diff --git a/README.md b/README.md
index 35c006c..977072f 100755
--- a/README.md
+++ b/README.md
@@ -60,9 +60,12 @@ Returns:
 ```shell
 normalize_dataframe(df, dependencies)
 ```
-Normalizes dataframe based on the dependencies given.
+Normalizes dataframe based on the dependencies given. Keys for the newly created DataFrames can only be columns that are strings, ints, or categories. Keys are chosen according to the priority: 
+1) shortest lenghts 
+2) has "id" in some form in the name of an attribute 
+3) has attribute furthest to left in the table
 
-Returns:
+Returns:x
 
 `new_dfs` (list[pd.DataFrame]) : list of new dataframes
 
@@ -71,7 +74,7 @@ Returns:
 ```shell
 make_entityset(df, dependencies, name=None, time_index=None):
 ```
-Creates a normalized EntitySet from dataframe based on the dependencies given.
+Creates a normalized EntitySet from dataframe based on the dependencies given. Keys are chosen in the same fashion as for `normalize_dataframe`and a new index will be created if any key has more than a single attribute.
 
 Returns:
 
diff --git a/autonormalize/.DS_Store b/autonormalize/.DS_Store
index 2a25c5e..e6ef6ee 100644
Binary files a/autonormalize/.DS_Store and b/autonormalize/.DS_Store differ
diff --git a/autonormalize/autonormalize.py b/autonormalize/autonormalize.py
index 25cb844..cc3727d 100644
--- a/autonormalize/autonormalize.py
+++ b/autonormalize/autonormalize.py
@@ -4,7 +4,7 @@
 from .classes import Dependencies
 
 
-def find_dependencies(df, accuracy=0.98, rep_percent=0.85, index=None):
+def find_dependencies(df, accuracy=0.98, index=None):
     """
     Finds dependencies within dataframe df with the DFD search algorithm.
     Returns the dependencies as a Dependencies object.
@@ -17,11 +17,6 @@ def find_dependencies(df, accuracy=0.98, rep_percent=0.85, index=None):
         required in order to conclude a dependency (i.e. with accuracy = 0.98,
         0.98 of the rows must hold true the dependency LHS --> RHS)
 
-        rep_percent (0 < float <= 1.00; default = 0.85) : the maximum amount of
-        data that may be unique in order to determine a dependency (i.e. with
-        rep_percent = 0.85, if less than 15% of rows are repeated for the columns
-        in LHS + RHS, no dependency will be concluded.)
-
         index (str, optional) : name of column that is intended index of df
 
     Returns:
@@ -29,7 +24,7 @@ def find_dependencies(df, accuracy=0.98, rep_percent=0.85, index=None):
         dependencies (Dependencies) : the dependencies found in the data
         within the contraints provided
     """
-    deps = Dependencies(dfd.dfd(df, accuracy, rep_percent, index))
+    deps = Dependencies(dfd.dfd(df, accuracy, index))
     if index is None:
         prim_key = normalize.choose_index(deps.find_candidate_keys(), df)
         deps.set_prim_key(prim_key)
@@ -57,7 +52,11 @@ def normalize_dependencies(df, dependencies):
 
 def normalize_dataframe(df, dependencies):
     """
-    Normalizes a dataframe based on the dependencies given.
+    Normalizes a dataframe based on the dependencies given. Keys for the newly
+    created DataFrames can only be columns that are strings, ints, or
+    categories. Keys are chosen according to the priority:
+    1) shortest lenghts 2) has "id" in some form in the name of an attribute
+    3) has attribute furthest to left in the table
 
     Arguments:
         df (pd.DataFrame) : dataframe to split up
@@ -74,6 +73,10 @@ def normalize_dataframe(df, dependencies):
 def make_entityset(df, dependencies, name=None, time_index=None):
     """
     Creates a normalized EntitySet from df based on the dependencies given.
+    Keys for the newly created DataFrames can only be columns that are strings,
+    ints, or categories. Keys are chosen according to the priority:
+    1) shortest lenghts 2) has "id" in some form in the name of an attribute
+    3) has attribute furthest to left in the table
 
     Arguments:
         df (pd.DataFrame) : dataframe to normalize and make entity set from
@@ -107,7 +110,7 @@ def make_entityset(df, dependencies, name=None, time_index=None):
     return ft.EntitySet(name, entities, relationships)
 
 
-def auto_entityset(df, accuracy=0.98, rep_percent=0.85, index=None, name=None, time_index=None):
+def auto_entityset(df, accuracy=0.98, index=None, name=None, time_index=None):
     """
     Creates a normalized entityset from a dataframe.
 
