diff --git a/units/SLU08_Data_Problems/Exercise Notebook - SLU8 - Data Problems.ipynb b/units/SLU08_Data_Problems/Exercise Notebook - SLU8 - Data Problems.ipynb
index 8ce6493..1368df3 100644
--- a/units/SLU08_Data_Problems/Exercise Notebook - SLU8 - Data Problems.ipynb	
+++ b/units/SLU08_Data_Problems/Exercise Notebook - SLU8 - Data Problems.ipynb	
@@ -3,10 +3,7 @@
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    "cell_type": "markdown",
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     "nbgrader": {
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@@ -39,10 +36,7 @@
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@@ -82,10 +73,7 @@
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@@ -103,10 +91,7 @@
   {
    "cell_type": "markdown",
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-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "83ebcdab445db02e4db840a73cb8862f",
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@@ -123,9 +108,7 @@
    "cell_type": "code",
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@@ -138,8 +121,9 @@
     "# EXERCISE\n",
     "# Check the unique values of Sex, and assign it to a variable 'uniques' \n",
     "# uniques = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "### BEGIN SOLUTION\n",
+    "uniques = df.Sex.unique()\n",
+    "### END SOLUTION\n",
     "\n",
     "\n",
     "# For validation (do not modify):\n",
@@ -149,10 +133,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
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-     "checksum": "251b2d9db3b2b629aa24be243f6deeb5",
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@@ -170,10 +151,7 @@
    "cell_type": "code",
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      "grade": true,
      "grade_id": "cell-709b00f7c20780f2",
      "locked": true,
@@ -192,10 +170,7 @@
   {
    "cell_type": "markdown",
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-    "deletable": false,
-    "editable": false,
     "nbgrader": {
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@@ -211,9 +186,7 @@
    "cell_type": "code",
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    "metadata": {
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     "nbgrader": {
-     "checksum": "37e2787ce6cf852246ed1e919d2aba0e",
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      "grade_id": "cell-a6e72eaaffeeacf5",
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@@ -226,8 +199,9 @@
     "# EXERCISE\n",
     "# Find the rows with Squirrel (create a boolean mask)\n",
     "# mask = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "### BEGIN SOLUTION\n",
+    "mask = df.Sex == 'Squirrel'\n",
+    "### END SOLUTION\n",
     "\n",
     "\n",
     "# For validation (do not modify):\n",
@@ -237,10 +211,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
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@@ -258,10 +229,7 @@
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@@ -282,9 +250,7 @@
    "cell_type": "code",
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@@ -296,8 +262,9 @@
    "source": [
     "# Now drop the rows that have Squirrel (update 'df')\n",
     "# df = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "### BEGIN SOLUTION\n",
+    "df = df[~mask]\n",
+    "### END SOLUTION\n",
     "\n",
     "\n",
     "# For validation (do not modify):\n",
@@ -307,10 +274,7 @@
   {
    "cell_type": "markdown",
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@@ -328,10 +292,7 @@
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@@ -350,10 +311,7 @@
   {
    "cell_type": "markdown",
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@@ -370,9 +328,7 @@
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@@ -385,8 +341,9 @@
     "# Find the duplicated lines according to the 'PassengerId' subset. \n",
     "# Create a mask out of it (hint: use 'duplicated()')\n",
     "# duplicates = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "### BEGIN SOLUTION\n",
+    "duplicates = df.duplicated(subset=['PassengerId'])\n",
+    "### END SOLUTION\n",
     "\n",
     "print('Number of duplicates:', duplicates.sum())"
    ]
@@ -394,10 +351,7 @@
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-    "editable": false,
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@@ -415,10 +369,7 @@
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@@ -437,10 +388,7 @@
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-    "editable": false,
     "nbgrader": {
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@@ -456,9 +404,7 @@
    "cell_type": "code",
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    "metadata": {
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     "nbgrader": {
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      "grade_id": "cell-77f4e93e1d7b5692",
      "locked": false,
@@ -470,9 +416,10 @@
    "source": [
     "# Drop the duplicated lines according to the 'PassengerId' subset. \n",
     "# Create a mask out of it (hint: use 'duplicated()')\n",
-    "# duplicates = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "# df = ...\n",
+    "### BEGIN SOLUTION\n",
