Repository: theonaunheim/surgeo (language: python)

Issue:
Implement BIFSG
Would there be any interest in attempting to implement the improved BIFSG model that includes first name data as well?

See https://www.tandfonline.com/doi/full/10.1080/2330443X.2018.1427012

> While the overall magnitude of the improvement associated with BIFSG is somewhat modest, the largest improvements occur for NH Blacks, which is the group for which BISG is least accurate. Moreover, the improvement for NH Blacks is much higher where geography has low ability to distinguish NH Blacks. This aspect is particularly important as much of the research on the topic of racial/ethnic differences focuses on specific geographic areas rather than the entire United States. It is also worthwhile to note that the improvements of BIFSG over BISG are generally comparable to the improvements of BISG over simpler methods. Last but not least, when assessing the degree of improvement from BIFSG, one should consider that even the most advanced methods are likely to result in incremental improvements for Hispanics and NH Asians, given that surnames alone are highly predictive for these particular groups.

I wouldn't mind submitting a PR on this if there is interest.


Discussion / hints:
Thanks for your interest, @TheCleric .

Tentative yes. Presuming there's a relaible, public data source it shouldn't be difficult to add a new model class. I will take a look this weekend and provide an update.
Since I needed it for my own project, I went ahead and started adding it. I should have a PR for you later. The data source that the paper I linked to references is this one:

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TYJKEZ

I have already created a .ipynb notebook like you have for downloading it and transforming it.

Fix patch:
diff --git a/CHANGELOG.txt b/CHANGELOG.txt
index 8cacbc0..1cbc2ae 100644
--- a/CHANGELOG.txt
+++ b/CHANGELOG.txt
@@ -18,3 +18,7 @@ v.1.0.1:
 v.1.0.2:
 ========
 -Bugfix to address multiple rows returned for ZCTAs in multiple states.
+
+v.1.1.0:
+========
+-Addition of First Name and BIFSG models
diff --git a/README.rst b/README.rst
index 133a851..a67ace4 100644
--- a/README.rst
+++ b/README.rst
@@ -22,6 +22,9 @@ probabilities from commonly available data such as ZIP codes and surnames.
 It is inspired by the work of the Consumer Financial Protection Bureau
 (CFPB) and was initially created by Mark Elliot et al.
 
+Surgeo also has an imlementation of the Bayesian Improved First Name Surname
+Geocode (BIFSG) as created by Ioan Voicu building off of BISG.
+
 Please see the ReadTheDocs link above for information on the implementation
 itself.
 
@@ -46,7 +49,9 @@ As a Program
 ~~~~~~~~~~~~
 
 To use the GUI, simply type in "surgeo_gui" or use the Start Menu after
-installing the executable.
+installing the executable. For Mac or Linux users, ensure that you have tkinter
+setup on your python distribution:
+<https://stackoverflow.com/questions/22550068/python-not-configured-for-tk>
 
 .. code-block::
 
diff --git a/docs/source/background.rst b/docs/source/background.rst
index f47dfb8..d5598d5 100644
--- a/docs/source/background.rst
+++ b/docs/source/background.rst
@@ -16,6 +16,9 @@ with the surname "Hernandez" identify as Hispanic according to Census data.
 Similarly, over 90% of those with the last name of "Yoo" identify as Asian
 or Pacific Islander.
 
+The first name-based approach works similarly to the last name approach, with
+first name being used as the proxy for racial/ethnic probability.
+
 The location-based approach works because racial/ethnic groups often live
 in similar areas. For example, roughly 75% of Americans living in the U.S.
 Virgin Islands are Black. Similarly, Vermont is approximately 95% White.
@@ -23,22 +26,31 @@ Virgin Islands are Black. Similarly, Vermont is approximately 95% White.
 These approaches both have their strengths and weaknesses. Surname-based
 approaches are excellent at picking out Hispanic and Asian/Pacific
 Islander surnames. Surname-based approaches are comparatively weaker when
-trying to Blacks, Whites, and Native Americans by name. Location-based
-approaches are much better at discerning Black from White areas, but are
-not particularly good at separating Asian and Hispanic populations [#]_.
+trying to Blacks, Whites, and Native Americans by name. First name approaches
+can help shore up the weaknesses of a surname only approach [#]_.
+Location-based approaches are much better at discerning Black from White
+areas, but are not particularly good at separating Asian and Hispanic
+populations [#]_.
 
 It is possible to combine these approaches using `Bayesian inference`_;
-this provides result that is superior to using either surname-based or
-location-based approaches alone [#]_. To do this we take the probability
-of race given a particular surname (prior probability) and then multiply
-it by ratio of the population in a particular geographical area given that
-race. The result is an updated (posterior) probability of race given both a
-particular surname and geographic area.
+this provides result that is superior to using any approach alone [#]_.
+To do this we take the probability of race given a particular surname
+(prior probability) and then multiply it by ratio of the population in a
+particular geographical area given that race. The result is an updated
+(posterior) probability of race given both a particular surname and geographic
+area. As well by including the first name data, this probability can be further
+refined.
 
 For additional detail on how this works, please see `BISG by Example`_.
 
 .. _Bayesian inference: https://en.wikipedia.org/wiki/Bayesian_inference
 
+.. [#]
+
+     Ioan Voicu "Using First Name Information to Improve Race and Ethnicity
+     Classification". Statistics and Public Policy (2018) 5:1, 1-13,
+     `<https://www.tandfonline.com/doi/full/10.1080/2330443X.2018.1427012>`_
+
 .. [#]
 
     Consumer Financial Protection Bureau. "Using Publicly Available
diff --git a/docs/source/class_documentation.rst b/docs/source/class_documentation.rst
index ed0ad02..0ec311b 100644
--- a/docs/source/class_documentation.rst
+++ b/docs/source/class_documentation.rst
@@ -12,6 +12,22 @@ SurgeoModel
     :inherited-members:
     :noindex:
 
+BIFSGModel
+~~~~~~~~~~
+
+.. autoclass:: surgeo.models.bifsg_model.BIFSGModel
+    :members:
+    :inherited-members:
+    :noindex:
+
+FirstNameModel
+~~~~~~~~~~~~~~
+
+.. autoclass:: surgeo.models.first_name_model.FirstNameModel
+    :members:
+    :inherited-members:
+    :noindex:
+
 GeocodeModel
 ~~~~~~~~~~~~
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index bb86c56..33b23b6 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -22,8 +22,8 @@
 project = 'surgeo'
 copyright = '2020, Theo Naunheim'
 author = 'Theo Naunheim'
-release = '1.0.2'
-version = '1.0.2'
+release = '1.1.0'
+version = '1.1.0'
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/overview.rst b/docs/source/overview.rst
index 8cb8ef1..81951ef 100644
--- a/docs/source/overview.rst
+++ b/docs/source/overview.rst
@@ -2,22 +2,29 @@ Overview
 ========
 
 Surgeo is a collection of tools that allows you to determine race by using
-substitute (or "proxy") variables such as last name and ZIP code. These
-proxies are useful for determining race in contexts where this data might
+substitute (or "proxy") variables such as first name, last name and ZIP code.
+These proxies are useful for determining race in contexts where this data might
 not otherwise be available, such as in public health and fair lending.
 
 This open source software contains a number of tools for conducting this
 analysis, including:
 
-1.  Python objects for doing Geocode-based, Surname-based, and Bayesian
+1.  Python objects for doing First Name-base, Geocode-based, Surname-based,
+    Bayesian Improved First Name Surname Geocoding (BIFSG), and Bayesian
     Improved Surname Geocoding (BISG) calculations;
 2.  A command line interface (CLI) for automating batch processing; and,
 3.  A graphical user interface (GUI) for one-off batch processing.
 
 The base data for this module is sourced from publicly available 2010
 United States Census files, specifically the `Summary File 1 data set`_ and
-the `Frequently Occurring Surnames data set`_.
+the `Frequently Occurring Surnames data set`_. As well it includes data from the `Demographics aspect of first names data set`_ [#]_.
 
 .. _Summary File 1 data set: https://www.census.gov/data/datasets/2010/dec/summary-file-1.html
 
 .. _Frequently Occurring Surnames data set: https://www.census.gov/topics/population/genealogy/data/2010_surnames.html
+
+.. _Demographics aspect of first names data set: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TYJKEZ
+
+.. [#]
+     Konstantinos Tzioumis, "Data for: Demographic aspects of first names".
+     Harvard Dataverse (2018), V1 `<https://doi.org/10.7910/DVN/TYJKEZ>`_
diff --git a/docs/source/surgeo.models.rst b/docs/source/surgeo.models.rst
index a3efdc1..a52c07a 100644
--- a/docs/source/surgeo.models.rst
+++ b/docs/source/surgeo.models.rst
@@ -12,6 +12,22 @@ surgeo.models.base\_model module
    :undoc-members:
    :show-inheritance:
 
+surgeo.models.bifsg\_model module
+---------------------------------
+
+.. automodule:: surgeo.models.bifsg_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+surgeo.models.first\_name\_model module
+---------------------------------------
+
+.. automodule:: surgeo.models.first_name_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 surgeo.models.geocode\_model module
 -----------------------------------
 
diff --git a/docs/source/usage.rst b/docs/source/usage.rst
index 97406d0..8c15306 100644
--- a/docs/source/usage.rst
+++ b/docs/source/usage.rst
@@ -14,13 +14,20 @@ data being analyzed.
 
 The available models are:
 
-    1. `GeocodeModel`, which uses a series of ZIP codes and gives the
+    1. `BIFSGModel`,  which takes a series of first names, a series of
+       surnames, and a series of ZIP codes and gives the BIFSG results of
+       those inputs (e.g. someone named HECTOR DIAZ in ZIP 79902 has a 80%
+       probability of being hispanic).
+    2. `FirstNameModel`, which uses a series of first names and gives the
+       racial percentage of that first name (e.g. 92% of people with the
+       first name AARON are white).
+    3. `GeocodeModel`, which uses a series of ZIP codes and gives the
        racial percentage make up of that ZIP code (e.g. 81% of those people
        in ZIP 65201 are white).
-    2. `SurnameModel`, which uses a series of surnames and gives the racial
+    4. `SurnameModel`, which uses a series of surnames and gives the racial
        percentage of that surname (e.g. 5% of people with the surname DIAZ
        are white); and,
-    3. `SurgeoModel`, which takes a series of surnames and a series of ZIP
+    5. `SurgeoModel`, which takes a series of surnames and a series of ZIP
        codes and gives the BISG results of those inputs (e.g. someone named
        DIAZ in ZIP 65201 has a 26% probability of being white).
 