@@ -119,11 +122,6 @@ def auto_entityset(df, accuracy=0.98, rep_percent=0.85, index=None, name=None, t
         required in order to conclude a dependency (i.e. with accuracy = 0.98,
         0.98 of the rows must hold true the dependency LHS --> RHS)
 
-        rep_percent (0 < float <= 1.00; default = 0.85) : the maximum amount of
-        data that may be unique in order to determine a dependency (i.e. with
-        rep_percent = 0.85, if less than 15% of rows are repeated for the columns
-        in LHS + RHS, no dependency will be concluded.)
-
         index (str, optional) : name of column that is intended index of df
 
         name (str, optional) : the name of created EntitySet
@@ -134,7 +132,7 @@ def auto_entityset(df, accuracy=0.98, rep_percent=0.85, index=None, name=None, t
 
         entityset (ft.EntitySet) : created entity set
     """
-    return make_entityset(df, find_dependencies(df, accuracy, rep_percent, index), name, time_index)
+    return make_entityset(df, find_dependencies(df, accuracy, index), name, time_index)
 
 
 def auto_normalize(df):
diff --git a/autonormalize/demos/AutoNormalize + FeatureTools Demo.ipynb b/autonormalize/demos/AutoNormalize + FeatureTools Demo.ipynb
index f1f92ae..f8b7b5a 100644
--- a/autonormalize/demos/AutoNormalize + FeatureTools Demo.ipynb	
+++ b/autonormalize/demos/AutoNormalize + FeatureTools Demo.ipynb	
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -146,7 +146,7 @@
        "2    1973-07-28     A  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -254,7 +254,7 @@
        "3                   1 2014-01-03 18:39:30      2834.44"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -351,7 +351,7 @@
        "3                   1 2014-01-03 17:39:30         True"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -419,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -428,7 +428,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [00:13<00:00,  1.41s/it]\n"
+      "100%|██████████| 10/10 [00:01<00:00,  7.11it/s]\n"
      ]
     },
     {
@@ -449,7 +449,7 @@
     }
    ],
    "source": [
-    "es = an.auto_entityset(transaction_df, name=\"transactions\", time_index='transaction_time')\n",
+    "es = an.auto_entityset(transaction_df, accuracy=1, name=\"transactions\", time_index='transaction_time')\n",
     "es.add_last_time_indexes()\n",
     "print(es)"
    ]
@@ -643,15 +643,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "\n",
-    "*write*\n",
-    "\n",
-    "\n",
-    "\n",
-    "In the feature matrix, let’s extract the labels and fill any missing values with zeros. Then, one-hot encode all categorical features by using encode_features().\n",
-    "\n",
-    "After preprocessing, we split the features and corresponding labels each into training and testing sets."
+    "Now we preprocess our features, and split the features and corresponding labels into training and testing sets."
    ]
   },
   {
@@ -672,6 +664,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we train a random forest classifer on the training set, and then test the models performance by evaluating predictions on the testing set."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -702,7 +701,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -725,6 +726,13 @@
     "print(classification_report(y_test, y_hat))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot is based on scores obtained by the model to illustrate which features are considered important for predictions."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
diff --git a/autonormalize/dfd.py b/autonormalize/dfd.py
index f2332bd..d6a39ba 100644
--- a/autonormalize/dfd.py
+++ b/autonormalize/dfd.py
@@ -10,7 +10,7 @@
 # run script.py  to see a couple examples
 
