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fixed typo in README.md file #2

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fixed typo in README.md file #2

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6 changes: 3 additions & 3 deletions Data Science Methodology/Week 3/Final Assingment.md
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Expand Up @@ -13,7 +13,7 @@ I have chosen as topic for this task the application of data science in the fiel

For example, using the food recipes use case discussed in the labs, the question that we defined was, "Can we automatically determine the cuisine of a given dish based on its ingredients?".

So the main problem for banks regarding credit cards is that they have to create a model to know to who they can provide them. Certain clients will not be feasible as they do not have the economic strenght to back up this service.
So the main problem for banks regarding credit cards is that they have to create a model to know to who they can provide them. Certain clients will not be feasible as they do not have the economic strength to back up this service.

So our question would be " Can we automatically determine if a client is suitable to obtain a credit card?

Expand All @@ -31,8 +31,8 @@ So our question would be " Can we automatically determine if a client is suitabl

2. Data Requirements: To create the classification model we will require information regarding the bank clients. This info should include personal data of the client and should include the ones that defaulted and the one that paid.

3: Data Collection: We would use techiques like descriptive statistics and data evalution should be implemented in this phase to make sure that we have useful data for our model.
3: Data Collection: We would use techniques like descriptive statistics and data evalution should be implemented in this phase to make sure that we have useful data for our model.

4: Data Undestanding and Preparation: In this step we need to evaluate the different variables of our data in order to undestant it better. For example we would calculate univariate statistics, such as mean or median and the correlation between variables. So we need to evaluate the quality of the data. In the data preparation phase we have to prepare the data in an specific way depending on the model.
4: Data Understanding and Preparation: In this step we need to evaluate the different variables of our data in order to understand it better. For example we would calculate univariate statistics, such as mean or median and the correlation between variables. So we need to evaluate the quality of the data. In the data preparation phase we have to prepare the data in an specific way depending on the model.

5: Modeling and Evaluation: Lastly we create a classification model, evaluate the outcome and perform the corresponding changes untill we have a suitable model.