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Pima-indians-diabetes.csv Download

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# The Pandas groupby method computes the distribution of one feature# with respect to the others# We see 8 histograms distrubuted against a negative diabetes chck# and other 8 histograms with distribution against a positive diabetes checkdata.groupby('class').hist(figsize=(8,8), xlabelsize=7, ylabelsize=7) class0 [[Axes(0.125,0.684722;0.215278x0.215278), Axes...1 [[Axes(0.125,0.684722;0.215278x0.215278), Axes...dtype: object sm = scatter_matrix(data, alpha=0.2, figsize=(7.5, 7.5), diagonal='kde')[plt.setp(item.yaxis.get_majorticklabels(), 'size', 6) for item in sm.ravel()][plt.setp(item.xaxis.get_majorticklabels(), 'size', 6) for item in sm.ravel()]plt.tight_layout(h_pad=0.15, w_pad=0.15) All the above Pandas coding and statistical charts are nice and helpfulbut in most practical situations are useless for coming up with an algorithmfor predicting if a person is likely to have diabetes based on his 8 medical records. In recent years, deep neural networks has been suggested as an effective technique for solvingsuch problem, and so far has shown to be successful in many areas.We will try to build a simple neural network for predicting if a person has diabetes (0 or 1),based of the 8 features in his record.This is the full data except the class column!We want to be able to predict the class (positive or negative diabetes check)from the 8 features (as input) # Let's first extract the first 8 features from our data (from the 9 we have)# We want to be able to predict the class (positive or negative diabetes check)X = data.ix[:,0:8] 0 11 02 13 04 15 06 17 08 19 1Name: class, dtype: int64 A sequential neural-network in Keras consists of a sequence of layers,starting from the inputs layer up to the output layer(also known as Feedforward Neural Network).The number and breadth of Continue reading >>

Linear Classification With Slp

Linear Classification With Slp

This tutorial demonstrates how to run a simple single layer perceptron (backpropagation network) for the two-class classification of eight-dimensional data (with normalization). We will be using the Pima Indians Dataset, which can be obtained from UCI Machine Learning Repository . This dataset contains 768 entries, each having eight real-valued features plus a binary class variable (0 or 1). For the sake of demonstration, the plan is to use one of the simplest possible supervised learning algorithms (single layer perceptron) to classify each dataset entry to one of the two classes according to its feature values. It has to be pointed out that the dataset is not linearly separable, while the SLP algorithm tries to find a linear decision boundary indeed; hence it is a priori impossible to find a perfect set of parameters of a particular model and the SSE score will always be non-zero. Standard Backpropagation programs in emergent assume that the input data is stored in the "Input" and "Output" columns, and for our purposes we need each Input cell to contain a matrix of real values. It is possible to import the data form a CSV file, but emergent also assumes that the CSV file has a certain format, namely, the first line of the file should specify how the data is to be stored in the matrix cell: Name,"Input_0","Input_1","Input_2","Input_3","Input_4","Input_5","Input_6","Input_7","Output_0" Additionally, the first entry of each line is assumed to contain the "line's name", and if there's no name, each line has to start with a pair of quotes. "",6,148,72,35,0,33.6,0.627,50,1"",1,85,66,29,0,26.6,0.351,31,0... The task of adding the empty name can of course be automated with the use of sed-like scripts or editors such as vim that allow to repeat a particular operation n times. Continue reading >>

Data Analysis And Visualization In Python (pima Indians Diabetes Data Set)

Data Analysis And Visualization In Python (pima Indians Diabetes Data Set)

Data analysis and visualization in Python (Pima Indians diabetes data set) in data-visualization - on October 14, 2017 - No comments Today I am going to perform data analysis for a very common data set i.e. Pima Indians Diabetes data set . You can download the data from here . I'll not give the meta information here in detail because it is given exclusively here . So it is recommended for all who want to understand the complete data analysis that what kind of data we are working with. In our analysis we'll be using two major Python libraries to do analysis and visualization. Pandas is for data processing, cleaning and analysis whereas Matplotlib is for visualization of our data. We start by calculating the descriptives which allows us to see the data summary. One of the reaasons why initial descriptives are important because we see the data summary and do preprocessing again if we find any potential outliers and do normalization if there is a significant difference of scales between the variables. Normalization makes our analysis easier specially when we try to visualize data. If you are using pandas, there is a very simple of calculating the descriptive statistics We see that 'df_temp.describe()' does all the calculations. We drop the binary variable 'diabetes?' in df_temp because its descriptive statistics are calculated by binomial distribution formula and the way pandas caluclated the descriptives will not give any insights. Rest of the other work is just to load the data and mapping the columns and meta information. The df.describe() method will return the following output. Here I am not going to spend much time in interprating the the results because it is very basic and you can find various sources to see the interpratation of these metrics. The idea here is to Continue reading >>

