diff --git a/requirements.txt b/requirements.txt
index 0e4e929..96a71e5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,36 +1,99 @@
-appnope==0.1.4
+anyio==4.11.0
+argon2-cffi==25.1.0
+argon2-cffi-bindings==25.1.0
+arrow==1.3.0
 asttokens==3.0.0
+async-lru==2.0.5
+attrs==25.4.0
+babel==2.17.0
+beautifulsoup4==4.14.2
+bleach==6.2.0
+certifi==2025.10.5
+cffi==2.0.0
+charset-normalizer==3.4.4
+colorama==0.4.6
 comm==0.2.3
-debugpy==1.8.16
+debugpy==1.8.17
 decorator==5.2.1
-et_xmlfile==2.0.0
-executing==2.2.0
-ipykernel==6.30.1
-ipython==9.4.0
+defusedxml==0.7.1
+executing==2.2.1
+fastjsonschema==2.21.2
+fqdn==1.5.1
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+idna==3.11
+ipykernel==7.0.1
+ipython==9.6.0
 ipython_pygments_lexers==1.1.1
+ipywidgets==8.1.7
+isoduration==20.11.0
 jedi==0.19.2
+Jinja2==3.1.6
+json5==0.12.1
+jsonpointer==3.0.0
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+jupyter==1.1.1
+jupyter-console==6.6.3
+jupyter-events==0.12.0
+jupyter-lsp==2.3.0
 jupyter_client==8.6.3
 jupyter_core==5.8.1
+jupyter_server==2.17.0
+jupyter_server_terminals==0.5.3
+jupyterlab==4.4.9
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+jupyterlab_widgets==3.0.15
+lark==1.3.0
+MarkupSafe==3.0.3
 matplotlib-inline==0.1.7
+mistune==3.1.4
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
 nest-asyncio==1.6.0
-numpy==2.3.2
-openpyxl==3.1.5
+notebook==7.4.7
+notebook_shim==0.2.4
 packaging==25.0
-pandas==2.3.1
-parso==0.8.4
-pexpect==4.9.0
-platformdirs==4.3.8
-prompt_toolkit==3.0.51
-psutil==7.0.0
-ptyprocess==0.7.0
+pandocfilters==1.5.1
+parso==0.8.5
+platformdirs==4.5.0
+prometheus_client==0.23.1
+prompt_toolkit==3.0.52
+psutil==7.1.0
 pure_eval==0.2.3
+pycparser==2.23
 Pygments==2.19.2
 python-dateutil==2.9.0.post0
-pytz==2025.2
-pyzmq==27.0.1
+python-json-logger==4.0.0
+pywin32==311
+pywinpty==3.0.2
+PyYAML==6.0.3
+pyzmq==27.1.0
+referencing==0.37.0
+requests==2.32.5
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rfc3987-syntax==1.1.0
+rpds-py==0.27.1
+Send2Trash==1.8.3
+setuptools==80.9.0
 six==1.17.0
+sniffio==1.3.1
+soupsieve==2.8
 stack-data==0.6.3
+terminado==0.18.1
+tinycss2==1.4.0
 tornado==6.5.2
 traitlets==5.14.3
-tzdata==2025.2
-wcwidth==0.2.13
+types-python-dateutil==2.9.0.20251008
+typing_extensions==4.15.0
+uri-template==1.3.0
+urllib3==2.5.0
+wcwidth==0.2.14
+webcolors==24.11.1
+webencodings==0.5.1
+websocket-client==1.9.0
+widgetsnbextension==4.0.14
diff --git a/src/notebooks/ashley.ipynb b/src/notebooks/ashley.ipynb
index 71ff571..429556e 100644
--- a/src/notebooks/ashley.ipynb
+++ b/src/notebooks/ashley.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 35,
    "id": "d11a2343",
    "metadata": {},
    "outputs": [],
@@ -33,7 +33,13 @@
     "import os\n",
     "import sys\n",
     "import re\n",
-    "import pandas.testing as pdt"
+    "import pandas.testing as pdt\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -46,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "7cd30f44",
    "metadata": {},
    "outputs": [],
@@ -146,17 +152,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "15c0e5af",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['ARC_Enrollments', 'ARC_Application', 'All_demographics_and_programs'])"
+       "dict_keys(['All_demographics_and_programs', 'ARC_Application', 'ARC_Enrollments'])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -176,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "5875ef3e",
    "metadata": {},
    "outputs": [
@@ -264,7 +270,7 @@
        "1           NaN         Reimage 21-22  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -284,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "c3c755a4",
    "metadata": {},
    "outputs": [],
@@ -296,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "fa63b693",
    "metadata": {},
    "outputs": [
@@ -384,7 +390,7 @@
        "1           NaN         Reimage 21-22  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -403,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "3a009686",
    "metadata": {},
    "outputs": [],
@@ -443,17 +449,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "ce7ffc41",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['ARC_Enrollments', 'ARC_Application', 'All_demographics_and_programs'])"
+       "dict_keys(['All_demographics_and_programs', 'ARC_Application', 'ARC_Enrollments'])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -488,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "749ae60a",
    "metadata": {},
    "outputs": [],
@@ -826,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "329da719",
    "metadata": {},
    "outputs": [
