Predictive Analytics Using External Data Augmented Analytics Use Case - Smarten

•Download as PPTX, PDF•

1 like•1,260 views

A company can use predictive analytics to determine how external factors like GDP, rainfall, and population influence sales. A multiple linear regression model is developed using these external factors to predict sales. The model performance is evaluated based on its R-squared value, with values over 0.7 indicating a more accurate model. Sample visualizations and predictions of sales based on the external factors are presented.

Data & Analytics

Predictive Analytics of
External Data
Predictive Analytics Use Case

A company can determine the influence of the internal and
external data on sales and identify whether factors such as
GDP, rainfall or population are effective for the business.
Further appropriate action can be taken to increase sales or
ROI.
Predictive Analytics of External Data
Sample Application
Description

Target
Sales
Predictive Analytics of External Data
Sample Application

• GDP
• Rainfall
• Vacancy
• Wholesale gap
Influencing
Factors
Predictive Analytics of External Data
Sample Application

Multiple Linear Regression is a technique that explores the
relationship between two or more independent variables
and one dependent variable.
• The higher the R-square value of a model(>=0.7), the
better the accuracy of the model.
• A lower R-square value of a model(<0.7) means the model
needs to be rebuilt using different input parameters or the
input dataset doesn't exhibit a structure suitable for
regression.
Algorithm(s)
Predictive Analytics of External Data
Sample Application

Model
Visualisation
Predictive Analytics of External Data
Sample Application

Model
Summary
Predictive Analytics of External Data
Sample Application

Interpretation
Predictive Analytics of External Data
Sample Application

Result
• Predicted Sales
Predictive Analytics of External Data
Sample Application

Result
Sales prediction with accuracy value (R-square value) based on
advertisement expense, wholesale gap and vacancy can be
performed using APPLY functionality as shown below.
Predictive Analytics of External Data
Sample Application

Result
Predictive Analytics of External Data
Sample Application

Smarten – Predictive Analytics of External Data Use Case - 2019
Predictive Analytics of
External Data
Predictive Analytics Use Case
For more information, contact us today.
www.Smarten.com
contact@Smarten.com

What's hot

Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.

What is Descriptive Statistics and How Do You Choose the Right One for Enterp...

Smarten Augmented Analytics

Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.

What is Hierarchical Clustering and How Can an Organization Use it to Analyze...

Smarten Augmented Analytics

What Is Random Forest Classification And How Can It Help Your Business?

Smarten Augmented Analytics

This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

Random Forest Regression Analysis Reveals Impact of Variables on Target Values

Smarten Augmented Analytics

Crime Type Prediction - Augmented Analytics Use Case – Smarten

Smarten Augmented Analytics

Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.

What is Binary Logistic Regression Classification and How is it Used in Analy...

Smarten Augmented Analytics

Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.

What is Multiple Linear Regression and How Can it be Helpful for Business Ana...

Smarten Augmented Analytics

Logistic regression measures the relationship between the categorical target variable and one or more independent variables It deals with situations in which the outcome for a target variable can have two or more possible types. The Multinomial-Logistic Regression Classification Algorithm is useful in identifying the relationships of various attributes, characteristics and other variables to a particular outcome.

What is the Multinomial-Logistic Regression Classification Algorithm and How ...

Smarten Augmented Analytics

What is Naïve Bayes Classification and How is it Used for Enterprise Analysis?

Smarten Augmented Analytics

SEE Sensitivity Analysis

umair khan

Fraud Mitigation Predictive Analytics Use Case – Smarten

Smarten Augmented Analytics

Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.

What is the Holt-Winters Forecasting Algorithm and How Can it be Used for Ent...

Smarten Augmented Analytics

The aim of this project is to help a telecom company with insights on customer behavior that would be useful for retention of customers. The specific goals expected to be achieved are given below 1. Identification of the top variables driving likelihood of churn 2. Build a predictive model to identify customers who have highest probability to terminate services with the company. 3. Build a lift chart for optimization of efforts by targeting most of the potential churns with least contact efforts. Here with 30% of the total customer pool, the model accurately provides 33% of total potential churn candidates. Models tried to arrive at the best are 1. Simple Models like Logistic Regression & Discriminant Analysis with different thresholds for classification 2. Random Forest after balancing the dataset using Synthetic Minority Oversampling Technique (SMOTE) 3. Ensemble of five individual models and predicting the output by averaging the individual output probabilities 4. Xgboost algorithm

Prediction of customer propensity to churn - Telecom Industry

Pranov Mishra

The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company.. The goal of the project is building a predictive model using the identified significant contributors and coming up with recommendations for changes which will lead to 1. Reducing Re-work 2. Reducing Operational Cost 3. Improve Customer Satisfaction 4. Improve company preparedness to respond to customer. Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex. Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance. Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles

Reduction in customer complaints - Mortgage Industry

Pranov Mishra

Ch. 4-demand-estimation(2)

anj134u

T19 factor analysis

kompellark

Linear regression

NilanjanaPradhan2

Credit risk - loan default model

Saleesh Satheeshchandran

The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. This algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.

What is the KMeans Clustering Algorithm and How Does an Enterprise Use it to ...

Smarten Augmented Analytics

Qt unit i

bhuvana ganesan

What's hot (20)