@@ -30,18 +37,23 @@ The available models are:
     import surgeo
 
     # Instatiate your model (or all three)
+    b = surgeo.BIFSGModel()
+    f = surgeo.FirstNameModel()
     g = surgeo.GeocodeModel()
     s = surgeo.SurnameModel()
     sg = surgeo.SurgeoModel()
 
     # Create pd.Series objects to analze (or load them)
-    names = pd.Series(['DIAZ', 'JOHNSON', 'WASHINGTON'])
+    first_names = pd.Series(['HECTOR', 'PHILLIP', 'JANICE'])
+    surnames = pd.Series(['DIAZ', 'JOHNSON', 'WASHINGTON'])
     zctas = pd.Series(['65201', '63144', '63110'])
 
     # Get results using the get_probabilities() function
+    b_results = b.get_probabilities(first_names, surnames, zctas)
+    f_results = f.get_probabilities(first_names)
     g_results = g.get_probabilities(zctas)
-    s_results = s.get_probabilities(names)
-    sg_results = sg.get_probabilities(names, zctas)
+    s_results = s.get_probabilities(surnames)
+    sg_results = sg.get_probabilities(surnames, zctas)
 
     # Show Surgeo results
     sg_results
@@ -80,6 +92,7 @@ they are not the defaults ("zcta5" and "name" respectively).
     $ python -m surgeo -h
 
     usage: surgeo_cli [-h] [--zcta_column ZCTA_COLUMN]
+                        [--first_name_column FIRST_NAME_COLUMN]
                         [--surname_column SURNAME_COLUMN]
                         input output type
 
@@ -87,11 +100,13 @@ they are not the defaults ("zcta5" and "name" respectively).
 
     input                 Input CSV or XLSX of data.
     output                Output CSV or XLSX of data.
-    type                  The model type being run ("sur", "geo" or "surgeo")
+    type                  The model type being run ("bifsg", "first_name", "sur", "geo" or "surgeo")
 
     optional arguments:
     -h, --help            show this help message and exit
     --zcta_column ZCTA_COLUMN
                         The input column to analyze as ZCTA/ZIP)
+    --first_name_column SURNAME_COLUMN
+                        The input column to analyze as first name")
     --surname_column SURNAME_COLUMN
                         The input column to analyze as surname")
diff --git a/scripts/fetch_first_names.ipynb b/scripts/fetch_first_names.ipynb
new file mode 100644
index 0000000..aa9314a
--- /dev/null
+++ b/scripts/fetch_first_names.ipynb
@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pathlib\n",
+    "import urllib.request\n",
+    "\n",
+    "import numpy as np\n",
+    "import openpyxl\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Background (links good as of 2021-01-22)\n",
+    "\n",
+    "This fetches data from the following Excel file:\n",
+    "\n",
+    "https://dataverse.harvard.edu/api/access/datafile/3078263?format=original&gbrecs=true\n",
+    "\n",
+    "This excel file contains race data based on the first names. The script creates dataframes from the worksheet in this excel file. It then cleans them the data.\n",
+    "\n",
+    "Information about the contents of these files may be found here:\n",
+    "\n",
+    "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TYJKEZ#\n",
+    "\n",
+    "It then the data for use in Surgeo calculations.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HARVARD_FIRST_NAME_URL = 'https://dataverse.harvard.edu/api/access/datafile/3078263?format=original&gbrecs=true'\n",
+    "\n",
+    "HARVARD_FIRST_NAME_COLUMNS = [\n",
+    "    'firstname',\n",
+    "    'obs',\n",
+    "    'pcthispanic',\n",
+    "    'pctwhite',\n",
+    "    'pctblack',\n",
+    "    'pctapi',\n",
+    "    'pctaian',\n",
+    "    'pct2prace',\n",
+    "    'NaN',\n",
+    "]\n",
+    "\n",
+    "TARGET_FIRST_NAME_COLUMNS = [\n",
+    "    'firstname',\n",
+    "    'pctwhite',\n",
+    "    'pctblack',\n",
+    "    'pctapi',\n",
+    "    'pctaian',\n",
+    "    'pct2prace',\n",
+    "    'pcthispanic',\n",
+    "]\n",
+    "\n",
+    "TARGET_COLUMN_RENAMES = {\n",
+    "    'firstname': 'name',\n",
+    "    'pcthispanic': 'hispanic',\n",
+    "    'pctwhite': 'white',\n",
+    "    'pctblack': 'black',\n",
+    "    'pctapi': 'api',\n",
+    "    'pctaian': 'native',\n",
+    "    'pct2prace': 'multiple',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download First Name Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def url_to_df(url):\n",
+    "    '''Takes the URL of a the Harvard first name excel file and converts to DF\n",
+    "    '''\n",
+    "    # Download zipfile from census URL\n",
+    "    with urllib.request.urlopen(url) as response:\n",
+    "        # Write file into BytesIO object\n",
+    "        excel_data = io.BytesIO(response.read())\n",
+    "        # Open excel data as openpyxl workbook\n",
+    "        ef = openpyxl.load_workbook(excel_data)\n",
+    "        # Get the correct worksheet\n",
+    "        ws_data = ef.get_sheet_by_name('Data')\n",
+    "        # Extract the worsheet data\n",
+    "        raw_data = ws_data.values\n",
+    "        # Get the column names\n",
+    "        cols = next(raw_data)\n",
+    "        # Get the actual data\n",
+    "        values = list(raw_data)\n",
+    "        # Create dataframe\n",
+    "        df = pd.DataFrame(values, columns=cols)\n",
+    "        return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_harvard = url_to_df(HARVARD_FIRST_NAME_URL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                  firstname     obs  pcthispanic  pctwhite  pctblack  pctapi  \\\n",
+       "4246                  ZOFIA      55        0.000   100.000     0.000   0.000   \n",
+       "4247                  ZOILA      66       86.364    12.121     1.515   0.000   \n",
+       "4248                 ZOLTAN      47        6.383    87.234     4.255   2.128   \n",
+       "4249                  ZORAN      52        0.000   100.000     0.000   0.000   \n",
+       "4250  ALL OTHER FIRST NAMES  214124        8.226    51.422    11.541  28.117   \n",
+       "\n",
+       "      pctaian  pct2prace   NaN  \n",
+       "4246    0.000      0.000  None  \n",
+       "4247    0.000      0.000  None  \n",
+       "4248    0.000      0.000  None  \n",
+       "4249    0.000      0.000  None  \n",
+       "4250    0.396      0.298  None  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>firstname</th>\n      <th>obs</th>\n      <th>pcthispanic</th>\n      <th>pctwhite</th>\n      <th>pctblack</th>\n      <th>pctapi</th>\n      <th>pctaian</th>\n      <th>pct2prace</th>\n      <th>NaN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4246</th>\n      <td>ZOFIA</td>\n      <td>55</td>\n      <td>0.000</td>\n      <td>100.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4247</th>\n      <td>ZOILA</td>\n      <td>66</td>\n      <td>86.364</td>\n      <td>12.121</td>\n      <td>1.515</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4248</th>\n      <td>ZOLTAN</td>\n      <td>47</td>\n      <td>6.383</td>\n      <td>87.234</td>\n      <td>4.255</td>\n      <td>2.128</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4249</th>\n      <td>ZORAN</td>\n      <td>52</td>\n      <td>0.000</td>\n      <td>100.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4250</th>\n      <td>ALL OTHER FIRST NAMES</td>\n      <td>214124</td>\n      <td>8.226</td>\n      <td>51.422</td>\n      <td>11.541</td>\n      <td>28.117</td>\n      <td>0.396</td>\n      <td>0.298</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "df_harvard.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clean Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_df(df):\n",
+    "    '''Change column names, set index, and convert percentages'''\n",
+    "    # Change names\n",
+    "    df.columns = HARVARD_FIRST_NAME_COLUMNS\n",
+    "    # Filter columns\n",
+    "    df = df[TARGET_FIRST_NAME_COLUMNS]\n",
+    "    # Rename columns\n",
+    "    df = df.rename(columns=TARGET_COLUMN_RENAMES)\n",
+    "    # Set index to name\n",
+    "    df = df.set_index('name')\n",
+    "    # Sort index\n",
+    "    df = df.sort_index()\n",
+    "    # Convert percentages to 0 to 1 numbers\n",
+    "    df = df / 100\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_harvard = clean_df(df_harvard)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "          white    black      api  native  multiple  hispanic\n",
+       "name                                                         \n",
+       "ZOE     0.87255  0.04902  0.03922     0.0       0.0   0.03922\n",
+       "ZOFIA   1.00000  0.00000  0.00000     0.0       0.0   0.00000\n",
+       "ZOILA   0.12121  0.01515  0.00000     0.0       0.0   0.86364\n",
+       "ZOLTAN  0.87234  0.04255  0.02128     0.0       0.0   0.06383\n",
+       "ZORAN   1.00000  0.00000  0.00000     0.0       0.0   0.00000"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>white</th>\n      <th>black</th>\n      <th>api</th>\n      <th>native</th>\n      <th>multiple</th>\n      <th>hispanic</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ZOE</th>\n      <td>0.87255</td>\n      <td>0.04902</td>\n      <td>0.03922</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.03922</td>\n    </tr>\n    <tr>\n      <th>ZOFIA</th>\n      <td>1.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>ZOILA</th>\n      <td>0.12121</td>\n      <td>0.01515</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.86364</td>\n    </tr>\n    <tr>\n      <th>ZOLTAN</th>\n      <td>0.87234</td>\n      <td>0.04255</td>\n      <td>0.02128</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.06383</td>\n    </tr>\n    <tr>\n      <th>ZORAN</th>\n      <td>1.00000</td>\n      <td>0.00000</td>\n      <td>0.00000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df_harvard.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Round to 4 digits\n",
+    "df_harvard = df_harvard.round(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "         white   black     api  native  multiple  hispanic\n",
+       "name                                                      \n",
+       "ZOE     0.8726  0.0490  0.0392     0.0       0.0    0.0392\n",
+       "ZOFIA   1.0000  0.0000  0.0000     0.0       0.0    0.0000\n",
+       "ZOILA   0.1212  0.0151  0.0000     0.0       0.0    0.8636\n",
+       "ZOLTAN  0.8723  0.0426  0.0213     0.0       0.0    0.0638\n",
+       "ZORAN   1.0000  0.0000  0.0000     0.0       0.0    0.0000"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>white</th>\n      <th>black</th>\n      <th>api</th>\n      <th>native</th>\n      <th>multiple</th>\n      <th>hispanic</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ZOE</th>\n      <td>0.8726</td>\n      <td>0.0490</td>\n      <td>0.0392</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0392</td>\n    </tr>\n    <tr>\n      <th>ZOFIA</th>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>ZOILA</th>\n      <td>0.1212</td>\n      <td>0.0151</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.8636</td>\n    </tr>\n    <tr>\n      <th>ZOLTAN</th>\n      <td>0.8723</td>\n      <td>0.0426</td>\n      <td>0.0213</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0638</td>\n    </tr>\n    <tr>\n      <th>ZORAN</th>\n      <td>1.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "df_harvard.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Write data to module as CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "current_directory = pathlib.Path().cwd()\n",
+    "project_directory = current_directory.parents[0]\n",
+    "data_directory    = project_directory / 'surgeo' / 'data'\n",
+    "path_harvard      = data_directory / 'prob_race_given_first_name_harvard.csv'\n",
+    "df_harvard.to_csv(path_harvard)"
+   ]
+  },
+  {
+   "source": [
+    "# Create the reverse mapping data"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_totals = df_harvard.sum(axis=0).divide(100.0)\n",
+    "ratio_by_column = df_harvard.divide(column_totals, axis='columns').copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           white     black       api  native  multiple  hispanic\n",
+       "name                                                            \n",
+       "ZOE     0.031176  0.020898  0.005225     0.0       0.0  0.008734\n",
+       "ZOFIA   0.035728  0.000000  0.000000     0.0       0.0  0.000000\n",
+       "ZOILA   0.004330  0.006440  0.000000     0.0       0.0  0.192423\n",
+       "ZOLTAN  0.031165  0.018168  0.002839     0.0       0.0  0.014216\n",
+       "ZORAN   0.035728  0.000000  0.000000     0.0       0.0  0.000000"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>white</th>\n      <th>black</th>\n      <th>api</th>\n      <th>native</th>\n      <th>multiple</th>\n      <th>hispanic</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ZOE</th>\n      <td>0.031176</td>\n      <td>0.020898</td>\n      <td>0.005225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.008734</td>\n    </tr>\n    <tr>\n      <th>ZOFIA</th>\n      <td>0.035728</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>ZOILA</th>\n      <td>0.004330</td>\n      <td>0.006440</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.192423</td>\n    </tr>\n    <tr>\n      <th>ZOLTAN</th>\n      <td>0.031165</td>\n      <td>0.018168</td>\n      <td>0.002839</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.014216</td>\n    </tr>\n    <tr>\n      <th>ZORAN</th>\n      <td>0.035728</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "ratio_by_column.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prob first name given race\n",
+    "rbc_path = data_directory / 'prob_first_name_given_race_harvard.csv'\n",
+    "ratio_by_column.to_csv(rbc_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/setup.py b/setup.py
index ba273c0..c42713c 100644
--- a/setup.py
+++ b/setup.py
@@ -3,13 +3,15 @@
 