@@ -834,7 +840,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/2d/yt4_w6zn5pbfjg_jx5sdmm180000gn/T/ipykernel_99476/3987826742.py:163: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "C:\\Users\\askidmore0008\\AppData\\Local\\Temp\\ipykernel_19972\\3987826742.py:163: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
       "  self.df[col] = pd.to_datetime(\n"
      ]
     },
@@ -916,7 +922,7 @@
        "1         Reimage 21-22  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -971,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "182eac4a",
    "metadata": {},
    "outputs": [
@@ -1022,7 +1028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "82806fc9",
    "metadata": {},
    "outputs": [
@@ -1070,7 +1076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "id": "123deb70",
    "metadata": {},
    "outputs": [],
@@ -1163,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "id": "688bdf74",
    "metadata": {},
    "outputs": [
@@ -1235,17 +1241,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "id": "e5b989d4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['ARC_Enrollments', 'ARC_Application', 'All_demographics_and_programs'])"
+       "dict_keys(['All_demographics_and_programs', 'ARC_Application', 'ARC_Enrollments'])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1256,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "id": "4a72a66d",
    "metadata": {},
    "outputs": [
@@ -1390,7 +1396,7 @@
        "[2 rows x 40 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1402,7 +1408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "id": "f952beff",
    "metadata": {},
    "outputs": [
@@ -1497,7 +1503,7 @@
        "1  Successfully Completed 2022-01-01  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1517,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "id": "8d6485e5",
    "metadata": {},
    "outputs": [],
@@ -1568,7 +1574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "id": "a4dda144",
    "metadata": {},
    "outputs": [
@@ -1584,7 +1590,7 @@
        " '2025-01-01']"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1596,7 +1602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "id": "2985783d",
    "metadata": {},
    "outputs": [
@@ -1652,7 +1658,7 @@
        "2  Software Development M1     11"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1672,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "id": "4a0e9551",
    "metadata": {},
    "outputs": [],
@@ -1753,7 +1759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "id": "7e3c6bfa",
    "metadata": {},
    "outputs": [
@@ -1975,7 +1981,7 @@
        "19       User Experience  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1988,7 +1994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "id": "142e9f0d",
    "metadata": {},
    "outputs": [
@@ -2210,7 +2216,7 @@
        "19       User Experience  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2221,6 +2227,441 @@
     "completion"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7de011a3",
+   "metadata": {},
+   "source": [
+    "Regression Analysis for Completion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3968e427",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>Auto Id</th>\n",
+       "      <th>First Name</th>\n",
+       "      <th>Last Name</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Race</th>\n",
+       "      <th>Ethnicity Hispanic/Latino</th>\n",
+       "      <th>Outcome_x</th>\n",
+       "      <th>Veteran</th>\n",
+       "      <th>Ex-Offender</th>\n",
+       "      <th>Justice Involved</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Assessment ID</th>\n",
+       "      <th>EnrollmentId</th>\n",
+       "      <th>Enrollment Service Name</th>\n",
+       "      <th>Service</th>\n",
+       "      <th>Projected Start Date</th>\n",
+       "      <th>Actual Start Date</th>\n",
+       "      <th>Projected End Date</th>\n",
+       "      <th>Actual End Date</th>\n",
+       "      <th>Outcome_y</th>\n",
+       "      <th>ATP Cohort</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202107-1206</td>\n",
+       "      <td>name</td>\n",
+       "      <td>name</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202107-1206</td>\n",
+       "      <td>name</td>\n",
+       "      <td>name</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202107-1206</td>\n",
+       "      <td>name</td>\n",
+       "      <td>name</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202108-5167</td>\n",
+       "      <td>name</td>\n",
+       "      <td>name</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Successfully Completed</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202108-5171</td>\n",
