diff --git a/doc/source/install.rst b/doc/source/install.rst
index cecb4c8d..e39f669e 100755
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -7,7 +7,7 @@ Installation
 The SWAT package is installed using the ``pip`` command.  The requirements
 for using the binary protocol of CAS (recommended) are as follows.
 
-* **64-bit** Python 3.7 - 3.11 on Linux or Windows
+* **64-bit** Python 3.7 - 3.12 on Linux or Windows
 
 See additional shared library notes below.
 
@@ -19,7 +19,7 @@ amounts of data.  It also offers more advanced data loading from the client
 and data formatting features.
 
 To access the CAS REST interface only, you can use the pure Python code which
-runs in Python 2.7/3.5+.  You will still need Pandas installed.  While not as
+runs in Python 3.7 - 3.12.  You will still need Pandas installed.  While not as
 fast as the binary protocol, the pure Python interface is more portable.
 For more information, see :ref:`Binary vs. REST <binaryvsrest>`.
 
diff --git a/setup.py b/setup.py
index b7aab7b6..4f06b285 100755
--- a/setup.py
+++ b/setup.py
@@ -43,7 +43,7 @@ def get_file(fname):
     license='Apache v2.0 (SWAT) + SAS Additional Functionality (SAS TK)',
     packages=find_packages(),
     package_data={
-        'swat': ['lib/*/*.*', 'tests/datasources/*.*'],
+        'swat': ['lib/*/*.*', 'tests/datasources/*.*', 'readme.md'],
     },
     install_requires=[
         'pandas >= 0.16.0',
@@ -68,6 +68,7 @@ def get_file(fname):
         'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',
         'Programming Language :: Python :: 3.11',
+        'Programming Language :: Python :: 3.12',
         'Topic :: Scientific/Engineering',
     ],
 )
diff --git a/swat/readme.md b/swat/readme.md
new file mode 100644
index 00000000..d2568a80
--- /dev/null
+++ b/swat/readme.md
@@ -0,0 +1,13 @@
+For **Python 3.12 on Windows only**, the following modification was made to the `pyport.h` file while building the SWAT C extensions:  
+
+* Updated the `#define` for `ALWAYS_INLINE`
+  <br>**Previous Definition :**
+  ```c 
+  #elif defined(__GNUC__) || defined(__clang__) || defined(__INTEL_COMPILER)
+  ```
+  **Updated Definition :**
+  ```c
+  #elif defined(__GNUC__) || defined(__clang__) || defined(__INTEL_LLVM_COMPILER) || (defined(__INTEL_COMPILER) && !defined(_WIN32))
+  ```
+
+This change addresses a compiler error encountered when using the Intel compiler on Windows.
diff --git a/swat/tests/cas/test_table.py b/swat/tests/cas/test_table.py
index acf3d6e0..c339bcdc 100755
--- a/swat/tests/cas/test_table.py
+++ b/swat/tests/cas/test_table.py
@@ -878,10 +878,10 @@ def test_drop_duplicates(self):
         df_dropped = df.drop_duplicates(subset='Make')
 
         # Equivalent to pandas in size
-        self.assertEquals(len(tbl_dropped), len(df_dropped))
+        self.assertEqual(len(tbl_dropped), len(df_dropped))
         # Number of elements in 'Make' column should be same as number of unique elements
-        self.assertEquals(tbl_dropped['Make'].nunique(), len(tbl_dropped['Make']))
-        self.assertEquals(tbl_dropped['Make'].nunique(), len(tbl_dropped))
+        self.assertEqual(tbl_dropped['Make'].nunique(), len(tbl_dropped['Make']))
+        self.assertEqual(tbl_dropped['Make'].nunique(), len(tbl_dropped))
 
         # drop duplicates for multi-element subset
         tbl_dropped_multi = tbl.drop_duplicates(casout={'replace': True,
@@ -890,7 +890,7 @@ def test_drop_duplicates(self):
         df_dropped_multi = df.drop_duplicates(subset=['Origin', 'Type'])
 
         # Equivalent to pandas in size
-        self.assertEquals(len(tbl_dropped_multi), len(df_dropped_multi))
+        self.assertEqual(len(tbl_dropped_multi), len(df_dropped_multi))
 
         # We need some rows where all values for each col are duplicate
         nDuplicates = 7
@@ -915,8 +915,8 @@ def test_drop_duplicates(self):
                                                                   'name': 'drop-test-4'})
 
         # Make sure that the correct amount of rows were dropped
-        self.assertEquals(len(tbl), len(tbl_dropped_all))
-        self.assertEquals(len(duplicate_table), len(tbl_dropped_all) + nDuplicates)
+        self.assertEqual(len(tbl), len(tbl_dropped_all))
+        self.assertEqual(len(duplicate_table), len(tbl_dropped_all) + nDuplicates)
 
     def test_column_iter(self):
         df = self.get_cars_df()
@@ -3314,23 +3314,23 @@ def test_nunique(self):
         tbl_nunique = tbl.nunique()
         df_nunique = df.nunique()
         # Length of Series are equal
-        self.assertEquals(len(tbl_nunique), len(df_nunique))
+        self.assertEqual(len(tbl_nunique), len(df_nunique))
         # Indices are equal
         self.assertTrue(sorted(tbl_nunique) == sorted(df_nunique))
         # Values are equal
         for col in tbl.columns:
-            self.assertEquals(tbl_nunique[col], df_nunique[col])
+            self.assertEqual(tbl_nunique[col], df_nunique[col])
 
         # Now counting NaN
         tbl_nunique_nan = tbl.nunique(dropna=False)
         df_nunique_nan = df.nunique(dropna=False)
         # Length of Series are equal
-        self.assertEquals(len(tbl_nunique_nan), len(df_nunique_nan))
+        self.assertEqual(len(tbl_nunique_nan), len(df_nunique_nan))
         # Indices are equal
-        self.assertEquals(sorted(tbl_nunique_nan), sorted(df_nunique_nan))
+        self.assertEqual(sorted(tbl_nunique_nan), sorted(df_nunique_nan))
         # Values are equal
         for col in tbl.columns:
-            self.assertEquals(tbl_nunique_nan[col], df_nunique_nan[col])
+            self.assertEqual(tbl_nunique_nan[col], df_nunique_nan[col])
 
     def test_column_unique(self):
         df = self.get_cars_df()