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Capstone 2
"Current title : "USING A PERSONALISED RECOMMENDATION MODEL TO IMPROVE THE SEARCH RESULT EXPERIENCE OF EDUKASYON.PH REGISTERED USERS"

"Propose change: "Explore your options  in searching schools and courseUsing Personalized Recommendation model to increase conversion rate in education platform"

### Capstone 1
### Methodology – express the business problem in the context of analytics algorithmic techniques.
3.1. Analytics Goal. Translate the business goal in technical terms.
3.2. Analytics Approach. From the review of related works, describe in detail the technical methodology the project will
use. Include discussion on data ethics and law.
3.3. Technical success criteria – Specify the technical criteria such as model accuracy necessary to support the business
criteria.
3.4. Project plan – Describe the plan for achieving the analytics and business goals.

### Capstone 2

#### Chapter 4 – Data understanding

4.1 Data requirements
-specify the data needed by the chosen analytics approach

4.2 Data collection 
-determine if additional data are needed, specify data sources

4.3 Data understanding 
-use descriptive statistics and visualization techniques to understand data content
-access data quality and discover initial insights about the data
-additional data collection may be necessary to fill gaps.


#### Chapter 5 Data Preparation
-construct the data set that will be used in subsequent modelling stage
-data preparation activities include data cleaning, combining data from multiple sources
And transforming data into more useful variables

#### Chapter 6 Modeling
-stage focus on developing models according to the previously defined approach 
-modelling process is typically highly iterative 
-multiple algorithms with respective parameters to find the best model available for variables

#### Chapter 7 – Evaluation
-evaluates the model to understand it's quality and ensure that it is properly and fully 
Meets the technical success criteria 
-model evaluation entails computing various diagnostic measure and other outputs such as tables and graphs to enable the interpretation of the model and it's efficacy in meeting the analytics goal.

#### Chapter 8 Result and Discussion
-direct answers to the business problem 
-analytics solution

#### Chapter 9 Conclusion 
-gives summary describing the significance of the findings with respect to any impact to the organization’s policy, strategy or practice.
-redeem the significance from chapter 1

#### Chapter 10 
-states some suggestions with reference to chapter 1 scope and limitations.


#####Due dates to remember####
![](https://i.imgur.com/XDkzFXI.png)
![](https://i.imgur.com/C4oMaqV.png)



| Project|Deliverables & NDC|Target Date C|Status|Remarks
| 
|Data UDST |                |05/1-7/22    |      |     |  |Data Prep |                |05/1-7/22    |      |     |
|Data MDL  |                |05/10-14/22  |      |     |
|Data Eval |                |05/17-21/22  |      |     |
|Results&D |                |05/17-21/22  |      |     |
|Concl &Rec|                |05/17-21/22  |      |     |