+       "      <td>name</td>\n",
+       "      <td>name</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Auto Id First Name Last Name Gender                       Race  \\\n",
+       "0  202107-1206       name      name   Male  Black or African American   \n",
+       "1  202107-1206       name      name   Male  Black or African American   \n",
+       "2  202107-1206       name      name   Male  Black or African American   \n",
+       "3  202108-5167       name      name   Male                      Asian   \n",
+       "4  202108-5171       name      name   Male  Black or African American   \n",
+       "\n",
+       "   Ethnicity Hispanic/Latino               Outcome_x Veteran Ex-Offender  \\\n",
+       "0                        NaN                     NaN      No         NaN   \n",
+       "1                        NaN                     NaN      No         NaN   \n",
+       "2                        NaN                     NaN      No         NaN   \n",
+       "3                        NaN  Successfully Completed      No         NaN   \n",
+       "4                        NaN                     NaN     NaN         NaN   \n",
+       "\n",
+       "  Justice Involved  ... Assessment ID EnrollmentId Enrollment Service Name  \\\n",
+       "0              NaN  ...           NaN          NaN                     NaN   \n",
+       "1              NaN  ...           NaN          NaN                     NaN   \n",
+       "2              NaN  ...           NaN          NaN                     NaN   \n",
+       "3               No  ...           NaN          NaN                     NaN   \n",
+       "4              NaN  ...           NaN          NaN                     NaN   \n",
+       "\n",
+       "  Service  Projected Start Date  Actual Start Date Projected End Date  \\\n",
+       "0     NaN                   NaT                NaT                NaT   \n",
+       "1     NaN                   NaT                NaT                NaT   \n",
+       "2     NaN                   NaT                NaT                NaT   \n",
+       "3     NaN                   NaT                NaT                NaT   \n",
+       "4     NaN                   NaT                NaT                NaT   \n",
+       "\n",
+       "  Actual End Date  Outcome_y ATP Cohort  \n",
+       "0             NaT        NaN        NaT  \n",
+       "1             NaT        NaN        NaT  \n",
+       "2             NaT        NaN        NaT  \n",
+       "3             NaT        NaN        NaT  \n",
+       "4             NaT        NaN        NaT  \n",
+       "\n",
+       "[5 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pull the cleaned dataframes from dictionary\n",
+    "demo = dfs[\"All_demographics_and_programs\"]\n",
+    "app  = dfs[\"ARC_Application\"]\n",
+    "enr  = dfs[\"ARC_Enrollments\"]\n",
+    "\n",
+    "# Merge demographics/programs with applications\n",
+    "merged = demo.merge(app, left_on=\"Auto Id\", right_on=\"Contact: Auto Id\", how=\"left\")\n",
+    "\n",
+    "# Merge with enrollments (on Auto Id if available, otherwise on Full Name)\n",
+    "if \"Auto Id\" in enr.columns:\n",
+    "    merged = merged.merge(enr, on=\"Auto Id\", how=\"left\")\n",
+    "else:\n",
+    "    merged[\"Full Name\"] = merged[\"First Name\"].str.strip() + \" \" + merged[\"Last Name\"].str.strip()\n",
+    "    merged = merged.merge(enr, on=\"Full Name\", how=\"left\")\n",
+    "\n",
+    "merged.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "0b2fcf37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "completed\n",
+       "NaN    23631\n",
+       "1.0    21981\n",
+       "0.0     8813\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Creating a binary flag for completion\n",
+    "\n",
+    "merged[\"completed\"] = merged[\"Outcome_x\"].map({\"Successfully Completed\": 1, \"Did Not Complete\": 0, \"Partially Completed\": 0})\n",
+    "\n",
+    "merged[\"completed\"].value_counts(dropna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d1f527bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 30794 entries, 3 to 54292\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column                                Non-Null Count  Dtype  \n",
+      "---  ------                                --------------  -----  \n",
+      " 0   completed                             30794 non-null  float64\n",
+      " 1   Gender                                30451 non-null  object \n",
+      " 2   Race                                  30298 non-null  object \n",