 setup(
     name='surgeo',
-    version='1.0.2',
+    version='1.1.0',
     description='Bayesian Improved Surname Geocoder model',
     long_description="""
         Surgeo is an impelmentation of the Bayesian Improved Surname
         Geocode (BISG) model created by Mark N. Elliot et al. and
         incluenced by the Consumer Financial Protection Bureau's (CFPB)
-        implementation of the same.
+        implementation of the same. It also includes an implementation
+        of the Bayesian Improved First Name Surname Geocode (BIFSG) model
+        created by Ioan Voicu.
 
     """,
     author='Theo Naunheim',
diff --git a/setup_exe.py b/setup_exe.py
index d7458a4..75e167d 100644
--- a/setup_exe.py
+++ b/setup_exe.py
@@ -14,13 +14,13 @@
     # cx_Freeze setup.py
     executables=[
         Executable(
-            "./surgeo/app/surgeo_gui.py", 
+            "./surgeo/app/surgeo_gui.py",
             base='Win32GUI',
             shortcutName='surgeo_gui',
             shortcutDir='ProgramMenuFolder',
         ),
         Executable(
-            "./surgeo/app/surgeo_cli.py", 
+            "./surgeo/app/surgeo_cli.py",
             shortcutName='surgeo_cli',
             shortcutDir='ProgramMenuFolder',
         ),
@@ -34,7 +34,9 @@
         Surgeo is an impelmentation of the Bayesian Improved Surname
         Geocode (BISG) model created by Mark N. Elliot et al. and
         incluenced by the Consumer Financial Protection Bureau's (CFPB)
-        implementation of the same.
+        implementation of the same. It also includes an implementation
+        of the Bayesian Improved First Name Surname Geocode (BIFSG) model
+        created by Ioan Voicu.
 
     """,
     author='Theo Naunheim',
diff --git a/surgeo/__init__.py b/surgeo/__init__.py
index e4fe408..408f30e 100644
--- a/surgeo/__init__.py
+++ b/surgeo/__init__.py
@@ -1,7 +1,9 @@
 """Surgeo is a Bayesian Improved Geocoding Surname Analysis module."""
 
+from  surgeo.models.bifsg_model import BIFSGModel
+from surgeo.models.first_name_model import FirstNameModel
 from surgeo.models.geocode_model import GeocodeModel
 from surgeo.models.surname_model import SurnameModel
 from surgeo.models.surgeo_model import SurgeoModel
 
-VERSION = '1.0.2'
+VERSION = '1.1.0'
diff --git a/surgeo/app/surgeo_cli.py b/surgeo/app/surgeo_cli.py
index c0973fb..d28f748 100644
--- a/surgeo/app/surgeo_cli.py
+++ b/surgeo/app/surgeo_cli.py
@@ -10,6 +10,8 @@
 import surgeo
 
 from surgeo.utility.surgeo_exception import SurgeoException
+from surgeo.models.bifsg_model import BIFSGModel
+from surgeo.models.first_name_model import FirstNameModel
 from surgeo.models.geocode_model import GeocodeModel
 from surgeo.models.surgeo_model import SurgeoModel
 from surgeo.models.surname_model import SurnameModel
@@ -19,7 +21,7 @@ class SurgeoCLI(object):
     """A CLI application class to function as an executable
 
     This script adds surgeo to path and runs a simple command line script.
-    The class pulls in a parser, parsers the command line arguments as
+    The class pulls in a parser, parses the command line arguments as
     needed, loads the data, processes the data, and sends the output to a
     file. It uses the "main()" function and then uses other methods as
     helpers.
@@ -31,6 +33,7 @@ class SurgeoCLI(object):
             $ surgeo --help
 
             usage: cli.py [-h] [--zcta_column ZCTA_COLUMN]
+                          [--first_name_column FIRST_NAME_COLUMN]
                           [--surname_column SURNAME_COLUMN]
                           input output type
 
@@ -38,12 +41,14 @@ class SurgeoCLI(object):
 
             input                 Input CSV or XLSX of data.
             output                Output CSV or XLSX of data.
-            type                  The model type being run ("sur", "geo" or "surgeo")
+            type                  The model type being run ("first", "sur", "geo", "bifsg", or "surgeo")
 
             optional arguments:
             -h, --help            show this help message and exit
             --zcta_column ZCTA_COLUMN
                                 The input column to analyze as ZCTA/ZIP)
+            --first_name_column FIRST_NAME_COLUMN
+                                The input column to analyze as first name")
             --surname_column SURNAME_COLUMN
                                 The input column to analyze as surname")
 
@@ -57,6 +62,7 @@ def __init__(self):
         self._output_path = pathlib.Path(args.output)
         self._model_type = args.type.lower()
         self._zcta_col = args.zcta_column
+        self._first_col = args.first_name_column
         self._sur_col = args.surname_column
 
     def main(self):
@@ -68,7 +74,7 @@ def main(self):
         2. Take a user defined path argument and load an Excel or CSV into a
            dataframe;
         3. Route that dataframe to a speciic processing function based on the
-           "type" function argument (e.g. surname, geocoding, or surgeo);
+           "type" function argument (e.g. first_name, surname, geocoding, bifsg, or surgeo);
         4. Optional specifies the column names to analyze (if not using the
            default "zcta5" or "name" headers);
         5. Runs the appropriate algorithm and returns a new dataframe;
@@ -96,7 +102,7 @@ def _load_df(self):
         # If CSV, read read_csv()
         elif suffix == '.csv':
             df = pd.read_csv(
-                self._input_path, 
+                self._input_path,
                 skip_blank_lines=False,
             )
         # If path is unrecognized, throw error
@@ -142,6 +148,24 @@ def _run_sur(self, df):
                                       'specified.')
         return result
 
+    def _run_first(self, df):
+        """This runs a first name model for a given dataframe"""
+        # Instantiate model
+        model = FirstNameModel()
+        # If target is specified, get probabilities based on that target
+        if self._first_col is not None:
+            target = df[self._first_col]
+            result = model.get_probabilities(target)
+        # Otherwise use "name" as default (will throw error if unfound)
+        else:
+            try:
+                target = df['name']
+                result = model.get_probabilities(target)
+            except KeyError:
+                raise SurgeoException('No "name" column and no column '
+                                      'specified.')
+        return result
+
     def _run_surgeo(self, df):
         """Runs a BISG model for a given dataframe"""
         # Instantiate model
@@ -155,7 +179,7 @@ def _run_surgeo(self, df):
         # Otherwise use zcta5 for ZIP target
         else:
             geo_target = df['zcta5']
-        # If Surname target spcified, check for accuracy
+        # If Surname target specified, check for accuracy
         if self._sur_col is not None:
             sur_target = df[self._sur_col]
             try:
@@ -169,26 +193,65 @@ def _run_surgeo(self, df):
         result = model.get_probabilities(sur_target, geo_target)
         return result
 
+    def _run_bifsg(self, df):
+        """Runs a BIFSG model for a given dataframe"""
+        # Instantiate model
+        model = BIFSGModel()
+        # If ZIP target is specified, check accuracy
+        if self._zcta_col is not None:
+            try:
+                geo_target = df[self._zcta_col]
+            except KeyError:
+                raise SurgeoException(f'Column "{self._zcta_col}"" not found.')
+        # Otherwise use zcta5 for ZIP target
+        else:
+            geo_target = df['zcta5']
+        # If Surname target specified, check for accuracy
+        if self._sur_col is not None:
+            sur_target = df[self._sur_col]
+            try:
+                sur_target = df[self._sur_col]
+            except KeyError:
+                raise SurgeoException(f'Column "{self._sur_col}" not found.')
+        # Otherwise, use name for surname column
+        else:
+            sur_target = df['name']
+        # If first name target specified, check for accuracy
+        if self._first_col is not None:
+            first_target = df[self._first_col]
+            try:
+                first_target = df[self._first_col]
+            except KeyError:
+                raise SurgeoException(f'Column "{self._first_col}" not found.')
+        # Otherwise, use name for surname column
+        else:
+            first_target = df['name']
+        # Get probabilities
+        result = model.get_probabilities(first_target, sur_target, geo_target)
+        return result
+
     def _process_df(self, df):
         """Dispach function to proper model based on arguments"""
         # Get model type and create type map
         model_type = self._model_type
         type_map = {
+            'first' : self._run_first,
             'sur'   : self._run_sur,
             'geo'   : self._run_geo,
+            'bifsg' : self._run_bifsg,
             'surgeo': self._run_surgeo,
         }
         # Look up model in type_map and process
         try:
             process_func = type_map[model_type]
-            result_df = process_func(df)
-            return result_df
         # And throw error if not present
         except KeyError:
             raise SurgeoException(
                 f'"{model_type}" is not valid model type. '
-                f'Please use "sur", "geo", or "surgeo".'
+                f'Please use one of {type_map.keys()}.'
             )
+        result_df = process_func(df)
+        return result_df
 
     def _write_df(self, df):
         """Write to CSV or XLSX depending on file suffix"""
@@ -223,7 +286,7 @@ def _get_parsed_args(self):
         # Model type argument
         parser.add_argument(
             'type',
-            help='The model type being run ("sur", "geo" or "surgeo")',
+            help='The model type being run ("first", "sur", "geo", "bifsg", or "surgeo")',
         )
         # Optional zcta column argument
         parser.add_argument(
@@ -237,6 +300,12 @@ def _get_parsed_args(self):
             help='The input column to analyze as surname")',
             dest='surname_column'
         )
+        # Optional first name column argument
+        parser.add_argument(
+            '--first_name_column',
+            help='The input column to analyze as first name")',
+            dest='first_name_column'
+        )
         # Parse args and return
         parsed_args = parser.parse_args()
         return parsed_args
diff --git a/surgeo/app/surgeo_gui.py b/surgeo/app/surgeo_gui.py
index 1cbebda..b2493d6 100644
--- a/surgeo/app/surgeo_gui.py
+++ b/surgeo/app/surgeo_gui.py
@@ -14,6 +14,8 @@
 import surgeo
 