Simple Data Science

Simple Data Science

How do ATMs in general work is a great question to ask? Well banks at times prefer not to manage their ATMs as it involves a lot of overhead such as transportation of cash, maintenance of ATM machines, rent and most importantly security. In order to avoid this over head a lot of banks outsource this task. The companies who overtake this responsibility , make their revenue based on every transaction made. Say for every non cash transaction from the ATM managed by them they get x$ and for every cash transaction they get y$ where y>x . So why do we need to predict cash ?? well these companies rent a place, put their ATMs at that place keep a service engineer to maintain that machine and pump enough security, but where they need to be careful is interest cost. What interest cost? lets say for todays date I decided to keep 100$ in my ATM, I would borrow this money from a bank, to whom I would pay interest every day for the cash that is not withdrawn by the customers. The obvious solution for this is to load ATMs with the smallest amount of money possible, however this leads to two problems, First is loss of revenue from a potential customer, and second one is brand loss, and brand loss is very bad. That means we do not want to load to much money to avoid paying interest cost on idle money, and neither do we want to put to less in order to avoid loss of revenue and brand loss. In order to find this perfect balance we need to create a forecasting model on how much money to load in the ATMs, in order to make the business profitable. One underlying constraint is transportation. We cannot transport and load money in ATMs on a daily basis to avoid transportation costs, that is why transportation will happen only once in two to three days. The best thing to do whenstarting somethi Continue reading >>

Introduction To Machine Learning With Python And Scikit-learn

Introduction To Machine Learning With Python And Scikit-learn

Introduction to Machine Learning with Python and Scikit-Learn My name is Alex. I deal with machine learning and web graphs analysis (mostly in theory). I also work on the development of Big Data products for one of the mobile operators in Russia. Its the first time I write a post, so please, dont judge me too harshly. Nowadays, a lot of people want to develop efficient algorithms and take part in machine learning competitions. So they come to me and ask: Where to start?. Some time ago, I led the development of Big Data tools for the analysis of media and social networks in one of the institutions of the Government of the Russian Federation. I still have some documentation my team used, and Id like to share it with you. It is assumed that the reader has a good knowledge of mathematics and machine learning (my team mostly consisted of MIPT (the Moscow Institute of Physics and Technology) and the School of Data Analysis graduates). Actually, it has been the introduction to Data Science. This science has become quite popular recently. Competitions in machine learning are increasingly held (for example, Kaggle , TudedIT ), and their budget is often quite considerable. The most common tools for a Data Scientist today are R and Python. Each tool has its pros and cons, but Python wins recently in all respects (this is just imho, I use both R and Python though). This happened after there had appeared a very well documented Scikit-Learn library that contains a great number of machine learning algorithms. Please note that we will focus on Machine Learning algorithms in the article. It is usually better to perform the primary data analysis by means of the Pandas package that is quite simple to deal with on your own. So, lets focus on implementation. For definiteness, we assume tha Continue reading >>

[]save And Load Your Keras Deep Learning Models -

[]save And Load Your Keras Deep Learning Models -

Each example will also demonstrate saving and loading your model weights to HDF5 formatted files. The examples will use the same simple network trained on the Pima Indians onset of diabetes binary classification dataset. This is a small dataset that contains all numerical data and is easy to work with. You can download this dataset and place it in your working directory with the filename pima-indians-diabetes.csv. JSON is a simple file format for describing data hierarchically. Keras provides the ability to describe any model using JSON format with a to_json() function. This can be saved to file and later loaded via the model_from_json() function that will create a new model from the JSON specification. The weights are saved directly from the model using the save_weights() function and later loaded using the symmetrical load_weights() function. The example below trains and evaluates a simple model on the Pima Indians dataset. The model is then converted to JSON format and written to model.json in the local directory. The network weights are written to model.h5 in the local directory. The model and weight data is loaded from the saved files and a new model is created. It is important to compile the loaded model before it is used. This is so that predictions made using the model can use the appropriate efficient computation from the Keras backend. The model is evaluated in the same way printing the same evaluation score. # MLP for Pima Indians Dataset Serialize to JSON and HDF5from keras.models import Sequentialfrom keras.layers import Densefrom keras.models import model_from_jsonimport numpyimport os# fix random seed for reproducibilitynumpy.random.seed(7)# load pima indians datasetdataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")# split into input (X) Continue reading >>