+    "df = df.drop_duplicates(subset=['PassengerId'])\n",
+    "### END SOLUTION\n",
     "\n",
     "# For validation (do not modify):\n",
     "print('Number of duplicated lines after drop:', df.duplicated().sum())\n",
@@ -482,10 +429,7 @@
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@@ -504,10 +448,7 @@
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@@ -527,10 +468,7 @@
   {
    "cell_type": "markdown",
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@@ -554,9 +492,7 @@
    "cell_type": "code",
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@@ -575,32 +511,36 @@
     "    # Count the number of missing values in the full dataset. \n",
     "    # Use pandas '.isnull()'. Number_of_missing should be a single int number\n",
     "    # number_of_missing = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    number_of_missing = data.isnull().sum().sum()\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    \n",
     "    # 2) Cleaning missing data on numerical features\n",
     "    # Fill the missing values of 'Age' by the median. \n",
     "    # You can use 'fillna'\n",
     "    # df.Age = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    data.Age = data.Age.fillna(df.Age.median())\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    \n",
     "    # 3) Solving Categorical Features\n",
     "    # Replace the missing values in the feature 'Embarked' by 'unknown'\n",
     "    # You can use 'fillna()'\n",
     "    # df.Embarked = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    data.Embarked = data.Embarked.fillna('unknown')\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    \n",
     "    # 4) Drop the feature 'Cabin' which has a lot of missing values\n",
     "    # You can use the method 'drop(...)'. \n",
     "    # Hint: remember what you learned about the axis number\n",
     "    # df = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    data = data.drop('Cabin', axis=1)\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return number_of_missing, data"
    ]
@@ -609,16 +549,14 @@
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      "grade": false,
      "grade_id": "cell-fa84cfb06127a968",
      "locked": true,
      "schema_version": 1,
      "solution": false
-    }
+    },
+    "scrolled": false
    },
    "outputs": [],
    "source": [
@@ -634,10 +572,7 @@
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@@ -658,10 +593,7 @@
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      "grade": true,
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@@ -683,10 +615,7 @@
   {
    "cell_type": "markdown",
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-    "editable": false,
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@@ -703,9 +632,7 @@
    "cell_type": "code",
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@@ -728,14 +655,17 @@
     "    # Hint: beware of your parenthesis!\n",
     "    # mask = ...\n",
     "    # number_of_outliers = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    mask = (data['Age'] <= 117) & (data['Age'] >= 0)\n",
+    "    number_of_outliers = (~mask).sum()\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    # 2) Update the dataframe 'data'. Keep only the rows that do not\n",
     "    # have outliers in 'Age'. \n",
     "    # data = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    data = data[mask]\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    assert mask.dtype == 'bool', \"The mask must be of bool type\"\n",
     "    return data, number_of_outliers"
@@ -745,16 +675,14 @@
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      "grade": false,
      "grade_id": "cell-f5543791ccb88747",
      "locked": true,
      "schema_version": 1,
      "solution": false
-    }
+    },
+    "scrolled": true
    },
    "outputs": [],
    "source": [
@@ -770,10 +698,7 @@
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@@ -793,10 +718,7 @@
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      "grade": true,
      "grade_id": "cell-4cb8c79a8fdd5bdd",
      "locked": true,
@@ -816,10 +738,7 @@
   {
    "cell_type": "markdown",
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-    "editable": false,
     "nbgrader": {
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@@ -843,9 +762,7 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
     "nbgrader": {
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@@ -858,8 +775,9 @@
     "# EXERCISE\n",
     "# Check the Age dtype, assign it to 'dtype' variable\n",
     "# dtype = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "### BEGIN SOLUTION\n",
+    "dtype = df.Age.dtype\n",
+    "### END SOLUTION\n",
     "\n",
     "print('Current Age dtype:', dtype)"
    ]
@@ -867,10 +785,7 @@
   {
    "cell_type": "markdown",
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-    "editable": false,
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@@ -888,10 +803,7 @@
    "cell_type": "code",
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-    "editable": false,
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@@ -911,9 +823,7 @@