 from surgeo.utility.surgeo_exception import SurgeoException
+from surgeo.models.bifsg_model import BIFSGModel
+from surgeo.models.first_name_model import FirstNameModel
 from surgeo.models.geocode_model import GeocodeModel
 from surgeo.models.surgeo_model import SurgeoModel
 from surgeo.models.surname_model import SurnameModel
@@ -74,9 +76,9 @@ def _window_setup(self):
         self._objects['root'].bind('<Return>', self._execute)
         # Add icon
         self._objects['root'].tk.call(
-            'wm', 
-            'iconphoto', 
-            self._objects['root']._w, 
+            'wm',
+            'iconphoto',
+            self._objects['root']._w,
             tk.PhotoImage(
                 file=str(self._app_static / 'logo.gif')
             )
@@ -146,7 +148,7 @@ def _add_widgets(self):
         select_path_text.grid(row=0, column=1, padx=10, stick='w')
         # File selection button
         select_button = ttk.Button(
-            root, 
+            root,
             text='Select',
             command=self._select_input,
         )
@@ -173,7 +175,7 @@ def _add_widgets(self):
         output_path_text.grid(row=1, column=1, padx=10, sticky='w')
         # Output selection button
         output_button = ttk.Button(
-            root, 
+            root,
             text='Select',
             command=self._select_output,
         )
@@ -182,32 +184,52 @@ def _add_widgets(self):
         #######################################################################
         # Row 3
         #######################################################################
+        # First Name label
+        first_name_label = ttk.Label(
+            root,
+            text='First Name Column Header',
+        )
+        first_name_label.grid(row=2, column=0, padx=10, sticky='w')
+        self._objects['first_name_label'] = first_name_label
+        # Text entry box and associated variable
+        first_name_var = tk.StringVar()
+        self._objects['first_name_var'] = first_name_var
+        first_name_entry = ttk.Entry(
+            root,
+            text='Enter First Name Column Header',
+            textvariable=first_name_var,
+        )
+        first_name_entry.grid(row=2, column=1, padx=10, sticky='w')
+        self._objects['first_name_entry'] = first_name_entry
+        #######################################################################
+        # Row 4
+        #######################################################################
         # Surname label
         surname_label = ttk.Label(
-            root, 
-            text='Surname Column Header', 
+            root,
+            text='Surname Column Header',
         )
-        surname_label.grid(row=2, column=0, padx=10, sticky='w')
+        surname_label.grid(row=3, column=0, padx=10, sticky='w')
         self._objects['surname_label'] = surname_label
         # Text entry box and associated variable
-        name_var = tk.StringVar()
-        self._objects['name_var'] = name_var
+        surname_var = tk.StringVar()
+        self._objects['surname_var'] = surname_var
         surname_entry = ttk.Entry(
             root,
-            text='Enter Name Column Header',
-            textvariable=name_var,
+            text='Enter Surname Column Header',
+            textvariable=surname_var,
         )
-        surname_entry.grid(row=2, column=1, padx=10, sticky='w')
+        surname_entry.grid(row=3, column=1, padx=10, sticky='w')
         self._objects['surname_entry'] = surname_entry
         #######################################################################
-        # Row 4
+        # Row 5
         #######################################################################
         # ZCTA label
         zcta_label = ttk.Label(
-            root, 
-            text='ZIP/ZCTA Column Header', 
+            root,
+            text='ZIP/ZCTA Column Header',
         )
-        zcta_label.grid(row=3, column=0, padx=10, sticky='w')
+        zcta_label.grid(row=4, column=0, padx=10, sticky='w')
         self._objects['zcta_label'] = zcta_label
         # String variable and text entry box
         zip_var = tk.StringVar()
@@ -217,14 +239,14 @@ def _add_widgets(self):
             text='Enter ZIP/ZCTA Column Header',
             textvariable=zip_var,
         )
-        zcta_entry.grid(row=3, column=1, padx=10, pady=3, sticky='w')
+        zcta_entry.grid(row=4, column=1, padx=10, pady=3, sticky='w')
         self._objects['zcta_entry'] = zcta_entry
         #######################################################################
-        # Row 5
+        # Row 6
         #######################################################################
-        # Model selector label 
+        # Model selector label
         model_label = ttk.Label(root, text='Model Type')
-        model_label.grid(row=4, column=0, padx=10, sticky='w')
+        model_label.grid(row=5, column=0, padx=10, sticky='w')
         self._objects['model_label'] = model_label
         # Dropdown to select which model to use. Also create variable
         model_var = tk.StringVar()
@@ -233,46 +255,63 @@ def _add_widgets(self):
             root,
             model_var,
             'Surgeo (Surname + Geocode)',
+            'Surgeo (Surname + Geocode)',
+            'BIFSG',
+            'First Name',
             'Surname',
             'Geocode',
         )
-        model_selector.grid(row=4, column=1, padx=10, sticky='w')
+        model_selector.grid(row=5, column=1, padx=10, sticky='w')
         self._objects['model_selector'] = model_selector
         #######################################################################
-        # Row 6
+        # Row 7
         #######################################################################
         # Proces inputs button (this runs self._execute)
         # Note: this is also bound to <Enter> in the window setup func.
         execute_button = ttk.Button(
-            root, 
+            root,
             text='Execute',
             command=self._execute,
         )
-        execute_button.grid(row=5, column=2, padx=10, sticky='w')
+        execute_button.grid(row=6, column=2, padx=10, sticky='w')
         self._objects['execute_button'] = execute_button
 
     def _check_inputs(self, df):
         """Take DF and raise error if improper column names given"""
         # Create shortnames for variables
-        name_var = self._objects['name_var'].get()
+        first_name_var = self._objects['first_name_var'].get()
+        surname_var = self._objects['surname_var'].get()
         zip_var = self._objects['zip_var'].get()
         model_var = self._objects['model_var'].get()
+        # If it's first name, make sure column is there. Otherwise error.
+        if model_var == 'First Name':
+            if first_name_var not in df.columns:
+                # Otherwise raise error
+                raise SurgeoException(f'{first_name_var} not in input data.')
         # If it's geocode, make sure column is there. Otherwise error.
-        if model_var == 'Geocode':
+        elif model_var == 'Geocode':
             if zip_var not in df.columns:
                 # Otherwise raise error
-                raise SurgeoException(f'{name_var} not in input data.')
+                raise SurgeoException(f'{surname_var} not in input data.')
         # If Surname, make sure the user-specified surname column is there.
         elif model_var == 'Surname':
             # Otherwise raise error
-            if name_var not in df.columns:
-                raise SurgeoException(f'{name_var} not in input data.')
+            if surname_var not in df.columns:
+                raise SurgeoException(f'{surname_var} not in input data.')
+        # If BIFSG, make sure the user-specified columns are present.
+        elif model_var == 'BIFSG':
+            if first_name_var not in df.columns:
+                raise SurgeoException(f'{first_name_var} not in input data.')
+            if zip_var not in df.columns:
+                raise SurgeoException(f'{surname_var} not in input data.')
+            if surname_var not in df.columns:
+                raise SurgeoException(f'{surname_var} not in input data.')
         # If Surgeo, make sure both user-specified columns are present.
         else:
             if zip_var not in df.columns:
-                raise SurgeoException(f'{name_var} not in input data.')
-            if name_var not in df.columns:
-                raise SurgeoException(f'{name_var} not in input data.')
+                raise SurgeoException(f'{surname_var} not in input data.')
+            if surname_var not in df.columns:
+                raise SurgeoException(f'{surname_var} not in input data.')
 
     def _load_df(self, input_path):
         """This creates a dataframe based on self._input_path"""
@@ -294,7 +333,7 @@ def _load_df(self, input_path):
 
     def _execute(self, event=None, show_msgbox=True):
         """This takes all the user inputs and runs the analysis.
-        
+
         It can be triggered by the enter key (in which case it supplied an
         event), or it can be triggered by clicking the "Execute" button.
         The outcome in either event is identical.
@@ -307,7 +346,9 @@ def _execute(self, event=None, show_msgbox=True):
         # Output path from file selection
         output_var = self._objects['output_var'].get()
         # Surname column header from text field
-        name_var = self._objects['name_var'].get()
+        first_name_var = self._objects['first_name_var'].get()
+        # Surname column header from text field
+        surname_var = self._objects['surname_var'].get()
         # ZCTA column header from text field
         zip_var = self._objects['zip_var'].get()
         # Model being run from drop down window
@@ -320,20 +361,32 @@ def _execute(self, event=None, show_msgbox=True):
             input_df = self._load_df(input_var)
             # Ensure the inputs are OK
             self._check_inputs(input_df)
+            # If first name, run the first name model assign result to df
+            if model_var == 'BIFSG':
+                bifsg = BIFSGModel()
+                output_df = bifsg.get_probabilities(
+                    input_df[first_name_var],
+                    input_df[surname_var],
+                    input_df[zip_var]
+                )
+            # If first name, run the first name model assign result to df
+            elif model_var == 'First Name':
+                first = FirstNameModel()
+                output_df = first.get_probabilities(input_df[first_name_var])
             # If geo, run the geo model assign result to df
-            if model_var == 'Geocode':
+            elif model_var == 'Geocode':
                 geo = GeocodeModel()
                 output_df = geo.get_probabilities(input_df[zip_var])
             # If sur, run the sur model and assign result to df
-            if model_var == 'Surname':
+            elif model_var == 'Surname':
                 sur = SurnameModel()
-                output_df = sur.get_probabilities(input_df[name_var])
+                output_df = sur.get_probabilities(input_df[surname_var])
             # If surgeo, run the surgeo model and assign to df
-            if model_var == 'Surgeo (Surname + Geocode)':
+            else: # model_var == 'Surgeo (Surname + Geocode)':
                 surgeo = SurgeoModel()
                 # Note that surgeo takes two input columns unlike others
                 output_df = surgeo.get_probabilities(
-                    input_df[name_var], 
+                    input_df[surname_var],
                     input_df[zip_var]
                 )
             # If output is .xlsx, write to Excel
diff --git a/surgeo/data/prob_first_name_given_race_harvard.csv b/surgeo/data/prob_first_name_given_race_harvard.csv
new file mode 100644
index 0000000..3c6605d
--- /dev/null
+++ b/surgeo/data/prob_first_name_given_race_harvard.csv
@@ -0,0 +1,4252 @@
+name,white,black,api,native,multiple,hispanic
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diff --git a/surgeo/data/prob_race_given_first_name_harvard.csv b/surgeo/data/prob_race_given_first_name_harvard.csv
new file mode 100644
index 0000000..952b727
--- /dev/null
+++ b/surgeo/data/prob_race_given_first_name_harvard.csv
@@ -0,0 +1,4252 @@
+name,white,black,api,native,multiple,hispanic
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+YULIYA,1.0,0.0,0.0,0.0,0.0,0.0
+YUN,0.0149,0.0,0.9627,0.0075,0.0075,0.0075
+YUNG,0.0,0.0,0.9762,0.0,0.0,0.0238
+YURI,0.7654,0.0,0.1235,0.0,0.0,0.1111
+YURIY,1.0,0.0,0.0,0.0,0.0,0.0
+YVES,0.6486,0.2973,0.054,0.0,0.0,0.0
+YVETTE,0.5399,0.2141,0.016,0.0023,0.0,0.2278
+YVONNE,0.6528,0.1836,0.0525,0.0054,0.0039,0.1019
+ZACH,0.9804,0.0,0.0,0.0,0.0,0.0196
+ZACHARIAH,0.9273,0.0364,0.0,0.0182,0.0,0.0182
+ZACHARY,0.9597,0.0161,0.0099,0.0027,0.0036,0.0081
+ZACHERY,0.9697,0.0303,0.0,0.0,0.0,0.0
+ZACK,0.8788,0.0606,0.0303,0.0,0.0,0.0303
+ZAHRA,0.7838,0.027,0.1892,0.0,0.0,0.0
+ZANE,0.9583,0.0104,0.0208,0.0,0.0,0.0104
+ZBIGNIEW,1.0,0.0,0.0,0.0,0.0,0.0
+ZDZISLAW,1.0,0.0,0.0,0.0,0.0,0.0
+ZELJKO,1.0,0.0,0.0,0.0,0.0,0.0
+ZENAIDA,0.1,0.0,0.59,0.0,0.0,0.31
+ZENON,0.7895,0.0,0.0,0.0,0.0,0.2105
+ZHANNA,0.9667,0.0,0.0,0.0,0.0,0.0333
+ZHAO,0.0,0.0,1.0,0.0,0.0,0.0
+ZHE,0.0,0.0,1.0,0.0,0.0,0.0
+ZHEN,0.0,0.0,1.0,0.0,0.0,0.0
+ZHENG,0.0,0.0,0.9787,0.0,0.0,0.0213
+ZHI,0.0353,0.0,0.9412,0.0,0.0235,0.0
+ZHIGANG,0.0,0.0,1.0,0.0,0.0,0.0
+ZHIQIANG,0.0,0.0,1.0,0.0,0.0,0.0
+ZHIYONG,0.0,0.0,1.0,0.0,0.0,0.0
+ZHONG,0.0,0.0,1.0,0.0,0.0,0.0
+ZHUO,0.0,0.0,1.0,0.0,0.0,0.0
+ZI,0.0,0.0,1.0,0.0,0.0,0.0
+ZINA,0.7097,0.2258,0.0323,0.0,0.0,0.0323
+ZOE,0.8726,0.049,0.0392,0.0,0.0,0.0392
+ZOFIA,1.0,0.0,0.0,0.0,0.0,0.0
+ZOILA,0.1212,0.0151,0.0,0.0,0.0,0.8636
+ZOLTAN,0.8723,0.0426,0.0213,0.0,0.0,0.0638
+ZORAN,1.0,0.0,0.0,0.0,0.0,0.0
diff --git a/surgeo/models/__init__.py b/surgeo/models/__init__.py
index 8197eb8..6027b31 100644
--- a/surgeo/models/__init__.py
+++ b/surgeo/models/__init__.py
@@ -1,1 +1,1 @@
-"""This contains the Geocode, Surname, and Surname-Geocode models"""
+"""This contains the First Name, Geocode, Surname, Surname-Geocode, and First Name-Surname-Geocode models"""
diff --git a/surgeo/models/base_model.py b/surgeo/models/base_model.py
index 11a4ec7..a0bffbd 100644
--- a/surgeo/models/base_model.py
+++ b/surgeo/models/base_model.py
@@ -1,4 +1,4 @@
-"""Contains the base model for Surname, Geocode, and Surgeo models."""
+"""Contains the base model for First Name, Surname, Geocode, BIFSG, and Surgeo models."""
 