 
-def dfd(df, accuracy, rep_percent, index=None):
+def dfd(df, accuracy, index=None):
     """
     Main loop of DFD algorithm. It returns all the dependencies represented
     in the data in dataframe df. Refer to section 3.2 of paper for literature.
@@ -28,11 +28,6 @@ def dfd(df, accuracy, rep_percent, index=None):
         to conclude a dependency (i.e. with accuracy = 0.98, 0.98 of the rows
         must hold true the dependency LHS --> RHS)
 
-        rep_percent (0 < float <= 1.00) : the maximum amount of
-        data that may be unique in order to determine a dependency (i.e. with
-        rep_percent = 0.85, if less than 15% of rows are repeated for the columns
-        in LHS + RHS, no dependency will be concluded.)
-
     Returns:
 
         minimal_dependencies (DfdDependencies) : the minimal dependencies
@@ -49,12 +44,12 @@ def dfd(df, accuracy, rep_percent, index=None):
             non_uniq.remove(i)
             dependencies.add_unique_lhs(i)
     for i in tqdm(non_uniq):
-        lhss = find_LHSs(i, non_uniq, df, partitions, accuracy, masks, rep_percent)
+        lhss = find_LHSs(i, non_uniq, df, partitions, accuracy, masks)
         dependencies.add_LHSs(i, lhss)
     return dependencies
 
 
-def find_LHSs(rhs, attrs, df, partitions, accuracy, masks, rep_percent):
+def find_LHSs(rhs, attrs, df, partitions, accuracy, masks):
     """
     Finds all LHS sets of attributes that satisfy a dependency relation for the
     RHS attribute i. This is such that LHS --> RHS.
@@ -76,11 +71,6 @@ def find_LHSs(rhs, attrs, df, partitions, accuracy, masks, rep_percent):
 
         masks (Masks) : contains past calculated masks
 
-        rep_percent (0 < float <= 1.00) : the maximum amount of data that may be
-        unique in order to determine a dependency (i.e. with rep_percent = 0.85,
-        if less than 15% of rows are repeated for the columns in LHS + RHS, no
-        dependency will be concluded.)
-
     Returns:
         lhss (LHSs) : all the LHS that determine rhs
     """
@@ -106,7 +96,7 @@ def find_LHSs(rhs, attrs, df, partitions, accuracy, masks, rep_percent):
             else:
                 node.infer_type()
                 if node.category == 0:
-                    if compute_partitions(df, rhs, node.attrs, partitions, accuracy, masks, rep_percent):
+                    if compute_partitions(df, rhs, node.attrs, partitions, accuracy, masks):
                         if node.is_minimal():
                             min_deps.add_dep(node.attrs)
                             node.category = 2
@@ -296,7 +286,7 @@ def generate_next_seeds(max_non_deps, min_deps, lhs_attrs):
     return list(seeds)
 
 
-def compute_partitions(df, rhs, lhs_set, partitions, accuracy, masks, rep_percent):
+def compute_partitions(df, rhs, lhs_set, partitions, accuracy, masks):
     """
     Returns true if lhs_set --> rhs for dataframe df.
 
@@ -318,17 +308,12 @@ def compute_partitions(df, rhs, lhs_set, partitions, accuracy, masks, rep_percen
 
         masks (Masks) : contains past calculated masks
 
-        rep_percent (0 < float <= 1.00) : the maximum amount of data that may be
-        unique in order to determine a dependency (i.e. with rep_percent = 0.85,
-        if less than 15% of rows are repeated for the columns in LHS + RHS, no
-        dependency will be concluded.)
-
     Returns:
         is_dependency (bool) : True if is a dependency, false otherwise
     """
     # for approximate dependencies see TANE section 2.3s
     if accuracy < 1:
-        return approximate_dependencies(list(lhs_set), rhs, df, accuracy, masks, rep_percent)
+        return approximate_dependencies(list(lhs_set), rhs, df, accuracy, masks)
     part_rhs = partition(lhs_set.union(set([rhs])), df, partitions)
     # if part_rhs > df.shape[0] * rep_percent:
     #     return False
@@ -347,7 +332,7 @@ def partition(attrs, df, partitions):
     return shape
 
