In this post, we’ll introduce the new predictive modeling functions by exploring the relationship between health spending per capita and female life expectancy in the World Indicators data set. Edit:Upgrade to 2020.3and follow these examples withthis sample workbook. What are predictive modeling func...
Python+Machine Learning tutorial - Data munging for predictive modeling with pandas and scikit-learnBuilding predictive models first requires shaping the data into the right format to meet the mathematical assumptions of machine learning algorithms. In this session we will introduce the pandas data ...
country_map=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Merge and Join/Medals/Athelete_Country_Map.csv') country_map.head() The output data frame looks similar to the following screenshot, with two columns: Athlete and Country: Fig. 3.48: First few entries of th...
The respective IPython notebooks can be found in theStatistical Pattern Classification Examples”section in my pattern classification repository if you are interested in how the decision boundaries were computed. In contrast, unsupervised learning task deal with unlabeled instances, and the classes have to...
Weather forecasting is one of the best-known uses of predictive modeling. Predictive analytics is also used to forecast elections, predict the spread of diseases and model the effects of climate change. In business, predictive modeling helps companies optimize operations, improve customer satisfaction,...
The main goal of this book is to make you familiar with all the stages in the process of solving a business problem using predictive modeling and to show, with hands-on examples, how to use Python and its data analytics ecosystem to implement many of the main techniques and approaches used...
The three aspects of predictive modeling we looked at were: Sample Data: the data that we collect that describes our problem with known relationships between inputs and outputs. Learn a Model: the algorithm that we use on the sample data to create a model that we can later use over and ...
Financial time series predictive modeling use case: predicting whether the S&P 500 Index will close positive or negative Canonical engineers use a proven and economical architecture to run this financial time series use case in an AI/ML infrastructure. The goal is to have an end-to-end on premi...
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. For interpretability in NLP, check out our new package: imodelsX 📚 docs • 📖 demo notebooks Modern machine-learning models are increasingly complex, often making them dif...
Coursera Machine Learning - Python code predictive-modelingcoursera-machine-learningandrew-ng UpdatedOct 1, 2020 Jupyter Notebook retentioneering/retentioneering-tools Star829 Code Issues Pull requests Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, tran...