Prediction rate Comparison between Simple Machine learning Model with ANN Model

1. Deep Learning
Arun Shukla
Senior Consultant
Data Science|Machine Learning|Deep Learning

2. Deep Learning Libraries

3. Content
 About Deep Learning
 Challenges that can be solved using
Deep Learning
 Applications
 Architecture
 Framework of Deep Learning
 Holistic Approach To End-to-End
Solution Building
 Case study
• Business Challenges
• Solution Approach and Execution
• Comparison with other Models

4. About Deep Learning
 Deep Learning is a subset of Machine Learning, grown out from the field of AI
• A field that examines computer algorithms that learn and improve on their own
• Neural networks, a beautiful biologically-inspired programming paradigm which
enables a computer to learn from observational data

5. Challenges that can be solved using Deep Learning
 Data dependencies
 Huge Volume of data
 High computational Accuracy,
 More Accurate prediction and classification models
 More Parameter and hyper parameter tuning
 Highly integrable with 4V of big data

6. Application Area
 Deep learning Support Supervised Learning &Unsupervised Learning
 Computer vision, Pattern recognition, NLP
 Building Business chatbot
 Marketing
 Retail and Sales
 Banking & Finance
 Insurance
 Telecommunications
 Operations management

7. Architecture of Deep Learning
Four fundamental network architectures
1. Convolutional neural networks
2. Recurrent neural networks
3. Self Organizing Map
4. Boltzmann Machine
5. Auto Encoder

8. Framework of Deep Learning
Processed
Data
Labeled
data
Unlabeled
data
Predicted
Item-1
Item-2
Item-3
Item-4

9. Holistic Approach To End-to-End Solution Building
Model Validation
K Fold Validation,
Accuracy Measurement,
Confusion Metrix,
Hyper Parameter tuning.
Model Design
Work frame Selection,
Algorithm Selection,
Training and Testing of Model
Data Pre Processing
Data understanding, Label/unlabeled
data, Missing/NAN values,
Descriptive/Statistical Analysis,
Business Problem
Business objective,
Pain point,
Key requirement,
Scope of work
Model deployment
Best Accurate Tuned Model

10. Case Study - Business Problem
Business Issue
• A bank was facing large number of churn
• Business risk of account closure
• A loss in revenue and ‘SoLoMo’ image
Business Requirements
• Development of Predictive analytics platform to identify and report:
o Set of customers who are prone to close the account
o Key factors which impact the decision to foreclosure of account
• Necessary actions based on analytical insights to reduce business risk
• Statistical tests to measure the effectiveness of actions and make necessary
changes if required.

11. Solution Approach & Execution
11
Validation
Data
Raw Data
Feature
Engineering
(13 feature)
Training
Data
Modeling
ANN-1
Logistic-2
SVM-3
Model
Validation
Select best
Model
Scoring of
Model
Customer
Tagging
Approach
• Integration of various datasets
• Exploration of datasets
• Treatment of missing values
• Creation of dummy variable
• Correlation between features
• Application of several predictive algorithms
• Select best algorithm based on prediction accuracy
• Tagging of customers based on predictive model based scores

12. Statistic of Data
12
Correlation matrix with in features and with dependent variable

13. Statistic of Data
13
0-Female
1-Male
Histogram of Age
1-Spain
2-Germany
3.france
Histogram of Geography
Histogram of Age Histogram of credit score
Histogram of Tenure
Histogram of Features

14. ANN-Model Accuracy Measurement
Model-ANN
Algo-Adam(Gradient Descent)
Epoch-100
Validation Loss-0.40
Validation Accuracy-83.60
Training Loss-0.38
Training Accuracy-84.01

15. Comparison with other models
15
CONFUSION MATRIX
ANN SVM Logistic
0 1 0 1 0 1
0 1532 73 0 1517 98 0 1526 69
1 254 141 1 255 130 1 309 96
% Accuracy=83.65 % Accuracy=82.35 % Accuracy=81.1
Model-2
SVM Classification
Model-3
Logistic Regression
Model-1
ANN

16. Thank You…

17. References
1.www.google.com
2. www.kdnuggets.com
3. www.machinelearningmastry.com