5 Steps in Alteryx Predictive Analytics Process
The five major stages of the predictive analytics process cycle include selecting a target variable, examining the data, collecting the data, creating the model, and scoring the model.
A detailed description of the steps involved in the predictive analytics process in Alteryx:
- Step 1: Select a Target Variable
- Step 2: Analyse Your Data
- Step 3: Run Calculations/ Collect New Data
- Step 4: Model Building
- Step 5: Score the Model
Step 1: Select a Target Variable
Select the target variable which is the column that should be predicted. It could be a binary or non-binary categorisation or a numerical value and it can be continuous or time-based. Each of these target variables helps in finding business solutions. But just because time is a variable in the problem does not mean that a time-based model will be the best way to solve it. Simultaneously if a field has a numeric value, it does not mean that a binary model cannot be utilised in finding insights.
Step 2: Analyse Your Data
The largest contributor to excellent predictive models is the sample size. Anything less than 5000 records is counted as under-sampled and using it is not considered the best practice.
Alteryx and Tableau Prep are both excellent tools for understanding data by creating histograms, scatterplots, and correlation matrices. Before step 3, in the data transformation procedure, it is better to know what types of variables are in the data. There are various sorts of predictor variables and several types of target variables, and each must be structured differently.
- Categorical Data: String fields with no order are categorical data. It contains data in the form of text.
- Ordinal Data: String fields with an order are ordinal data. It can be substituted into numeric order in a predictive workflow.
- Numeric Data: It represents information with a measurable value.
- Cyclical Information: Data which gets repeated as such in a cyclic process is cyclic information.
Step 3: Run Calculations/ Collect New Data
Obtaining the greatest data or inferring fields from present data, such as adding seasonality, can be a powerful predictor variable. Always be inventive in the choice of variables. It is crucial to note that if there is to infer a piece of data, it is sometimes unwise to include both that data and the original data column in the same model because the predictive model would automatically give higher weight to this column. It is also critical to recognise that while it is beneficial to include factors with correlation, variables that drown out all other variables must occasionally be removed.
Step 4: Model Building
a. Make Use of the Decision Tree
Using a Decision Tree, it is possible to rapidly discover which of the factors are the most crucial for predicting the target variable. This model will not be utilised in the final forecast since it will over-fit, but it will show whether some of the variables are overly connected to the target variable.
b. Experiment with Different Models
Data Science is complicated, and it is difficult to know which model will yield the best results, therefore a variety of models, such as Random Forest, Boosted Models, and Neural Networks can be employed for better results.
Step 5: Score the Model
Alteryx offers a scoring tool that may be used to score models. During this step, data should be withheld for the model to test and score. Even though different models can provide different scores, through testing and reconfiguring, accurate predictions can be made.
What makes Alteryx an exceptional tool for predictive analytics?
These remarkable capabilities make Alteryx an excellent tool to carry out predictive analytics tasks easily:
- • No or Low coding required
- • Predictive analytics by drag and drop
- • Predictive tool kit for specifically performing predictive analytics
- • Integration to R and Python
- • Variety of built-in and custom ML models are available
- • Model customizations are possible
- • Automation and/or scheduling of predictive analytics workflows
Predictive Analytics Tools
Predictive analytics solutions use the power of data to help businesses in identifying trends in customer behaviour, making predictions, and developing optimised marketing plans.
The tools that aid in predictive analytics are enlisted below:
- Data Investigation Tools
- Predictive Tools
- Tools for the Modern Statistical Learning Method
- Tools for Predictive Model Comparison and Hypothesis Testing
- Tool for Predicting Values for All General Predictive Modeling Tools
1. Data Investigation Tools
Data investigation tools contain tools that help to get a better understanding of data. To better understand the data used in a predictive analytics project including both visualization tools and tools that provide tables of descriptive statistics.
The list of data investigation tools is given below:
- Field Summary Tool
- Heat Plot Tool
- Histogram Tool
- Plot of Means Tool
- Scatterplot Tool
- Violin Plot Tool
2. Predictive Tools
This category contains general predictive modelling tools for classification and regression models, and also tools for predictive modelling related to model comparison and hypothesis testing.
Predictive tools are enlisted below:
- Count Regression Tool
- Gamma Regression Tool
- Linear Regression Tool
- Logistic Regression Tool
- Naïve Bayes Classifier Tool
- Neutral Network Tool
- Stepwise Tool
- Support Vector Machine Tool
3. Tools for the Modern Statistical Learning Method
- Boosted Model Tool
- Decision Tree Tool
- Forest Model Tool
- Spline Tool
4. Tools for Predictive Model Comparison and Hypothesis Testing
- Cross-Validation Tool
- Lift Chart Tool
- Model Coefficients Tool
- Model Comparison Tool
- Nested Test Tool
- Test of Means Tool
- Variance Inflation Factors Tool
5. Tool for Predicting Values for All General Predictive Modeling Tools
- Score Tool
6. Time Series Tools
- ARIMA tool
- ETS tool
- TS Compare Tool
- TS Covariate Forecast Tool
- TS Filler Tool
- TS Forecast Tool
- TS Forecast Factory Tool
- TS Model Factory Tool
- TS Plot Tool
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