 import pathlib
 import string
@@ -9,7 +9,8 @@
 
 
 class BaseModel(object):
-    """Base class for the surname, geocode, and surname-geocode models.
+    """Base class for the first name, surname, geocode, bifsg, and
+    surname-geocode models.
 
     Class creation is greatly simplified by placing most of the
     funcionality wihtin a single base class and leaving only small areas
@@ -17,14 +18,14 @@ class BaseModel(object):
     operations:
 
     1. Creating functions to provide lookup dataframes; and,
-    2. Houseing normalization routines for dirty ZIP code and surname data.
+    2. Housing normalization routines for dirty ZIP code and name data.
 
     Note
     ----
-    Surnames are normalized in a manner consistent with Word et. al (2007)
+    Names are normalized in a manner consistent with Word et. al (2007)
     [#]_. This includes removing all whitespace/punctuation/digits,
     making the strings upper case, and then removing elements such as
-    "JR", "SR", "IV" from the tail of the string. AN example would be
+    "JR", "SR", "IV" from the tail of the string. An example would be
     "Dav 3idson" being translated to "DAVIDSON".
 
     ZCTAs, which serve as a proxy for ZIP codes, are normalized by simply
@@ -37,7 +38,7 @@ class BaseModel(object):
     .. [#]
 
         Word, David L., Charles D. Coleman, Robert Nunziata and Robert
-        Kominski. 2007. Demographic Aspects of Surnames from Census 2000.   
+        Kominski. 2007. Demographic Aspects of Surnames from Census 2000.
         `<http://www2.census.gov/topics/genealogy/2000surnames/surnames.pdf>`_.
 
     """
@@ -94,6 +95,27 @@ def _get_prob_race_given_surname(self):
         )
         return prob_race_given_surname
 
