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‎projects/capstone/README.md

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@@ -8,6 +8,4 @@ The Capstone is a two-staged project. The first is the proposal component, where
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You can find the [capstone proposal rubric here](https://review.udacity.com/#!/rubrics/410/view), and the [capstone project rubric here](https://review.udacity.com/#!/rubrics/108/view). Please ensure that you are following directions correctly before submitting these two stages which encapsulate your capstone.
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You will find an `open_projects` folder in these files. This will be where pre-curated capstone projects are available, should you choose to work on a project already partially designed for you.
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Please email [[email protected]](mailto:[email protected]) if you have any questions.

‎projects/finding_donors/finding_donors.ipynb

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"| 2 | A | | 1 | 0 | 0 |\n",
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"Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n",
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" - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_raw'` data.\n",
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" - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_log_minmax_transform'` data.\n",
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" - Convert the target label `'income_raw'` to numerical entries.\n",
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" - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
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"precision = None\n",
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"# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
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"# HINT: The formula above can be written as (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall)\n",
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"fscore = None\n",
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"# Print the results \n",

‎projects/practice_projects/naive_bayes_tutorial/Naive_Bayes_tutorial.ipynb

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