Duration: Hours

Predictive modeling is a statistical technique using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data. It works by analyzing current and historical data and projecting what it learns on a model generated to forecast likely outcomes.

Training Mode: Online

Description

Predictive Modeling is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics.The model class defines a new Kind of datastore entity and the properties the Kind is expected to take. The Kind name is defined by the instantiated class name that inherits from db. Model .

1. Introduction to Predictive Modeling

a). Concept of model in analytics and how it is used

b). Common terminology used in modelling process

c). Types of Business problems – Mapping of Algorithms

d). Different Phases of Predictive Modelling

e). Data Exploration for modelling

f). Exploring the data and identifying any problems with the data (Data Audit Report)

g). Identify missing/Outliers in the data

h). Visualize the data trends and patterns

2. Regression Problems

a). Linear Regression

b). Non-linear Regression

c). K-Nearest Neighbour

d). Decision Trees

e). Ensemble Learning – Bagging, Random Forest, Ad boost, Gradient Boost, XGBoost

f). Support Vector Regressor

3. Classification Problems

a). Logistic Regression

b). K-Nearest Neighbour

c). Naïve Bayes Classifier

d). Decision Trees

e). Ensemble Learning – Bagging, Random Forest, Ad boost, Gradient Boost, XGBoost

f). Support Vector Classifier

For more inputs on Predictive Modeling with Python you can connect here.
Contact the L&D Specialist at Locus IT.

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