+    def _get_prob_race_given_first_name(self):
+        """Create dataframe of race probabilities given first names (for First)"""
+        # Create first name df (beware ... some NA values like "NAN" are names)
+        prob_race_given_first_name = pd.read_csv(
+            self._package_root / 'data' / 'prob_race_given_first_name_harvard.csv',
+            index_col='name',
+            na_values=[''],
+            keep_default_na=False,
+        )
+        return prob_race_given_first_name
+
+    def _get_prob_first_name_given_race(self):
+        """Create dataframe of first name ratios given a race (for BIFSG)"""
+        prob_first_name_given_race = pd.read_csv(
+            self._package_root / 'data' / 'prob_first_name_given_race_harvard.csv',
+            index_col='name',
+            na_values=[''],
+            keep_default_na=False,
+        )
+        return prob_first_name_given_race
+
     def _normalize_names(self, names: pd.Series) -> pd.Series:
         """Take names and run a normalization routine"""
         # Make a transalation table of unwanted characers
diff --git a/surgeo/models/bifsg_model.py b/surgeo/models/bifsg_model.py
new file mode 100644
index 0000000..21eb029
--- /dev/null
+++ b/surgeo/models/bifsg_model.py
@@ -0,0 +1,249 @@
+"""Module containing Surgeo BIFSG class"""
+
+import pandas as pd
+
+from surgeo.models.base_model import BaseModel
+from surgeo.utility.surgeo_exception import SurgeoException
+
+
+class BIFSGModel(BaseModel):
+    r"""Subclass for running a Bayesian Improved First Name Surname Geocode model.
+
+    This class:
+
+    1. Loads the appropriate first name, surname, and geocode lookup dataframes
+       upon instantiation;
+    2. Exposes a public get_probabilities() function to compute race
+       probabilities based on proxy data (namely first names, surnames and
+       ZIP codes); and,
+    3. Contains a number of helper functions for cleaning ZCTA/names,
+       multiplying probabilities, checking input values, and obtaining
+       ZCTA/name data components.
+
+    Notes
+    -----
+    The surname probability dataframe for this model is identical to that
+    used for the SurnameModel (`prob_race_given_surname_2010.csv`);
+    the first name probability dataframe for this modelis not the same as that
+    used for the FirstNameModel. his model uses the
+    `prob_first_name_given_race_harvard.csv` file, which has the percentage of
+    a particular race that uses that first name (e.g. 3% of all
+    White US citizens have the first name AARON). The FirstNameModel uses the
+    `prob_race_given_first_name_harvard.csv` file, which has the race percentages
+    for a given first name (e.g. 92% of people with the first name AARON are
+    White); the geocode probability dataframe for this model is not the same as
+    that used for the GeocodeModel. This model uses the
+    `prob_zcta_given_race_2010.csv` file, which has the percentage of
+    a particular race that falls within that ZCTA (e.g. .002% of all
+    White US citizens live within this ZIP code). The GeocodeModel uses the
+    `prob_race_given_zcta_2010.csv` file, which has the race percentages
+    for a given ZCTA (e.g. 90% of ZCTA 63144 is White).
+
+    The manner in which the first name data file was created can be found in
+    the "fetch_first_names" Jupyter notebook.
+
+    The manner in which the geography data file was created can be found in
+    the "fetch_geography" Jupyter notebook.
+
+    This is based of the following general formula from Voicu [#]_.
+
+    | :math:`q(r \mid s,f,g) = \Large \frac{u(r,s,f,g)}{u(1,s,f,g) \, + \, u(2,s,f,g) \, + \, u(3,s,f,g) \, + \, u(4,s,f,g) \, + \, u(5,s,f,g) \, + \, u(6,s,f,g)}`
+    |
+    | Where:
+    | :math:`\hspace{25px} u(r,s,f,g) = p(r \mid s) \times p(g \mid r) \times p(f \mid r)`
+    |
+    | And where:
+    | :math:`\hspace{25px} p(r \mid s)` is the probability of a selected race given surname
+    | :math:`\hspace{25px} p(g \mid r)` is the probability of a selected ZCTA of residence given race
+    | :math:`\hspace{25px} p(f \mid r)` is the probability of a selected first name given race
+    | :math:`\hspace{25px} g` is Census Block
+    | :math:`\hspace{25px} f` is First Name
+    | :math:`\hspace{25px} s` is Surname
+    | :math:`\hspace{25px} r` is Race
+    |
+    | And where:
+    | :math:`\hspace{25px} 1 \text{ is } r =` Hispanic
+    | :math:`\hspace{25px} 2 \text{ is } r =` White
+    | :math:`\hspace{25px} 3 \text{ is } r =` Black
+    | :math:`\hspace{25px} 4 \text{ is } r =` Asian or Pacific Islander
+    | :math:`\hspace{25px} 5 \text{ is } r =` American Indian / Alaska Native
+    | :math:`\hspace{25px} 6 \text{ is } r =` Multi Racial
+
+    References
+    ----------
+
+    .. [#]
+
+        Ioan Voicu "Using First Name Information to Improve Race and Ethnicity
+        Classification". Statistics and Public Policy (2018) 5:1, 1-13,
+        `<https://www.tandfonline.com/doi/full/10.1080/2330443X.2018.1427012>`_
+
+    """
+    def __init__(self):
+        super().__init__()
+        self._PROB_ZCTA_GIVEN_RACE = self._get_prob_zcta_given_race()
+        self._PROB_RACE_GIVEN_SURNAME = self._get_prob_race_given_surname()
+        self._PROB_FIRST_NAME_GIVEN_RACE = self._get_prob_first_name_given_race()
+
+    def get_probabilities(self, first_names, surnames, zctas):
+        """Obtain a set of BIFSG probabilities for first_name/surname/ZCTA
+        series
+
+        This method first takes the data and checks to see if the data is
+        formatted appropriately. It triggers the _get_surname_probs(),
+        _get_first_name_probs(), and _get_geocode_probs() helper functions to
+        merge the probabilities for the inputs with their looked-up values. It
+        then runs the _combined_probs() helper function to actually conduct the
+        data calculation and obtain the BIFSG probabilities. It finally runs
+        the _adjust_frame() method to concatenate the inputs and outputs in a
+        single convenient frame.
+
+        Parameters
+        ----------
+        first_names : pd.Series
+            A series of first names to use for the BIFSG algorithm
+        surnames : pd.Series
+            A series of surnames to use for the BIFSG algorithm
+        zctas : pd.Series
+            A series of ZIP/ZCTA codes for the BIFSG algorithm
+
+        Returns
+        -------
+        pd.DataFrame
+            Dataframe of BIFSG probability results
+
+        """
+
+        # Check inputs
+        self._check_inputs(first_names, surnames, zctas)
+
+        # Get component probabilities
+        first_name_probs = self._get_first_name_probs(first_names)
+        sur_probs = self._get_surname_probs(surnames)
+        geo_probs = self._get_geocode_probs(zctas)
+        # Run BIFSG algorithm
+        bifsg_probs = self._combined_probs(
+            first_name_probs,
+            sur_probs,
+            geo_probs
+        )
+        # Combine inputs with results and adjust as necessary
+        result = self._adjust_frame(
+            first_name_probs,
+            sur_probs,
+            geo_probs,
+            bifsg_probs,
+        )
+        return result
+
+    def _combined_probs(self,
+                        first_name_probs: pd.DataFrame,
+                        sur_probs: pd.DataFrame,
+                        geo_probs: pd.DataFrame) -> pd.DataFrame:
+        """Performs the BIFSG calculation"""
+        # Calculate each of the numerators
+        bifsg_numer = first_name_probs.iloc[:, 1:] * sur_probs.iloc[:, 1:] * \
+                      geo_probs.iloc[:, 1:]
+        # Calculate the denominator
+        bifsg_denom = bifsg_numer.sum(axis=1)
+        # Caluclate the bifsg probabilities (each num / denom)
+        bifsg_probs = bifsg_numer.div(bifsg_denom, axis=0)
+        return bifsg_probs
+
+    def _adjust_frame(self,
+                      first_name_probs: pd.DataFrame,
+                      sur_probs: pd.DataFrame,
+                      geo_probs: pd.DataFrame,
+                      bifsg_probs: pd.DataFrame) -> pd.DataFrame:
+        # Build frame from zctas, first names, surnames, and probabilities
+        bifsg_data = pd.concat([
+            geo_probs['zcta5'].to_frame(),
+            first_name_probs
+                .rename(columns={'name': 'first_name'})['first_name']
+                .to_frame(),
+            sur_probs
+                .rename(columns={'name': 'surname'})['surname']
+                .to_frame(),
+            bifsg_probs
+        ], axis=1)
+        return bifsg_data
+
+    def _check_inputs(self,
+                      first_names: pd.Series,
+                      surnames: pd.Series,
+                      zctas: pd.Series):
+        """Check first names, surnames, and ZCTAs and ensure they are the same
+        length
+        """
+        if len(first_names) != len(surnames) or len(surnames) != len(zctas):
+            err_string = (
+                f'Length mismatch. '
+                f'First Name length: {len(first_names)}. '
+                f'Surname length: {len(surnames)}. '
+                f'ZCTA length: {len(zctas)}.'
+            )
+            raise SurgeoException(err_string)
+
+    def _get_first_name_probs(self, first_names: pd.Series) -> pd.DataFrame:
+        """Normalizes ZCTAs/ZIPs and joins them to their race probs."""
+
+        # Normalize
+        normalized_first_names = (
+            self._normalize_names(first_names)
+                .to_frame()
+        )
+
+        # Merge names to dataframe, which gives probs for each name.
+        first_name_probs = normalized_first_names.merge(
+            self._PROB_FIRST_NAME_GIVEN_RACE,
+            left_on='name',
+            right_index=True,
+            how='left',
+        )
+
+        # Replace missing first names with "other" values
+        all_others = self._PROB_FIRST_NAME_GIVEN_RACE.query('name == "ALL OTHER FIRST NAMES"')
+        other_name_probs = all_others.to_dict('index')["ALL OTHER FIRST NAMES"]
+        first_name_probs = first_name_probs.fillna(value=other_name_probs)
+
+        return first_name_probs
+
+    def _get_surname_probs(self, surnames: pd.Series) -> pd.DataFrame:
+        """Normalizes names and joins names to their race probabilities."""
+
+        # Normalize names
+        normalized_names = (
+            self._normalize_names(surnames)
+                .to_frame()
+        )
+
+        # Merge names to dataframe, which gives probs for each name
+        surname_probs = normalized_names.merge(
+            self._PROB_RACE_GIVEN_SURNAME,
+            left_on='name',
+            right_index=True,
+            how='left',
+        )
+
+        # Replace missing first names with "other" values
+        all_others = self._PROB_RACE_GIVEN_SURNAME.query('name == "ALL OTHER NAMES"')
+        other_name_probs = all_others.to_dict('index')["ALL OTHER NAMES"]
+        surname_probs = surname_probs.fillna(value=other_name_probs)
+
+        return surname_probs
+
+    def _get_geocode_probs(self, zctas: pd.Series) -> pd.DataFrame:
+        """Normalizes ZCTAs/ZIPs and joins them to their race probs."""
+        # Normalize
+        normalized_zctas = (
+            self._normalize_zctas(zctas)
+                .to_frame()
+        )
+        # Merge names to dataframe, which gives probs for each name.
+        geocode_probs = normalized_zctas.merge(
+            self._PROB_ZCTA_GIVEN_RACE,
+            left_on='zcta5',
+            right_index=True,
+            how='left',
+        )
+        return geocode_probs
diff --git a/surgeo/models/first_name_model.py b/surgeo/models/first_name_model.py
new file mode 100644
index 0000000..f91787a
--- /dev/null
+++ b/surgeo/models/first_name_model.py
@@ -0,0 +1,65 @@
+"""Module containing the FirstNameModel class."""
+
+import pandas as pd
+
+from surgeo.models.base_model import BaseModel
+
+
+class FirstNameModel(BaseModel):
+    """Provides a way to look up race percentages by first name.
+
+    This class uses a get_probabilities() method to provide a simple
+    mechanism for obtaining race data. It is created using a simple join
+    of a race data table and the first names that are input.
+
+    Notes
+    -----
+    The manner in which the first name data file was created can be found in
+    the "fetch_first_names" Jupyter notebook.
+
+    The first name probability dataframe for this model is generated from the
+    `prob_race_given_first_name_harvard.csv` file.
+
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._PROB_RACE_GIVEN_FIRST_NAME = self._get_prob_race_given_first_name()
+
+    def get_probabilities(self, names):
+        """Obtain race probabilities for a set of first names.
+
+        Parameters
+        ----------
+        names : pd.Series
+            names to which to attach race probability data
+
+        Return
+        ------
+        pd.DataFrame
+            Dataframe of race probability results
+
+        """
+
+        # Clean and process names (consistent with Word et al)
+        normalized_names = (
+            self._normalize_names(names)
+                .to_frame()
+        )
+        # Do a simple join to obtain the names along with provs.
+        first_name_probs = normalized_names.merge(
+            self._PROB_RACE_GIVEN_FIRST_NAME,
+            left_on='name',
+            right_index=True,
+            how='left',
+        )
+
+        # Replace missing first names with "other" values
+        all_others = self._PROB_RACE_GIVEN_FIRST_NAME.query('name == "ALL OTHER FIRST NAMES"')
+        other_name_probs = all_others.to_dict('index')["ALL OTHER FIRST NAMES"]
+        first_name_probs = first_name_probs.fillna(value=other_name_probs)
+
+        # Rename to avoid clashes with "name"
+        first_name_probs = first_name_probs.rename(columns={'name': 'first_name'})
+
+        return first_name_probs
diff --git a/surgeo/models/geocode_model.py b/surgeo/models/geocode_model.py
index b966a1a..626ef44 100644
--- a/surgeo/models/geocode_model.py
+++ b/surgeo/models/geocode_model.py
@@ -18,7 +18,7 @@ class GeocodeModel(BaseModel):
     codes. While ZIP codes change, ZCTAs are static for a given census
     cycle. They are not identical.
 
-    The manner in which the geogrpahy data file was created can be found in
+    The manner in which the geography data file was created can be found in
     the "fetch_geography" Jupyter notebook.
 
     This does not use the same Geocode data as the Surgeo class. This model
diff --git a/surgeo/models/surgeo_model.py b/surgeo/models/surgeo_model.py
index 2a86116..23aca4c 100644
--- a/surgeo/models/surgeo_model.py
+++ b/surgeo/models/surgeo_model.py
@@ -31,7 +31,7 @@ class SurgeoModel(BaseModel):
     `prob_race_given_zcta_2010.csv` file, which has the race percentages
     for a given ZCTA (e.g. 90% of ZCTA 63144 is White).
 
-    The manner in which the geogrpahy data file was created can be found in
+    The manner in which the geography data file was created can be found in
     the "fetch_geography" Jupyter notebook.
 
     This is based of the following general formula from Elliott et al [#]_.
@@ -76,7 +76,7 @@ def get_probabilities(self, names, zctas):
         """Obtain a set of BISG probabilities for name/ZCTA series
 
         This method first takes the data and checks to see if the data is
-        formatted appropraitely. It triggers the _get_surname_probs() and
+        formatted appropriately. It triggers the _get_surname_probs() and
         _get_geocode_probs() helper function to merge the probabilities
         for the inputs with their looked-up values. It then runs the
         _combined_probs() helper function to actually conduct the data
@@ -157,6 +157,7 @@ def _get_surname_probs(self,
             self._normalize_names(names)
                 .to_frame()
         )
+
         # Merge names to dataframe, which gives probs for each name
         surname_probs = normalized_names.merge(
             self._PROB_RACE_GIVEN_SURNAME,
@@ -164,6 +165,12 @@ def _get_surname_probs(self,
             right_index=True,
             how='left',
         )
+
+        # Replace missing first names with "other" values
+        all_others = self._PROB_RACE_GIVEN_SURNAME.query('name == "ALL OTHER NAMES"')
+        other_name_probs = all_others.to_dict('index')["ALL OTHER NAMES"]
+        surname_probs = surname_probs.fillna(value=other_name_probs)
+
         return surname_probs
 
     def _get_geocode_probs(self, zctas: pd.Series) -> pd.DataFrame:
diff --git a/surgeo/models/surname_model.py b/surgeo/models/surname_model.py
index 85daebc..bd0fe47 100644
--- a/surgeo/models/surname_model.py
+++ b/surgeo/models/surname_model.py
@@ -14,7 +14,7 @@ class SurnameModel(BaseModel):
 
     Notes
     -----
-    The manner in which the surame data file was created can be found in
+    The manner in which the surname data file was created can be found in
     the "fetch_surnames" Jupyter notebook.
 
