognition and edge detection using open cv

https://github.com/dnishimoto/python-deep-learning/blob/master/Face%20recognition%20-%20edge%20detection.ipynb

  • I added hyper parameter tuning for credit card default and built a mlpclassifier to predict

https://github.com/dnishimoto/python-deep-learning/blob/master/Credit%20Card%20Defaults%20-%20hyperparameter.ipynb

  • I added candy power ranking using keras linear predictor

https://github.com/dnishimoto/python-deep-learning/blob/master/Candy%20Power%20Ranking.ipynb

  • I added credit card default prediction using keras

https://github.com/dnishimoto/python-deep-learning/blob/master/Credit%20Card%20Default%20usings%20Keras%20Default%20Prediction.ipynb

  • I added multinomialnb and kNearestNeighbor UFO sightings predictor

https://github.com/dnishimoto/python-deep-learning/blob/master/UFO%20.ipynb

  • I added passenger prediction using LSTM one day back prediction

https://github.com/dnishimoto/python-deep-learning/blob/master/LSTM%20passenger%201%20day%20back%20prediction.ipynb

  • I added the pima diabetes dimension reduction

https://github.com/dnishimoto/python-deep-learning/blob/master/Pimas%20Diabetes%20Dimension%20Reduction.ipynb

  • I added Army AUNR dimensional reduction

https://github.com/dnishimoto/python-deep-learning/blob/master/ANSUR%202%20-%20Army%20-%20Dimension%20reduction.ipynb

  • I added Trump tweets

https://github.com/dnishimoto/python-deep-learning/blob/master/Trump%20tweets.ipynb

  • I added super bowl prediction creating a probable profile of a winner

https://github.com/dnishimoto/python-deep-learning/blob/master/Superbowel.ipynb

  • I added general social survey

https://github.com/dnishimoto/python-deep-learning/blob/master/General%20Social%20Survey.ipynb

  • I added baby birthweight predictions

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20Baby%20BirthWeight.ipynb

  • I added pct change for netflix and domino then feed it into a lstm for a future prediction

*https://github.com/dnishimoto/python-deep-learning/blob/master/LSTM%20Netflix.ipynb

  • I added auc the stepwise refinement to find multiple variables for the model using customer churn data

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20Customer%20Churn%20-%20stepwise%20variables%20auc.ipynb

  • I added medicare healthcare by drg

https://github.com/dnishimoto/python-deep-learning/blob/master/Healthcare%20%20costs%20by%20DRG%20Codes.ipynb

  • I added linear model of restaurant tips

https://github.com/dnishimoto/python-deep-learning/blob/master/linear%20model%20-%20restaurant%20tips.ipynb

  • I added boise weather for sept 2020, temperature and humidity affected wind speeds

https://github.com/dnishimoto/python-deep-learning/blob/master/Idaho%20Weather%20in%20Sep%202020.ipynb

  • I added a chicago crime sas file - crime trends from 2001 to 2016 trended down

https://github.com/dnishimoto/python-deep-learning/blob/master/Crime%20analysis%20(SAS).ipynb

  • I added a search for the best movies

https://github.com/dnishimoto/python-deep-learning/blob/master/Movies%202013.ipynb

  • I added visualization of nitrogen dioxide levels in Utah

https://github.com/dnishimoto/python-deep-learning/blob/master/Visualizing%20Nitrogen%20Dioxide.ipynb

  • I am hypothesis testing finch beak size changes between 1975 and 2012

https://github.com/dnishimoto/python-deep-learning/blob/master/statistics%20hypothesis%20testing%20scandens%20and%20fortis%20beak%20size.ipynb

I am using python to visualize data and learn from it.

  • I added an XGBoost predictor for idaho housing prices

https://github.com/dnishimoto/python-deep-learning/blob/master/XGBoost%20Idaho%20housing%20prices.ipynb

  • I added a bayensian predictor for covid cases and deaths (keras)

https://github.com/dnishimoto/python-deep-learning/blob/master/covid%20prediction%20bayesian.ipynb

  • I added stackoverflow programmer features

https://github.com/dnishimoto/python-deep-learning/blob/master/2020%20Stack%20Overflow%20survey.ipynb

  • I added logistic regression telco customer churn prediction

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20Telco%20customer%20churn.ipynb

  • I added XgBoost classifier for the iris Data. I included the notes to installing xgboost.

https://github.com/dnishimoto/python-deep-learning/blob/master/XGBoost%20Iris.ipynb

  • I added Remaining payments (geometric progression)

https://github.com/dnishimoto/python-deep-learning/blob/master/Remaining%20Payment.ipynb

  • I added Reinforcement Learning Balancing pole

https://github.com/dnishimoto/python-deep-learning/blob/master/Reinforcement%20Learning%20Balancing%20Pole.ipynb