 
-def approximate_dependencies(lhs_set, rhs, df, accuracy, masks, rep_percent):
+def approximate_dependencies(lhs_set, rhs, df, accuracy, masks):
     """
     Checks whether the columns represented in lhs_set functionally determines the column rhs
     for the dataframe df.
diff --git a/autonormalize/profile.svg b/autonormalize/profile.svg
deleted file mode 100644
index d5f0112..0000000
--- a/autonormalize/profile.svg
+++ /dev/null
@@ -1,308 +0,0 @@
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(pandas/core/algorithms.py:1588) (87 samples, 0.10%)</title><rect x="1175" y="148" width="1" height="15" fill="rgb(242,114,38)"/><text x="1178.00" y="158.50"></text></g><g><title>is_interval_dtype (pandas/core/dtypes/common.py:569) (84 samples, 0.10%)</title><rect x="1175" y="164" width="1" height="15" fill="rgb(250,79,46)"/><text x="1178.00" y="174.50"></text></g><g><title>is_dtype (pandas/core/dtypes/dtypes.py:991) (78 samples, 0.09%)</title><rect x="1175" y="180" width="1" height="15" fill="rgb(246,106,22)"/><text x="1178.00" y="190.50"></text></g><g><title>take_nd (pandas/core/algorithms.py:1591) (78 samples, 0.09%)</title><rect x="1176" y="148" width="1" height="15" fill="rgb(221,162,13)"/><text x="1179.00" y="158.50"></text></g><g><title>take_nd (pandas/core/algorithms.py:1609) (232 samples, 0.27%)</title><rect x="1177" y="148" width="4" height="15" fill="rgb(247,190,44)"/><text x="1180.00" y="158.50"></text></g><g><title>issubclass_ (numpy/core/numerictypes.py:328) (80 samples, 0.09%)</title><rect x="1185" y="212" width="1" height="15" fill="rgb(249,172,28)"/><text x="1188.00" y="222.50"></text></g><g><title>issubdtype (numpy/core/numerictypes.py:392) (103 samples, 0.12%)</title><rect x="1185" y="196" width="1" height="15" fill="rgb(217,186,53)"/><text x="1188.00" y="206.50"></text></g><g><title>_name_get (numpy/core/_dtype.py:334) (152 samples, 0.18%)</title><rect x="1184" y="180" width="3" height="15" fill="rgb(232,210,5)"/><text x="1187.00" y="190.50"></text></g><g><title>_get_take_nd_function (pandas/core/algorithms.py:1419) (337 samples, 0.39%)</title><rect x="1182" y="164" width="5" height="15" fill="rgb(239,217,22)"/><text x="1185.00" y="174.50"></text></g><g><title>take_nd (pandas/core/algorithms.py:1654) (369 samples, 0.43%)</title><rect x="1182" y="148" width="5" height="15" fill="rgb(238,210,19)"/><text x="1185.00" y="158.50"></text></g><g><title>_list_of_series_to_arrays (pandas/core/internals/construction.py:457) (1,170 samples, 1.35%)</title><rect x="1173" y="132" width="16" height="15" fill="rgb(216,192,36)"/><text x="1176.00" y="142.50"></text></g><g><title>take_nd (pandas/core/algorithms.py:1655) (111 samples, 0.13%)</title><rect x="1187" y="148" width="2" height="15" fill="rgb(215,127,25)"/><text x="1190.00" y="158.50"></text></g><g><title>__init__ (pandas/core/frame.py:435) (1,394 samples, 1.61%)</title><rect x="1170" y="100" width="19" height="15" fill="rgb(238,3,3)"/><text x="1173.00" y="110.50"></text></g><g><title>to_arrays (pandas/core/internals/construction.py:411) (1,394 samples, 1.61%)</title><rect x="1170" y="116" width="19" height="15" fill="rgb(234,54,43)"/><text x="1173.00" y="126.50"></text></g><g><title>all (86,395 samples, 100%)</title><rect x="10" y="36" width="1180" height="15" fill="rgb(212,96,50)"/><text x="13.00" y="46.50"></text></g><g><title>&lt;module&gt; (pyspy2.py:68) (84,731 samples, 98.07%)</title><rect x="32" y="52" width="1158" height="15" fill="rgb(214,66,31)"/><text x="35.00" y="62.50">&lt;module&gt; (pyspy2.py:68)</text></g><g><title>split_dataframe (autonormalize/user_interaction.py:77) (84,727 samples, 98.07%)</title><rect x="32" y="68" width="1158" height="15" fill="rgb(213,38,23)"/><text x="35.00" y="78.50">split_dataframe (autonormalize/user_interaction.py:77)</text></g><g><title>drop_primary_dups (autonormalize/normalize.py:183) (1,412 samples, 1.63%)</title><rect x="1170" y="84" width="20" height="15" fill="rgb(254,103,18)"/><text x="1173.00" y="94.50"></text></g></g></svg>
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diff --git a/autonormalize/tests/test_dfd.py b/autonormalize/tests/test_dfd.py
index 3de4b54..1f1e832 100644
--- a/autonormalize/tests/test_dfd.py
+++ b/autonormalize/tests/test_dfd.py
@@ -42,11 +42,11 @@ def serialization_equal(dic_1, dic_2):
 def test_dfd():
     dep = {"id": [], "age": [["height"], ["id"]], "height": [["age"], ["id"]],
            "less_than_5": [["age"], ["height"], ["id"]]}
-    assert_equal_dependency_dics(dfd.dfd(df_1, 0.98, 0.85).serialize(), dep)
+    assert_equal_dependency_dics(dfd.dfd(df_1, 0.98).serialize(), dep)
 