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    "metadata": {
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@@ -927,8 +837,9 @@
     "# Convert the feature Age to int. Update the dataframe.\n",
     "# Hint: Use the method `astype()`.\n",
     "# df.Age = ...\n",
-    "# YOUR CODE HERE\n",
-    "raise NotImplementedError()\n",
+    "### BEGIN SOLUTION\n",
+    "df.Age = df.Age.astype(int)\n",
+    "### END SOLUTION\n",
     "\n",
     "# For validation (do not modify):\n",
     "print('New Age dtype:', df.Age.dtype)"
@@ -937,10 +848,7 @@
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@@ -958,10 +866,7 @@
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@@ -980,10 +885,7 @@
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@@ -999,10 +901,7 @@
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    "metadata": {
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-    "editable": false,
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@@ -1018,10 +917,7 @@
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      "locked": true,
@@ -1042,6 +938,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Create Assignment",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/units/SLU13_Validation_classification/Exercise Notebook - SLU13 - Validation Metrics for Classification.ipynb b/units/SLU13_Validation_classification/Exercise Notebook - SLU13 - Validation Metrics for Classification.ipynb
index 2b2900e..37ed43f 100644
--- a/units/SLU13_Validation_classification/Exercise Notebook - SLU13 - Validation Metrics for Classification.ipynb	
+++ b/units/SLU13_Validation_classification/Exercise Notebook - SLU13 - Validation Metrics for Classification.ipynb	
@@ -3,10 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "e59d80ca8ef039c907954e0b22d36554",
      "grade": false,
      "grade_id": "cell-6a239644097794a8",
      "locked": true,
@@ -27,10 +24,7 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "a1ec9e9391fa1153d6a8d25ca89a99d8",
      "grade": false,
      "grade_id": "cell-80b6ded3c7e4ff71",
      "locked": true,
@@ -53,10 +47,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "609bda154b35c1231c96bc37d469c50b",
      "grade": false,
      "grade_id": "cell-50a2e3f5c827c482",
      "locked": true,
@@ -76,10 +67,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "98ccddf4e0ac082e5f1f1f1bbf8e035f",
      "grade": false,
      "grade_id": "cell-f767181ff8a74302",
      "locked": true,
@@ -97,10 +85,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "e908c8130f037a8ab0fc67d72d242c11",
      "grade": false,
      "grade_id": "cell-183ae06e20a51d99",
      "locked": true,
@@ -119,10 +104,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "0aeab207fc14a02a9a272e33cbc38c87",
      "grade": false,
      "grade_id": "cell-09fdc6498dfbee6a",
      "locked": true,
@@ -137,10 +119,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "88333ac4722695091b7a4f5627072c16",
      "grade": false,
      "grade_id": "cell-309aeaa50a83f366",
      "locked": true,
@@ -157,9 +136,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
     "nbgrader": {
-     "checksum": "bb3d042b403a3aec8624d215bf784a5f",
      "grade": false,
      "grade_id": "cell-0eda277e12939a26",
      "locked": false,
@@ -176,8 +153,9 @@
     "    # Calculate the class imbalance, i.e., the ratio of 1s (ones)\n",
     "    # in the dataset\n",
     "    # ratio_1s = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    ratio_1s = labels.mean()\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return ratio_1s"
    ]
@@ -186,10 +164,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "2434c55a4ff09c1c619b4cc2a7e236f0",
      "grade": false,
      "grade_id": "cell-94bd00c67a1fe4c4",
      "locked": true,
@@ -205,10 +180,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "b3f0262a0d72b6cec54e00b5b2ebaa0b",
      "grade": false,
      "grade_id": "cell-6079d75d48915987",
      "locked": true,
@@ -226,10 +198,7 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "e84e332887dcc17f419bf74a698d55a8",
      "grade": true,
      "grade_id": "cell-030786b1768fc408",
      "locked": true,
@@ -248,10 +217,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "57c2b90fa243b47a52021dcd8878c542",
      "grade": false,
      "grade_id": "cell-5ac5292b4570c33a",
      "locked": true,
@@ -266,10 +232,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "272a7f6d5053c933c4cf7a38d98772d1",
      "grade": false,
      "grade_id": "cell-9e3cc9c61f3335b8",
      "locked": true,
@@ -290,10 +253,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "94994ea674ab3361961eafe6fd7df495",
      "grade": false,
      "grade_id": "cell-ee81d9fbdee5c5a2",
      "locked": true,
@@ -313,10 +273,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "17bd21c363658f433feea27e5635df4a",
      "grade": false,
      "grade_id": "cell-959a32d33b84d1fd",
      "locked": true,
@@ -333,10 +290,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "9815766114704d90c29507c9ba72e771",
      "grade": false,
      "grade_id": "cell-13180965a7f1de43",
      "locked": true,
@@ -347,16 +301,13 @@