Andrea Grandi - Machine Learning

Andrea Grandi - Machine Learning

Sat 14 April 2018| in Development | tags: Machine Learning Python scikit-learn tutorial The Pima are a group of Native Americans living in Arizona. A genetic predisposition allowed this group to survive normally to a diet poor of carbohydrates for years. In the recent years, because of a sudden shift from traditional agricultural crops to processed foods, together with a decline in physical activity, made them develop the highest prevalence of type 2 diabetes and for this reason they have been subject of many studies. The dataset includes data from 768 women with 8 characteristics, in particular: Plasma glucose concentration a 2 hours in an oral glucose tolerance test Body mass index (weight in kg/(height in m)^2) The last column of the dataset indicates if the person has been diagnosed with diabetes (1) or not (0) The original dataset is available at UCI Machine Learning Repository and can be downloaded from this address: The type of dataset and problem is a classic supervised binary classification. Given a number of elements all with certain characteristics (features), we want to build a machine learning model to identify people affected by type 2 diabetes. To solve the problem we will have to analyse the data, do any required transformation and normalisation, apply a machine learning algorithm, train a model, check the performance of the trained model and iterate with other algorithms until we find the most performant for our type of dataset. # We import the libraries needed to read the datasetimport osimport pandas as pdimport numpy as np # We placed the dataset under datasets/ sub folderDATASET_PATH = 'datasets/' # We read the data from the CSV filedata_path = os.path.join(DATASET_PATH, 'pima-indians-diabetes.csv')dataset = pd.read_csv(data_path, header=None)# Bec Continue reading >>

Use The Sample Datasets In Azure Machine Learning Studio

Use The Sample Datasets In Azure Machine Learning Studio

Adult Census Income Binary Classification dataset A subset of the 1994 Census database, using working adults over the age of 16 with an adjusted income index of > 100. Usage: Classify people using demographics to predict whether a person earns over 50K a year. Related Research: Kohavi, R., Becker, B., (1996). UCI Machine Learning Repository . Irvine, CA: University of California, School of Information and Computer Science This dataset contains one row for each U.S. airport, providing the airport ID number and name along with the location city and state. Information about automobiles by make and model, including the price, features such as the number of cylinders and MPG, as well as an insurance risk score. The risk score is initially associated with auto price. It is then adjusted for actual risk in a process known to actuaries as symboling. A value of +3 indicates that the auto is risky, and a value of -3 that it is probably safe. Usage: Predict the risk score by features, using regression or multivariate classification. Related Research: Schlimmer, J.C. (1987). UCI Machine Learning Repository . Irvine, CA: University of California, School of Information and Computer Science UCI Bike Rental dataset that is based on real data from Capital Bikeshare company that maintains a bike rental network in Washington DC. The dataset has one row for each hour of each day in 2011 and 2012, for a total of 17,379 rows. The range of hourly bike rentals is from 1 to 977. Publicly available image file converted to CSV data. The code for converting the image is provided in the Color quantization using K-Means clustering model detail page. A subset of data from the blood donor database of the Blood Transfusion Service Center of Hsin-Chu City, Taiwan. Donor data includes the months since l Continue reading >>

