Applied Data Science
Sonpvh, 2022
1
2
1. Introduction
2. Application
3. EDA
4. Learning Process
5. Bias –Variance TradeOff
6. Regression
7. Classification
8. Validation
9. Regularization
10. Clustering
11. Evaluation
12. Deployment
13. Ethics
3
UNKNOWN TARGET
FUNCTION
𝑓: 𝒳 → Υ
Training sample
(x𝟏, y𝟏), (x𝟐, y𝟐), …
Hypothesis Set
ℋ
Learning
algorithms
𝓐
Final Hypothesis
g: 𝒳 → Υ
Learning From Data –Yaser [1]
g(x) ≈𝑓(x)
4
AWARENESS
INTEREST
LEAD FORM
TELESALE
ELIGIBILITY
DISBURSED
GOOD vs BAD
𝑓: 𝒳 → Υ
Probability Distribution
P on 𝒳
•Age
•Salary
•Job status
•Household size
•….
Training sample
(x𝟏,y𝟏), (x𝟐,y𝟐), …
Hypothesis Set
ℋ
Learning
algorithms
𝓐
x𝟏, x𝟐, … , xN
GOOD vs BAD
y (1:0)
Final Hypothesis
g: 𝒳 → Υ
What is eligibility?
(label definition)
g(x) ≈
𝑓(x)
