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automations

Intended for re-useable scripts in data preparation and plotting functions

automlbenchmark

OpenML AutoML Benchmarking Framework

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. New PR ☛☛☛

candle-stick-screener-for-nifty-companies

candle stick screener using yfinance and ta-lib library in flask

code_snippets

codestackr

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

covid19africa

Africa open COVID-19 data working group

coworking-analysis

Creative economy and the future of work through the lens of meetup.

data_wrangling-1

datascience

How to be a Data Scientist

docker-for-data-science-tutorial

Materials for "Docker for Data Science" tutorial presented at PyCon 2018 in Cleveland, OH

gameofthrones---network-analysis

An analysis of game of thrones characters to predict the most important ones from book 1 to book5

heart-disease-analysis

LOGISTIC REGRESSION - HEART DISEASE PREDICTION Introduction World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients and in turn reduce the complications. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression Data Preparation Source The dataset is publically available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD).The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. Demographic: • Sex: male or female(Nominal) • Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) Behavioral • Current Smoker: whether or not the patient is a current smoker (Nominal) • Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history) • BP Meds: whether or not the patient was on blood pressure medication (Nominal) • Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal) • Prevalent Hyp: whether or not the patient was hypertensive (Nominal) • Diabetes: whether or not the patient had diabetes (Nominal) Medical(current) • Tot Chol: total cholesterol level (Continuous) • Sys BP: systolic blood pressure (Continuous) • Dia BP: diastolic blood pressure (Continuous) • BMI: Body Mass Index (Continuous) • Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.) • Glucose: glucose level (Continuous) Predict variable (desired target) • 10 year risk of coronary heart disease CHD (binary: “1”, means “Yes”, “0” means “No”) Logistic Regression Logistic regression is a type of regression analysis in statistics used for prediction of outcome of a categorical dependent variable from a set of predictor or independent variables. In logistic regression the dependent variable is always binary. Logistic regression is mainly used to for prediction and also calculating the probability of success. The results above show some of the attributes with P value higher than the preferred alpha(5%) and thereby showing low statistically significant relationship with the probability of heart disease. Backward elimination approach is used here to remove those attributes with highest P-value one at a time followed by running the regression repeatedly until all attributes have P Values less than 0.05. Feature Selection: Backward elimination (P-value approach) Logistic regression equation P=eβ0+β1X1/1+eβ0+β1X1P=eβ0+β1X1/1+eβ0+β1X1 When all features plugged in: logit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucoselogit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucose Interpreting the results: Odds Ratio, Confidence Intervals and P-values • This fitted model shows that, holding all other features constant, the odds of getting diagnosed with heart disease for males (sex_male = 1)over that of females (sex_male = 0) is exp(0.5815) = 1.788687. In terms of percent change, we can say that the odds for males are 78.8% higher than the odds for females. • The coefficient for age says that, holding all others constant, we will see 7% increase in the odds of getting diagnosed with CDH for a one year increase in age since exp(0.0655) = 1.067644. • Similarly , with every extra cigarette one smokes thers is a 2% increase in the odds of CDH. • For Total cholesterol level and glucose level there is no significant change. • There is a 1.7% increase in odds for every unit increase in systolic Blood Pressure. Model Evaluation - Statistics From the above statistics it is clear that the model is highly specific than sensitive. The negative values are predicted more accurately than the positives. Predicted probabilities of 0 (No Coronary Heart Disease) and 1 ( Coronary Heart Disease: Yes) for the test data with a default classification threshold of 0.5 lower the threshold Since the model is predicting Heart disease too many type II errors is not advisable. A False Negative ( ignoring the probability of disease when there actually is one) is more dangerous than a False Positive in this case. Hence in order to increase the sensitivity, threshold can be lowered. Conclusions • All attributes selected after the elimination process show P-values lower than 5% and thereby suggesting significant role in the Heart disease prediction. • Men seem to be more susceptible to heart disease than women. Increase in age, number of cigarettes smoked per day and systolic Blood Pressure also show increasing odds of having heart disease • Total cholesterol shows no significant change in the odds of CHD. This could be due to the presence of 'good cholesterol(HDL) in the total cholesterol reading. Glucose too causes a very negligible change in odds (0.2%) • The model predicted with 0.88 accuracy. The model is more specific than sensitive. Overall model could be improved with more data

images

keras-timeseries-prediction

Time series prediction with Sequential Model and LSTM units

kmeans-clustering

Multivariate analysis: a white box approach to a black box algorithm (unsupervised machine learning with k-means classification)

lstm-fcn

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

m0nica

All about Monica

m4metalearning

M4 competition - https://doi.org/10.1016/j.ijforecast.2019.02.011

machine-learning-engineering-with-mlflow

Machine Learning Engineering with MLflow, published by Packt

machine-learning-engineering-with-python

Machine Learning Engineering with Python

machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

mapping

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

mlcourse.ai-yorko

Open Machine Learning Course

ncov2019

query stats of infected coronavirus cases

pml2-book

Probabilistic Machine Learning: Advanced Topics

python

All Algorithms implemented in Python

python-docker-app