     The surname probability dataframe for this model is generated from the
@@ -46,6 +46,7 @@ def get_probabilities(self, names):
             self._normalize_names(names)
                 .to_frame()
         )
+
         # Do a simple join to obtain the names along with provs.
         surname_probs = normalized_names.merge(
             self._PROB_RACE_GIVEN_SURNAME,
@@ -53,4 +54,10 @@ def get_probabilities(self, names):
             right_index=True,
             how='left',
         )
+
+        # Replace missing first names with "other" values
+        all_others = self._PROB_RACE_GIVEN_SURNAME.query('name == "ALL OTHER NAMES"')
+        other_name_probs = all_others.to_dict('index')["ALL OTHER NAMES"]
+        surname_probs = surname_probs.fillna(value=other_name_probs)
+
         return surname_probs


Test patch:
diff --git a/tests/app/test_cli.py b/tests/app/test_cli.py
index 616871c..a43d20d 100644
--- a/tests/app/test_cli.py
+++ b/tests/app/test_cli.py
@@ -35,18 +35,21 @@ def tearDown(self):
         if pathlib.Path(self._EXCEL_OUTPUT_PATH).exists():
             os.unlink(self._EXCEL_OUTPUT_PATH)
 
-    def _compare(self, input_name, model_type, true_output_name):
+    def _compare(self, input_name, model_type, true_output_name, **kwargs):
         """"Helper function that runs the comparison"""
         # Generate input name based on input file
         input_path = str(self._DATA_FOLDER / input_name)
         # Run a process that writes to CSV output
-        subprocess.run([
-            sys.executable, 
+        subprocess_commands = [
+            sys.executable,
             self._CLI_SCRIPT,
             input_path,
             self._CSV_OUTPUT_PATH,
-            model_type
-        ], stderr=None)
+            model_type,
+        ]
+        if kwargs:
+            subprocess_commands.extend([command for kvp in [[f'--{key}', kwargs[key]] for key in kwargs.keys()] for command in kvp])
+        subprocess.run(subprocess_commands, stderr=None)
         # Read the newly generated information
         df_generated = pd.read_csv(self._CSV_OUTPUT_PATH)
         # Read the true information
@@ -56,9 +59,11 @@ def _compare(self, input_name, model_type, true_output_name):
 
     def _is_close_enough(self, df_generated, df_true):
         """Helper function to select floats, round them, and compare"""
+
         df_generated = df_generated.select_dtypes(np.float64).round(4)
         df_true = df_true.select_dtypes(np.float64).round(4)
-        self.assertTrue(df_generated.equals(df_true))          
+        
+        pd.testing.assert_frame_equal(df_generated, df_true, check_dtype=False)
 
     def test_surgeo_cli(self):
         """Test BISG model functionality of CLI"""
@@ -68,6 +73,24 @@ def test_surgeo_cli(self):
             'surgeo_output.csv',
         )
 
+    def test_bifsg_cli(self):
+        """Test bifsg model functionality of CLI"""
+        self._compare(
+            'bifsg_input.csv',
+            'bifsg',
+            'bifsg_output.csv',
+            surname_column='surname',
+            first_name_column='first_name'
+        )
+
+    def test_first_cli(self):
+        """Test first name model functionality of CLI"""
+        self._compare(
+            'first_name_input.csv',
+            'first',
+            'first_name_output.csv',
+        )
+
     def test_sur_cli(self):
         """Test surname model functionality of CLI"""
         self._compare(
@@ -90,7 +113,7 @@ def test_excel(self):
         input_path = str(self._DATA_FOLDER / 'surgeo_input.xlsx')
         # Run a process that writes to CSV output
         subprocess.run([
-            sys.executable, 
+            sys.executable,
             self._CLI_SCRIPT,
             input_path,
             self._EXCEL_OUTPUT_PATH,
diff --git a/tests/app/test_gui.py b/tests/app/test_gui.py
index 3a02708..82587dc 100644
--- a/tests/app/test_gui.py
+++ b/tests/app/test_gui.py
@@ -45,17 +45,19 @@ def tearDown(self):
             os.unlink(self._EXCEL_OUTPUT_PATH)
 
     def _run_model(self,
-                   input_name, 
-                   model, 
+                   input_name,
+                   model,
                    output_path,
-                   name_header,
+                   first_name_header,
+                   surname_header,
                    zip_header):
         """Helper function that actually runs the model"""
         # Input variables
         self._GUI._objects['input_var'].set(input_name)
         self._GUI._objects['model_var'].set(model)
         self._GUI._objects['output_var'].set(output_path)
-        self._GUI._objects['name_var'].set(name_header)
+        self._GUI._objects['first_name_var'].set(first_name_header)
+        self._GUI._objects['surname_var'].set(surname_header)
         self._GUI._objects['zip_var'].set(zip_header)
         # Update events (required)
         self._GUI._objects['root'].update()
@@ -66,20 +68,66 @@ def _is_close_enough(self, df_generated, df_true):
         """Helper function to select floats, round them, and compare"""
         df_generated = df_generated.select_dtypes(np.float64).round(4)
         df_true = df_true.select_dtypes(np.float64).round(4)
-        self.assertTrue(df_generated.equals(df_true))   
+        self.assertTrue(df_generated.equals(df_true))
+
+    def test_bifsg(self):
+        """Test GUI BIFSG functionality"""
+        INPUT = 'bifsg_input.csv'
+        MODEL = 'BIFSG'
+        TRUE_OUTPUT = 'bifsg_output.csv'
+        SURNAME_HEADER = 'surname'
+        FIRST_NAME_HEADER = 'first_name'
+        ZIP_HEADER = 'zcta5'
+        self._run_model(
+            self._DATA_FOLDER / INPUT,
+            MODEL,
+            self._CSV_OUTPUT_PATH,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
+            ZIP_HEADER,
+        )
+        # Get our data
+        df_generated = pd.read_csv(self._CSV_OUTPUT_PATH)
+        df_true = pd.read_csv(self._DATA_FOLDER / TRUE_OUTPUT)
+        # Compare values
+        self._is_close_enough(df_generated, df_true)
+
+    def test_first_name(self):
+        """Test GUI First Name functionality"""
+        INPUT = 'first_name_input.csv'
+        MODEL = 'First Name'
+        TRUE_OUTPUT = 'first_name_output.csv'
+        SURNAME_HEADER = 'surname'
+        FIRST_NAME_HEADER = 'name'
+        ZIP_HEADER = 'zcta5'
+        self._run_model(
+            self._DATA_FOLDER / INPUT,
+            MODEL,
+            self._CSV_OUTPUT_PATH,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
+            ZIP_HEADER,
+        )
+        # Get our data
+        df_generated = pd.read_csv(self._CSV_OUTPUT_PATH)
+        df_true = pd.read_csv(self._DATA_FOLDER / TRUE_OUTPUT)
+        # Compare values
+        self._is_close_enough(df_generated, df_true)
 
     def test_geocode(self):
         """Test GUI geocode functionality"""
         INPUT = 'geocode_input.csv'
         MODEL = 'Geocode'
         TRUE_OUTPUT = 'geocode_output.csv'
-        NAME_HEADER = 'name'
+        SURNAME_HEADER = 'name'
+        FIRST_NAME_HEADER = 'first_name'
         ZIP_HEADER = 'zcta5'
         self._run_model(
             self._DATA_FOLDER / INPUT,
             MODEL,
             self._CSV_OUTPUT_PATH,
-            NAME_HEADER,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
             ZIP_HEADER,
         )
         # Get our data
@@ -93,13 +141,15 @@ def test_surname(self):
         INPUT = 'surname_input.csv'
         MODEL = 'Surname'
         TRUE_OUTPUT = 'surname_output.csv'
-        NAME_HEADER = 'name'
+        SURNAME_HEADER = 'name'
+        FIRST_NAME_HEADER = 'first_name'
         ZIP_HEADER = 'zcta5'
         self._run_model(
             self._DATA_FOLDER / INPUT,
             MODEL,
             self._CSV_OUTPUT_PATH,
-            NAME_HEADER,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
             ZIP_HEADER,
         )
         # Get our data
@@ -113,13 +163,15 @@ def test_surgeo(self):
         INPUT = 'surgeo_input.csv'
         MODEL = 'Surgeo (Surname + Geocode)'
         TRUE_OUTPUT = 'surgeo_output.csv'
-        NAME_HEADER = 'name'
+        SURNAME_HEADER = 'name'
+        FIRST_NAME_HEADER = 'first_name'
         ZIP_HEADER = 'zcta5'
         self._run_model(
             self._DATA_FOLDER / INPUT,
             MODEL,
             self._CSV_OUTPUT_PATH,
-            NAME_HEADER,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
             ZIP_HEADER,
         )
         # Get our data
@@ -133,13 +185,15 @@ def test_malformed(self):
         INPUT = 'surgeo_input_misnamed.csv'
         MODEL = 'Surgeo (Surname + Geocode)'
         TRUE_OUTPUT = 'surgeo_output.csv'
-        NAME_HEADER = 'info_name'
+        SURNAME_HEADER = 'info_name'
+        FIRST_NAME_HEADER = 'info_first_name'
         ZIP_HEADER = 'info_zip'
         self._run_model(
             self._DATA_FOLDER / INPUT,
             MODEL,
             self._CSV_OUTPUT_PATH,
-            NAME_HEADER,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
             ZIP_HEADER,
         )
         # Get our data
@@ -153,13 +207,15 @@ def test_excel(self):
         INPUT = 'surgeo_input.xlsx'
         MODEL = 'Surgeo (Surname + Geocode)'
         TRUE_OUTPUT = 'surgeo_output.xlsx'
-        NAME_HEADER = 'name'
+        SURNAME_HEADER = 'name'
+        FIRST_NAME_HEADER = 'first_name'
         ZIP_HEADER = 'zcta5'
         self._run_model(
             self._DATA_FOLDER / INPUT,
             MODEL,
             self._EXCEL_OUTPUT_PATH,
-            NAME_HEADER,
+            FIRST_NAME_HEADER,
+            SURNAME_HEADER,
             ZIP_HEADER,
         )
         # Get our data
diff --git a/tests/data/bifsg_input.csv b/tests/data/bifsg_input.csv
new file mode 100644
index 0000000..89602be
--- /dev/null
+++ b/tests/data/bifsg_input.csv
@@ -0,0 +1,7 @@
+zcta5,first_name,surname
+00631,ADAM,WILSON
+63110,,
+,ADAN,
+,AISHA,
+will fail,DAT,ROBINSON
+,,HELTERBRIDLE
diff --git a/tests/data/bifsg_output.csv b/tests/data/bifsg_output.csv
new file mode 100644
index 0000000..2ff1301
--- /dev/null
+++ b/tests/data/bifsg_output.csv
@@ -0,0 +1,7 @@
+zcta5,first_name,surname,white,black,api,native,multiple,hispanic
+00631,ADAM,WILSON,0.152,0.0,0.0005,0.0,0.0,0.8476
+63110,,,0.3659,0.5071,0.0719,0.003,0.0405,0.0117
+,ADAN,,,,,,,
+,AISHA,,,,,,,
+will fail,DAT,ROBINSON,,,,,,
+,TROLLIE,HELTERBRIDLE,,,,,,
diff --git a/tests/data/first_name_input.csv b/tests/data/first_name_input.csv
new file mode 100644
index 0000000..e20a9b8
--- /dev/null
+++ b/tests/data/first_name_input.csv
@@ -0,0 +1,7 @@
+﻿name
+ADAM
+
+ADAN
+AISHA
+DAT
+TROLLIE
diff --git a/tests/data/first_name_output.csv b/tests/data/first_name_output.csv
new file mode 100644
index 0000000..a747fb8
--- /dev/null
+++ b/tests/data/first_name_output.csv
@@ -0,0 +1,7 @@
+first_name,white,black,api,native,multiple,hispanic
+ADAM,0.9615,0.0064,0.0102,0.0008,0.0013,0.0199
+,0.5142,0.1154,0.2812,0.004,0.003,0.0823
+ADAN,0.0548,0.0206,0.0068,0.0,0.0,0.9178
+AISHA,0.25,0.5972,0.0972,0.0,0.0,0.0556
+DAT,0.0172,0.0,0.9828,0.0,0.0,0.0
+TROLLIE,0.5142,0.1154,0.2812,0.004,0.003,0.0823
diff --git a/tests/data/geocode_input.csv b/tests/data/geocode_input.csv
index 79d8f88..0a31b13 100644
--- a/tests/data/geocode_input.csv
+++ b/tests/data/geocode_input.csv
@@ -1,4 +1,7 @@
 ﻿zcta5
- 631 
- 63110
+631
+63110
+
+
 will fail
+
diff --git a/tests/data/geocode_output.csv b/tests/data/geocode_output.csv
index d9d3c45..f1584f2 100644
--- a/tests/data/geocode_output.csv
+++ b/tests/data/geocode_output.csv
@@ -1,4 +1,7 @@
 zcta5,white,black,api,native,multiple,hispanic
-631,0.003239741,0,0.002159827,0,0,0.994600432
+00631,0.003239741,0,0.002159827,0,0,0.994600432
 63110,0.518377396,0.40154757,0.031772085,0.001817223,0.024268714,0.022217012
+,,,,,,
+,,,,,,
 will fail,,,,,,
+,,,,,,
diff --git a/tests/data/surgeo_input.csv b/tests/data/surgeo_input.csv
index 1935226..326964b 100644
--- a/tests/data/surgeo_input.csv
+++ b/tests/data/surgeo_input.csv
@@ -1,4 +1,7 @@
 ﻿zcta5,name
 631,WILSON
 63110,
+,
+,
 will fail,ROBINSON
+,HELTERBRIDLE
diff --git a/tests/data/surgeo_input_misnamed.csv b/tests/data/surgeo_input_misnamed.csv
index 6c426f8..b83b88e 100644
--- a/tests/data/surgeo_input_misnamed.csv
+++ b/tests/data/surgeo_input_misnamed.csv
@@ -1,4 +1,7 @@
 ﻿info_zip,info_name
 631,WILSON
 63110,
+,
+,
 will fail,ROBINSON
+,HELTERBRIDLE
diff --git a/tests/data/surgeo_output.csv b/tests/data/surgeo_output.csv
index 01e518e..4f6d389 100644
--- a/tests/data/surgeo_output.csv
+++ b/tests/data/surgeo_output.csv
@@ -1,4 +1,7 @@
 zcta5,name,white,black,api,native,multiple,hispanic
 00631,WILSON,0.022581185316828418,0.0,0.0017634417056115377,0.0,0.0,0.97565537297756
-63110,,,,,,,
+63110,,0.5871,0.3037,0.0566,0.0023,0.0315,0.0187
+,,,,,,
+,,,,,,
 will fail,ROBINSON,,,,,,
+,HELTERBRIDLE,,,,,,
diff --git a/tests/data/surgeo_output.xlsx b/tests/data/surgeo_output.xlsx
index 7eb89c0..69c2116 100644
Binary files a/tests/data/surgeo_output.xlsx and b/tests/data/surgeo_output.xlsx differ
diff --git a/tests/data/surname_input.csv b/tests/data/surname_input.csv
index fe472f8..1a5f46a 100644
--- a/tests/data/surname_input.csv
+++ b/tests/data/surname_input.csv
@@ -1,4 +1,7 @@
 ﻿name
 WILSON
 