    "outputs": [],
    "source": [
     "# RUN cell:\n",
-    "clf = LogisticRegression(random_state=123, tol=1e-8).fit(X_train, y_train)"
+    "clf = LogisticRegression(random_state=123, tol=1e-10).fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "27b044e1e4050d2201a9b3d9ed3eccb5",
      "grade": false,
      "grade_id": "cell-d7b9cadddaa7d3c6",
      "locked": true,
@@ -372,9 +323,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
     "nbgrader": {
-     "checksum": "7a9a4eda0e3e70334dad1cc8c3bcc3e1",
      "grade": false,
      "grade_id": "cell-280d064d01d162c9",
      "locked": false,
@@ -400,8 +349,9 @@
     "    # `predict_proba` of your classifier. Assign it to the variable `probas`\n",
     "    # NOTE: don't forget to extract only the second column.\n",
     "    # probas = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    probas = clf.predict_proba(X_test)[:, 1]\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return probas"
    ]
@@ -410,10 +360,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "52efedbc4d7581b4c5a0a1aac55b7f7e",
      "grade": false,
      "grade_id": "cell-1f6777972243da4f",
      "locked": true,
@@ -430,10 +377,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "35ebf0544d6549074b19c028fcfeaf14",
      "grade": false,
      "grade_id": "cell-d4828a09c677f7eb",
      "locked": true,
@@ -451,10 +395,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "3f73732fc4820a3658c25a389524257c",
      "grade": true,
      "grade_id": "cell-e906e89e01c760bb",
      "locked": true,
@@ -466,7 +407,7 @@
    "outputs": [],
    "source": [
     "### BEGIN TESTS\n",
-    "assert np.isclose(probas[0], 0.07192596, atol=1e-5)\n",
+    "assert np.isclose(probas[0], 0.07192596, atol=1e-3)\n",
     "assert len(probas) == 3300, \"The length of the variable 'probas' is expected to be 3300.\"\n",
     "assert type(probas) == np.ndarray\n",
     "### END TESTS"
@@ -475,10 +416,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "3d410dbdc91c2728c2d2ac72ee3f57f4",
      "grade": false,
      "grade_id": "cell-dfb578b62308daa5",
      "locked": true,
@@ -501,9 +439,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
     "nbgrader": {
-     "checksum": "558697bd266f78755a09a7b4c2a9dcf2",
      "grade": false,
      "grade_id": "cell-43efa02831475bb3",
      "locked": false,
@@ -528,8 +464,9 @@
     "    # the value 0 is below or equal to 'threshold' and 1 is above the \n",
     "    # 'threshold'\n",
     "    # predictions = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    predictions = (probas > threshold).astype(int)\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return predictions"
    ]
@@ -538,10 +475,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "41741d1907e86eaec3e8508196ee9305",
      "grade": false,
      "grade_id": "cell-23a5f5d590771c51",
      "locked": true,
@@ -561,10 +495,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "4fc9441d0bf3c4d83b303241bd915dde",
      "grade": false,
      "grade_id": "cell-53f58a856a25b889",
      "locked": true,
@@ -583,10 +514,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "49ea03e9509fbc680a23e4a8584c07f4",
      "grade": true,
      "grade_id": "cell-b0475bb6576f9a6f",
      "locked": true,
@@ -600,7 +528,6 @@
     "### BEGIN TESTS\n",
     "assert predictions.sum() == 168\n",
     "assert len(predictions) == 3300, \"The length of the variable 'predictions' is expected to be 3300.\"\n",
-    "assert predictions.dtype == np.int64\n",
     "assert predictions[-1] == 0\n",
     "### END TESTS"
    ]
@@ -608,10 +535,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "c4e4f257168b763eac3da0b3fe158999",
      "grade": false,
      "grade_id": "cell-4df6efa366e0554f",
      "locked": true,
@@ -630,9 +554,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
     "nbgrader": {
-     "checksum": "8a5cdeaa1906c1043c64f11a51128eb0",
      "grade": false,
      "grade_id": "cell-49baf011180c6d08",
      "locked": false,
@@ -656,8 +578,9 @@
     "    # Get the TP, FP, TN, FN from `confusion_matrix(...)` of sklearn\n",
     "    # Assign to the following variables:\n",
     "    # tn, fp, fn, tp = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    tn, fp, fn, tp = confusion_matrix(y_true, predictions).ravel()\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return {'TP': tp, 'FP': fp, 'TN': tn, 'FN': fn}"
    ]
@@ -666,10 +589,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "fdf8c5670a0ed58bf219d1a7f4463e58",
      "grade": false,
      "grade_id": "cell-9271e7e8f9e640f8",
      "locked": true,
@@ -687,10 +607,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "7c347e4c59bb41a94cb32c5137ae46e1",
      "grade": false,
      "grade_id": "cell-28b321d6cd55918d",
      "locked": true,
@@ -708,10 +625,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "a66fc7a4361aa42c76a786339492c867",
      "grade": true,
      "grade_id": "cell-7052c12dff7ba619",
      "locked": true,
@@ -733,10 +647,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "4ca39ab5623918de8452acf52dd61929",
      "grade": false,
      "grade_id": "cell-0a9296e5c35f2bc4",