Understanding K-nearest Neighbours With The Pima Indians Diabetes Dataset

Understanding K-nearest Neighbours With The Pima Indians Diabetes Dataset

Understanding k-Nearest Neighbours with the PIMA Indians Diabetes dataset K nearest neighbors (kNN) is one of the simplest supervised learning strategies: given a new, unknown observation, it simply looks up in the reference database which ones have the closest features and assigns the predominant class. Let's try and understand kNN with examples. #Importing required packagesfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn import metricsfrom sklearn.cross_validation import train_test_splitimport matplotlib.pyplot as pltimport matplotlib as mplimport numpy as npimport seabornfrom pprint import pprint%matplotlib inline #Let's begin by exploring one of scikit-learn's easiest sample datasets, the Iris.from sklearn.datasets import load_irisiris = load_iris()print iris.keys() ['target_names', 'data', 'target', 'DESCR', 'feature_names'] #The Iris contains data about 3 types of Iris flowers namely:print iris.target_names#Let's look at the shape of the Iris datasetprint iris.data.shapeprint iris.target.shape#So there is data for 150 Iris flowers and a target set with 0,1,2 depending on the type of Iris.#Let's look at the featuresprint iris.feature_names#Great, now the objective is to learn from this dataset so given a new Iris flower we can best guess its type#Let's keep this simple to start with and train on the whole dataset. ['setosa' 'versicolor' 'virginica'](150, 4)(150,)['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] #Fitting the Iris dataset using KNNX,y = iris.data, iris.target#Fitting KNN with 1 Neighbor. This is generally a very bad idea since the 1st closest neighbor to each point is itself#so we will definitely overfit. It's equivalent to hardcoding labels for each row in the dataset.iris_knn = KNeighborsClassifier(n_ Continue reading >>

Diagnostic Controversy: Gestational Diabetes And The Meaning Of Risk For Pima Indian Women

Diagnostic Controversy: Gestational Diabetes And The Meaning Of Risk For Pima Indian Women

Gestational diabetes is the one form of this well known, chronic disease of development that disappears. After the birth of the child, the mother's glucose levels typically return to normal. As a harbinger of things to come, gestational diabetes conveys greater risk for later type 2 (previously “non-insulin dependent”) diabetes in both the mother and child. Thus, pregnant women have become a central target for prevention of this disease in the entire Pima population. Based on ethnographic interviews conducted between 1999 and 2000, I discuss the negotiated meanings of risk, “borderline” diabetes, and women's personal knowledge and experiences of diabetes, particularly during the highly surveilled period of pregnancy. I also highlight the heterogeneity of professional discourse pertaining to gestational diabetes, most notably the debate surrounding its diagnosis. Significantly, women's narratives reveal the same set of questions as is raised in the professional debate. Implications for diabetes prevention and for balancing the increased surveillance of pregnant women with clinical strategies that privilege their experience and perspectives are also discussed. Additional information Author information Continue reading >>

Mldata :: Repository :: :: Diabetes_scale

Mldata :: Repository :: :: Diabetes_scale

This data was originally automatically copied from: and is originally from There are various versions of this data on this repository: UCI / Pima Indians Diabetes The README from UCI: Sources: (a) Original owners: National Institute of Diabetes and Digestive and Kidney Diseases (b) Donor of database: Vincent Sigillito ([email protected]) Research Center, RMI Group Leader Applied Physics Laboratory The Johns Hopkins University Johns Hopkins Road Laurel, MD 20707 (301) 953-6231 (c) Date received: 9 May 1990 Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., & Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In {it Proceedings of the Symposium on Computer Applications and Medical Care} (pp. 261--265). IEEE Computer Society Press. The diagnostic, binary-valued variable investigated is whether the patient shows signs of diabetes according to World Health Organization criteria (i.e., if the 2 hour post-load plasma glucose was at least 200 mg/dl at any survey examination or if found during routine medical care). The population lives near Phoenix, Arizona, USA. Results: Their ADAP algorithm makes a real-valued prediction between 0 and 1. This was transformed into a binary decision using a cutoff of 0.448. Using 576 training instances, the sensitivity and specificity of their algorithm was 76% on the remaining 192 instances. Relevant Information: Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. ADAP is an adaptive learning routine that generates and executes digital analogs of perceptron-like devices. It is a unique algorithm; see the paper for details. Plasma glucose concentr Continue reading >>

R Programs And Datasets

R Programs And Datasets

The R procedures and datasets provided here correspond to many of the examples discussed in R.K. Pearson, Exploring Data in Engineering, the Sciences, and Medicine. The R procedures are provided as text files (.txt) that may be copied and pasted into an interactive R session, and the datasets are provided as comma-separated value (.csv) files. These files are easily read in R via the read.csv command, or they may be examined by opening them in Microsoft Excel. Note that the R procedures described here are built on commands available in base R and the add-on packages designated as recommended, and do not require any other add-on packages. These commands were implemented in R version 2.11.1, installed as binary files in a Microsoft Windows environment. Note that versions of a number of these datasets are available as built-in datasets in a variety of R packages (e.g., the von Bortkewitsch horsekick deaths data is available in the R add-on package vcd as the dataset VonBort). In addition, three of these datasets (federalist.csv, horsekick.csv, and bitterpit.csv) were constructed from datasets described in the book Data by D.F. Andrews and A.M. Herzberg (Springer-Verlag, New York, 1985) and available from the following website: Similarly, the datasets mushroom.csv and pima.csv were constructed from datasets available from the UCI Machine Learning Repository (Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository [Irvine, CA: University of California, School of Information and Computer Science). The first industrial pressure dataset ( pressure1.csv ) The second industrial pressure dataset ( pressure2.csv ) The third industrial pressure dataset ( pressure3.csv ) The fourth industrial pressure dataset ( pressure4.csv ) R code to generate Fig. 1.8 boxplot ( ch1fig8pr Continue reading >>