  • I added Reinforcement Learning Tic Tac Toe

https://github.com/dnishimoto/python-deep-learning/blob/master/Reinforcement%20Learning%20Tic%20Tac%20Toe.ipynb

  • I added Reinforcement Learning NChain

https://github.com/dnishimoto/python-deep-learning/blob/master/Reinforced%20Learning%20NChain.ipynb

  • I added LSTM Input_shape (need to fix output)

https://github.com/dnishimoto/python-deep-learning/blob/master/LSTM.ipynb

  • I added a logistic regression binary classifier with lasso called (Logistic Regression predicting magazine subscription)

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20predicting%20magazine%20subscription.ipynb

  • I added a logistic regression of multiple classes using cross_entropy and softmax called Logistic Regression prediction of Iris.ipynb

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20prediction%20of%20Iris.ipynb

  • I added a logistic regression binary prediction of whether or not a person will open an account (Logistic Regression open account prediction.ipynb)

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20open%20account%20prediction.ipynb

  • I added a logistic regression binary prediction for credit card fraud

https://github.com/dnishimoto/python-deep-learning/blob/master/Logistic%20Regression%20binary%20classification%20of%20credit%20card%20fraud.ipynb

  • I added a deep learning stocastic descent to solve a linear matrix X, Y, Z set of equations.

https://github.com/dnishimoto/python-deep-learning/blob/master/Deep%20learning%20and%20matrix%20determinants.ipynb

  • I added NMF feature reduction to analyze words in a document to build a better search engine using ensemblies

https://github.com/dnishimoto/python-deep-learning/blob/master/NMF%20Search%20Engine.ipynb

https://github.com/dnishimoto/python-deep-learning/blob/master/NMF%20feature%20reduction.ipynb

  • I added Michelson vs Newcomb speed of light hypothesis testing code

https://github.com/dnishimoto/python-deep-learning/blob/master/Michaelson%20speed%20of%20light%20-%20hypothesis%20testing.ipynb

  • I feature analyze and extract using K-means cluster on the Iris classifier using K-means cluster

https://github.com/dnishimoto/python-deep-learning/blob/master/iris-classifier%20k-means%20clustering.ipynb

  • I modeled the statistical distribution of the Junk bond market and determine it is not a normal distribution

https://github.com/dnishimoto/python-deep-learning/blob/master/statistics%20poisson%20junk%20bond%20market.ipynb

  • I modeled binomal and poisson distributions in python (modeling distributions guassian curves)

https://github.com/dnishimoto/python-deep-learning/blob/master/statistics%20part%201.ipynb

  • I built a neural network for predict covid deaths

https://github.com/dnishimoto/python-deep-learning/blob/master/neural%20network.ipynb

  • I modeled the trigometric function 5 times sin 1.5 times x plus pi divided 4

https://github.com/dnishimoto/python-deep-learning/blob/master/5%20times%20sin%201.5%20times%20x%20plus%20pi%20divided%204.ipynb

  • I built a convolution neural net to learn an apple, banana, and orange image (deep learning - cnn - recognizing a list of images)

https://github.com/dnishimoto/python-deep-learning/blob/master/CNN%20%20image%20classification.ipynb

  • I built a deep learning network to predict team scores

https://github.com/dnishimoto/python-deep-learning/blob/master/deep%20learning%20addition.ipynb

  • I built a deep learning stocastic descent model to learn a trigonomy function (r=1-sin theta)

https://github.com/dnishimoto/python-deep-learning/blob/master/r%3D1-sin%20theta.ipynb

  • I built a deep learning linear classifier to identify cultivator (deep learning intro with keras - linear classification.ipynb)

https://github.com/dnishimoto/python-deep-learning/blob/master/deep%20learning%20with%20keras%20-%20binary%20classification.ipynb

  • I built a deep learning classifier to predict the flower types based on features

https://github.com/dnishimoto/python-deep-learning/blob/master/deep%20learning%20iris.ipynb

  • I built a deep learning regressor to track the perimeters of a circle (deep learning circle chasing.ipynb)

https://github.com/dnishimoto/python-deep-learning/blob/master/deep%20learning%20circle%20chasing.ipynb

  • I applied gradient boost to predict death trends for three states (time series covid 19.ipynb)

https://github.com/dnishimoto/python-deep-learning/blob/master/time%20series%20covid%2019.ipynb

  • I applied linear regression and gradient boost to predict the morality rate trends (time series with machine learning.ipynb)

https://github.com/dnishimoto/python-deep-learning/blob/master/time%20series%20with%20machine%20learning.ipynb

  • I applied deep learning to predict mpg based in hp, weight, displacement, and gears (linear regressor mpg and horse power.ipynb)

https://github.com/dnishimoto/python-deep-learning/blob/master/linear%20regressor%20mpg%20and%20horse%20power.ipynb

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