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My Interaction was very short but left a positive impression. I enrolled and asked for a refund since I could not find the time. What happened next: They initiated Refund immediately. Their... Read More

The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More

Importing and understanding the dataset

Understanding unbalanced data and converting class into factors

Dividing the dataset into equal parts with equal distribution of both classes

Imputing for the null values

Defining cross-validation, metrics, and preprocessing techniques

Understanding and implementing LOGISTIC REGRESSION and selecting important features

Applying Bayesian model along with recursive partitioning

Improve Logistic Results using Random Forest

Implementing boosting like AdaBoost and GradientBoostingClassifier.

Defining AUC-ROC score and getting in-depth knowledge of how it works

Using Gini, AUC, and KS for evaluating model

Understanding Recall, Precision and F1score

Model Improvement with Gaussian RBF kernel

Display Performance Reports and interpreting the same

Visualizing the result of each model via plot

In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Introduction

07m

Import Data Sets

01m

Rename Columns

10m

Next Steps

00m

Import Libraries

06m

Distribution of Columns

04m

Different Approaches

02m

Weight of Evidence (WOE)

12m

Information Value (IV)

04m

Compute WOE and IV

06m

Univariate, Bivariate and Multivariate

06m

Logistic Regression Introduction

07m

Recap

01m

Library Overview

01m

Data Set Overview

02m

Next Steps - Overview

02m

Custom Functions

05m

Function to Compute WOE and IV

02m

Compute WOE and IV for each Variable

21m

Variable Clustering

05m

Training and Testing Sample

02m

Logistic Regression

11m

Logistic Regression - QnA

08m

Decision Tree

08m

Random Forest

04m

Logistic Regression using important variables

03m

Conditional Tree

01m

Bayesian Learn Model

03m

KSVM - Kernel Support Vector Machines

05m

Neural Network

04m

Compare all Models

05m