+
+
 ROBINSON
+HELTERBRIDLE
diff --git a/tests/data/surname_output.csv b/tests/data/surname_output.csv
index 02df036..3cb04a2 100644
--- a/tests/data/surname_output.csv
+++ b/tests/data/surname_output.csv
@@ -1,4 +1,7 @@
 name,white,black,api,native,multiple,hispanic
 WILSON,0.6736,0.2599,0.006,0.0106,0.0238,0.0261
-,,,,,,
+,0.6665,0.0853,0.0797,0.0086,0.0232,0.1367
+,0.6665,0.0853,0.0797,0.0086,0.0232,0.1367
+,0.6665,0.0853,0.0797,0.0086,0.0232,0.1367
 ROBINSON,0.48700000000000004,0.4493,0.0046,0.0053,0.0277,0.0261
+HELTERBRIDLE,0.6665,0.0853,0.0797,0.0086,0.0232,0.1367
diff --git a/tests/models/test_base_model.py b/tests/models/test_base_model.py
index c3aa3f0..bc5dd71 100644
--- a/tests/models/test_base_model.py
+++ b/tests/models/test_base_model.py
@@ -25,7 +25,7 @@ class TestBaseModel(unittest.TestCase):
     _BASE_MODEL = BaseModel()
 
     _ZCTA_DF_LENGTH = 33_120
-    
+
     _SURNAME_DF_LENGTH = 162_254
 
     def test_get_prob_race_given_zcta(self):
@@ -73,4 +73,4 @@ def test_normalize_zctas(self):
 
 
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/tests/models/test_bifsg_model.py b/tests/models/test_bifsg_model.py
new file mode 100644
index 0000000..df7fc75
--- /dev/null
+++ b/tests/models/test_bifsg_model.py
@@ -0,0 +1,47 @@
+import pathlib
+import unittest
+
+import pandas as pd
+
+from surgeo.models.bifsg_model import BIFSGModel
+
+
+class TestSurgeoModel(unittest.TestCase):
+
+    _BIFSG_MODEL = BIFSGModel()
+
+    _DATA_FOLDER = pathlib.Path(__file__).resolve().parents[1] / 'data'
+
+    def test_get_probabilities(self):
+        """Test BIFSG model versus known result"""
+        # Load data
+        surname_data = pd.read_csv(
+            self._DATA_FOLDER / 'surname_input.csv',
+            skip_blank_lines=False,
+        )
+        first_name_data = pd.read_csv(
+            self._DATA_FOLDER / 'first_name_input.csv',
+            skip_blank_lines=False,
+        )
+        geocode_data = pd.read_csv(
+            self._DATA_FOLDER / 'geocode_input.csv',
+            skip_blank_lines=False,
+        )
+        # Get probs
+        result = self._BIFSG_MODEL.get_probabilities(
+            first_name_data['name'],
+            surname_data['name'],
+            geocode_data['zcta5'],
+        )
+        true_result = pd.read_csv(
+            self._DATA_FOLDER / 'bifsg_output.csv'
+        )
+        # Clean for consistency
+        result = result.round(4).fillna('')
+        true_result = true_result.round(4).fillna('')
+        # Check that all items in the series are equal
+        pd.testing.assert_frame_equal(result, true_result)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/models/test_first_name_model.py b/tests/models/test_first_name_model.py
new file mode 100644
index 0000000..594dc1c
--- /dev/null
+++ b/tests/models/test_first_name_model.py
@@ -0,0 +1,37 @@
+import pathlib
+import unittest
+
+import numpy as np
+import pandas as pd
+
+from surgeo.models.first_name_model import FirstNameModel
+
+
+class TestFirstNameModel(unittest.TestCase):
+
+    _FIRST_NAME_MODEL = FirstNameModel()
+
+    _DATA_FOLDER = pathlib.Path(__file__).resolve().parents[1] / 'data'
+
+    def test_get_probabilities(self):
+        """Test the main first name probability method"""
+        # Get data and clean it
+        input_data = pd.read_csv(
+            self._DATA_FOLDER / 'first_name_input.csv',
+            skip_blank_lines=False,
+        )
+        # Get prob
+        result = self._FIRST_NAME_MODEL.get_probabilities(input_data['name'])
+        # Get true result
+        true_result = pd.read_csv(
+            self._DATA_FOLDER / 'first_name_output.csv',
+        )
+        # Clean for consistency
+        result = result.round(4).fillna('')
+        true_result = true_result.round(4).fillna('')
+        # Check that all items in the series are equal
+        pd.testing.assert_frame_equal(result, true_result, check_dtype=False)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/models/test_geocode_model.py b/tests/models/test_geocode_model.py
index 320e860..c6489a4 100644
--- a/tests/models/test_geocode_model.py
+++ b/tests/models/test_geocode_model.py
@@ -24,11 +24,11 @@ def test_get_probabilities(self):
         result = self._GEOCODE_MODEL.get_probabilities(input_data['zcta5'])
         # Get true result
         true_result = pd.read_csv(
-            self._DATA_FOLDER / 'geocode_output.csv',             
+            self._DATA_FOLDER / 'geocode_output.csv',
         )
         # Clean for consistency
         result = result.round(4).fillna('')
-        true_result = result.round(4).fillna('')
+        true_result = true_result.round(4).fillna('')
         # Check that all items in the series are equal
         self.assertTrue(
             result.equals(true_result)
diff --git a/tests/models/test_surgeo_model.py b/tests/models/test_surgeo_model.py
index 8c87a32..9e78335 100644
--- a/tests/models/test_surgeo_model.py
+++ b/tests/models/test_surgeo_model.py
@@ -23,6 +23,7 @@ def test_get_probabilities(self):
             self._DATA_FOLDER / 'geocode_input.csv',
             skip_blank_lines=False,
         )
+
         # Get probs
         result = self._SURGEO_MODEL.get_probabilities(
             surname_data['name'],
@@ -31,14 +32,13 @@ def test_get_probabilities(self):
         true_result = pd.read_csv(
             self._DATA_FOLDER / 'surgeo_output.csv'
         )
+
         # Clean for consistency
         result = result.round(4).fillna('')
-        true_result = result.round(4).fillna('')
-        # Check that all items in the series are equal
-        self.assertTrue(
-            result.equals(true_result)
-        )
+        true_result = true_result.round(4).fillna('')
 
+        # Check that all items in the series are equal
+        pd.testing.assert_frame_equal(result, true_result, check_dtype=False)
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/tests/models/test_surname_model.py b/tests/models/test_surname_model.py
index 860d773..967e19f 100644
--- a/tests/models/test_surname_model.py
+++ b/tests/models/test_surname_model.py
@@ -20,19 +20,21 @@ def test_get_probabilities(self):
             self._DATA_FOLDER / 'surname_input.csv',
             skip_blank_lines=False,
         )
+
         # Get prob
         result = self._SURNAME_MODEL.get_probabilities(input_data['name'])
+
         # Get true result
         true_result = pd.read_csv(
-            self._DATA_FOLDER / 'surname_output.csv',   
+            self._DATA_FOLDER / 'surname_output.csv',
         )
+
         # Clean for consistency
         result = result.round(4).fillna('')
-        true_result = result.round(4).fillna('')
+        true_result = true_result.round(4).fillna('')
+
         # Check that all items in the series are equal
-        self.assertTrue(
-            result.equals(true_result)
-        )
+        pd.testing.assert_frame_equal(result, true_result, check_dtype=False)
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/tests/test_runner.py b/tests/test_runner.py
index 9a9c6a4..3f663a5 100644
--- a/tests/test_runner.py
+++ b/tests/test_runner.py
@@ -5,6 +5,8 @@
 import app.test_cli
 import app.test_gui
 import models.test_base_model
+import models.test_bifsg_model
+import models.test_first_name_model
 import models.test_geocode_model
 import models.test_surgeo_model
 import models.test_surname_model
@@ -14,6 +16,8 @@
     app.test_cli,
     app.test_gui,
     models.test_base_model,
+    models.test_bifsg_model,
+    models.test_first_name_model,
     models.test_geocode_model,
     models.test_surgeo_model,
     models.test_surname_model,