     dep = {"A": [], "B": [["A"]], "C": [["D", "G"], ["A"]], "D": [["C", "G"], ["A"]],
            "E": [["C"], ["D", "G"], ["A"]], "F": [["B"], ["A"]], "G": [["C", "D"], ["A"]]}
-    assert_equal_dependency_dics(dfd.dfd(df_2, 0.98, 0.90).serialize(), dep)
+    assert_equal_dependency_dics(dfd.dfd(df_2, 0.98).serialize(), dep)
 
 
 def test_compute_partitions():
@@ -57,20 +57,20 @@ def test_compute_partitions():
     # c = [(a[i] + b[i])<4 for i in range(40)]
     c = [False, True, True, False, False, True, True, True, True, True, False, True, False, False, False, False, False, False, True, False, True, True, True, True, True, False, False, False, False, True, False, True, False, False, True, True, False, False, False, False]
     df = pd.DataFrame({'a': a, 'b': b, 'c': c})
-    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 1.00, mask, 0.90)
-    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.90, mask, 0.90)
+    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 1.00, mask)
+    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.90, mask)
 
-    assert not dfd.compute_partitions(df, 'a', frozenset(['c']), {}, 1.00, mask, 0.90)
-    assert not dfd.compute_partitions(df, 'a', frozenset(['c']), {}, 0.90, mask, 0.90)
+    assert not dfd.compute_partitions(df, 'a', frozenset(['c']), {}, 1.00, mask)
+    assert not dfd.compute_partitions(df, 'a', frozenset(['c']), {}, 0.90, mask)
 
     c[0] = True
     df = pd.DataFrame({'a': a, 'b': b, 'c': c})
-    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.97, mask, 0.90)
-    assert not dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.98, mask, 0.90)
+    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.97, mask)
+    assert not dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.98, mask)
     c[35] = False
     df = pd.DataFrame({'a': a, 'b': b, 'c': c})
-    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.95, mask, 0.90)
-    assert not dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.96, mask, 0.90)
+    assert dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.95, mask)
+    assert not dfd.compute_partitions(df, 'c', frozenset(['a', 'b']), {}, 0.96, mask)
 
 
 # def test_approximate_dependencies():