How To Load Machine Learning Data In Python

How To Load Machine Learning Data In Python

How To Load Machine Learning Data in Python You must be able to load your data before you can start your machine learning project. The most common format for machine learning data is CSV files. There are a number of ways to load a CSV file in Python. In this post you will discover the different ways that you can use to load your machine learning data in Python. Update March/2017: Change loading from binary (rb) to ASCII (rt). Update March/2018: Added alternate link to download the dataset as the original appears to have been taken down. Update March/2018: Updated NumPy load from URL example to work wth Python 3. How To Load Machine Learning Data in Python Photo by Ann Larie Valentine , some rights reserved. There are a number of considerations when loading your machine learning data from CSV files. For reference, you can learn a lot about the expectations for CSV files by reviewing the CSV request for comment titled Common Format and MIME Type for Comma-Separated Values (CSV) Files . If so this can help in automatically assigning names to each column of data. If not, you may need to name your attributes manually. Either way, you should explicitly specify whether or not your CSV file had a file header when loading your data. Continue reading >>

5. Datasets Kurs Programowania W Python I Machine Learning #ebb625a, 2018-04-19 Documentation

5. Datasets Kurs Programowania W Python I Machine Learning #ebb625a, 2018-04-19 Documentation

The Iris flower data set or Fishers Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other. Fig. 5.5. Scatterplot of the Iris data set Based on Fishers linear discriminant model, this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines. Fig. 5.6. Unsatisfactory k-means clustering result (the data set does not cluster into the known classes) and actual species visualized using ELKI >>> from sklearn.datasets import load_iris>>> iris = load_iris()>>> print(iris.feature_names)['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']>>> print(iris.target_names)['setosa' 'versicolor' 'virginica']>>> print(iris.data[0])[5.1 3.5 1.4 0.2]>>> print(iris.target[0])0 This problem is comprised of 768 observations of medical details for Pima indians patents. The records describe instantaneous measurements taken from the patient such as their age, the number of times pregnant and blood workup. All patients are women aged 21 or older. All attributes are numeric, and their units vary from attribute to attribute. Plasma glucose concentration a 2 hours in an oral glucose tolerance test Body mass index (weight in kg/(height in m)^2) The sklearn.dat Continue reading >>

Python Basics: Logistic Regression With Python

Python Basics: Logistic Regression With Python

Python Basics: Logistic regression with Python Logistic regression is one of the basics of data analysis and statistics. The goal of the regression is to predict an outcome, will I sell my car or not? Is this bank transfer fraudulent? Is this patient ill or not? All these outcomes can be encoded as 0 and 1, a fraudulent bank transfer could be encoded as 1 while a regular one would be encoded as 0. As with linear regression, the inputs variable can be either categorical or continuous. In this tutorial, we will create a Logistic regression model to predict whether or not someone has diabetes or not. The dataset that will be used is from Kaggle: Pima Indians Diabetes Database . It has 9 variables:Pregnancies, Glucose,BloodPressure,SkinThickness,Insulin, BMI, DiabetesPedigreeFunction,Age, Outcome. Here is the variable description from Kaggle : Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm) BMI: Body mass index (weight in kg/(height in m)^2) DiabetesPedigreeFunction: Diabetes pedigree function All these variables are continuous, the goal of the tutorial is to predict if someone has diabetes (Outcome=1) according to the other variables. It worth noticing that all the observations are from women older than 21 years old. First, please download the data. Then, with pandas, we will read the CSV: import pandas as pdimport numpy as npDiabetes=pd.read_csv('diabetes.csv')table1=np.mean(Diabetes,axis=0)table2=np.std(Diabetes,axis=0) To understand the data, lets take a look at the different variables means and standard deviations Mean and stard deviation of the vairables The data are unbalanced with 35% of observations having diabetes. The standard deviati Continue reading >>

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