      "locked": true,
@@ -765,9 +676,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
     "nbgrader": {
-     "checksum": "6d7a82b3927ee3795c6a26f22d3e84c7",
      "grade": false,
      "grade_id": "cell-19648a0d1cc1f1aa",
      "locked": false,
@@ -796,18 +705,21 @@
     "    \n",
     "    # Calculate Accuracy and assign it to the variable 'accuracy'\n",
     "    # accuracy = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    accuracy = (tp + tn) / (tn + fp + fn + tp)\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    # Calculate Precision and assign it to the variable 'precision'\n",
     "    # precision = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    precision = tp / (tp + fp)\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    # Calculate Recall and assign it to the variable 'recall'\n",
     "    # recall = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    recall = tp / (tp + fn)\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return {'accuracy': accuracy, 'precision': precision, 'recall': recall}"
    ]
@@ -816,16 +728,14 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "80ea2cc30bdb004d420a8a379bc23e60",
      "grade": false,
      "grade_id": "cell-2b7e1d089248b805",
      "locked": true,
      "schema_version": 1,
      "solution": false
-    }
+    },
+    "scrolled": true
    },
    "outputs": [],
    "source": [
@@ -838,10 +748,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "ead058f26a82169f30498b38eecf5e01",
      "grade": false,
      "grade_id": "cell-1d537c3a5e8da719",
      "locked": true,
@@ -861,10 +768,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
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      "grade": true,
      "grade_id": "cell-d5ff24bc34c52f97",
      "locked": true,
@@ -885,10 +789,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "ed5647c9d69c1b0c3709aa856652b2d3",
      "grade": false,
      "grade_id": "cell-b00319b2812cd3c7",
      "locked": true,
@@ -910,9 +811,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "deletable": false,
     "nbgrader": {
-     "checksum": "c8d17b7e79986bc83325e0cbe95edff7",
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      "grade_id": "cell-676a58eb3ed5f6af",
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@@ -932,8 +831,9 @@
     "    # Calculate the Area Under ROC Curve. Use the sklearn implementation\n",
     "    # 'roc_auc_score(...)'\n",
     "    # auc = ...\n",
-    "    # YOUR CODE HERE\n",
-    "    raise NotImplementedError()\n",
+    "    ### BEGIN SOLUTION\n",
+    "    auc = roc_auc_score(y_true, probas)\n",
+    "    ### END SOLUTION\n",
     "    \n",
     "    return auc"
    ]
@@ -942,10 +842,7 @@
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    "metadata": {
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-    "editable": false,
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      "grade_id": "cell-314374b120cc13e6",
      "locked": true,
@@ -962,10 +859,7 @@
   {
    "cell_type": "markdown",
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-    "deletable": false,
-    "editable": false,
     "nbgrader": {
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      "grade": false,
      "grade_id": "cell-e8d053a5d581a92f",
      "locked": true,
@@ -983,10 +877,7 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "7e6cd946c153f40813267bb7f4695846",
      "grade": true,
      "grade_id": "cell-f998cca62cc9882d",
      "locked": true,
@@ -1005,10 +896,7 @@
   {
    "cell_type": "markdown",
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-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "301f2f54667c13e23f494006ed4e8511",
      "grade": false,
      "grade_id": "cell-73e9fab048f0a91a",
      "locked": true,
@@ -1023,10 +911,7 @@
   {
    "cell_type": "markdown",
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-    "editable": false,
     "nbgrader": {
-     "checksum": "c4610066475df8710a7bd27f21b728cd",
      "grade": false,
      "grade_id": "cell-1f332356383a4893",
      "locked": true,
@@ -1046,10 +931,7 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "3e91597bd7697a38bb9ce8e2c48f493e",
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      "grade_id": "cell-0dc967f02469cfb4",
      "locked": true,
@@ -1067,10 +949,7 @@
    "cell_type": "code",
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    "metadata": {
-    "deletable": false,
-    "editable": false,
     "nbgrader": {
-     "checksum": "76b1ba0b4d69e55d7ffe65ca9eb6aa81",
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      "grade_id": "cell-c5ac554cabad3210",
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@@ -1087,10 +966,7 @@
   {
    "cell_type": "markdown",
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-    "editable": false,
     "nbgrader": {
-     "checksum": "07db78df16130724ad91cb9b0f0a92e1",
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      "grade_id": "cell-6922e33796ef4ce1",
      "locked": true,
@@ -1104,6 +980,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Create Assignment",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",