+      " 3   Veteran                               29266 non-null  object \n",
+      " 4   Justice Involved                      22528 non-null  object \n",
+      " 5   Single Parent_x                       0 non-null      object \n",
+      " 6   Highest level of education completed  16438 non-null  object \n",
+      " 7   Employment Status                     16438 non-null  object \n",
+      " 8   Low Income                            16438 non-null  object \n",
+      " 9   Disability                            16438 non-null  object \n",
+      " 10  Homeless                              8229 non-null   object \n",
+      " 11  Program: Program Name                 30794 non-null  object \n",
+      "dtypes: float64(1), object(11)\n",
+      "memory usage: 3.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Keep non-null rows\n",
+    "model_df = merged.loc[merged[\"completed\"].notna()].copy()\n",
+    "\n",
+    "# Choosing independent variables\n",
+    "predictors = [\"Gender\", \"Race\", \"Veteran\", \"Justice Involved\", \"Single Parent_x\", \"Highest level of education completed\", \"Employment Status\", \"Low Income\", \"Disability\", \"Homeless\", \"Program: Program Name\"]\n",
+    "\n",
+    "# Create a smaller working dataset\n",
+    "model_data = model_df[[\"completed\"] + predictors].copy()\n",
+    "\n",
+    "# Looking for missing data\n",
+    "model_data.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d5e1941e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dropping Single Parent_x\n",
+    "model_data = model_data.drop(columns=[\"Single Parent_x\"])\n",
+    "\n",
+    "# Drop rows missing all independent variables\n",
+    "model_data = model_data.dropna(subset=[\"Gender\", \"Race\", \"Veteran\", \"Justice Involved\", \"Highest level of education completed\", \"Employment Status\", \"Low Income\", \"Disability\", \"Homeless\", \"Program: Program Name\"], how=\"all\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c04bc3b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "Logistic regression in Python can only handle numeric values, so I'm turning the values in each independent variable into a number.\n",
+    "One-hot encoding makes one new column for each variable and uses a 1 or 0 to mark whether the category applies.\n",
+    "Drop one category for each variable so that there isn't redundancy (i.e., if there are two value options and option 1 is false then option 2 is true by default and doesn't need its own column).\n",
+    "'''\n",
+    "X = pd.get_dummies(model_data.drop(columns=[\"completed\"]), drop_first=True, dtype=float)\n",
+    "\n",
+    "y = model_data[\"completed\"] #dependent variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56504bbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top positive coefficients (increase completion odds):\n",
+      "                                             Variable  Coefficient\n",
+      "76        Program: Program Name_Tech Louisville 20-21     0.813511\n",
+      "66  Program: Program Name_Connecting Young Adults ...     0.505952\n",
+      "72                Program: Program Name_Reimage 21-22     0.408321\n",
+      "67                   Program: Program Name_Metro 2025     0.396370\n",
+      "75                Program: Program Name_Reimage 24-25     0.384239\n",
+      "37                                         Race_White     0.376262\n",
+      "58          Program: Program Name_Code Kentucky 23-24     0.323945\n",
+      "68                  Program: Program Name_Metro 24-25     0.320938\n",
+      "74                Program: Program Name_Reimage 23-24     0.319116\n",
+      "73                Program: Program Name_Reimage 22-23     0.291004\n",
+      "\n",
+      "Top negative coefficients (decrease completion odds):\n",
+      "                                             Variable  Coefficient\n",
+      "18  Race_Asian; Native Hawaiian or Other Pacific I...    -0.014926\n",
+      "26              Race_Black or African American; Other    -0.016164\n",
+      "51  Highest level of education completed_Vocationa...    -0.033775\n",
+      "54                    Employment Status_Self-employed    -0.038787\n",
+      "12  Race_American Indian or Alaskan Native; Native...    -0.041968\n",
+      "30       Race_Black or African American; White; Other    -0.042773\n",
+      "61        Program: Program Name_Code Louisville 21-22    -0.053759\n",
+      "60        Program: Program Name_Code Louisville 20-21    -0.060502\n",
+      "42  Highest level of education completed_12th grad...    -0.072291\n",
+      "11  Race_American Indian or Alaskan Native; Black ...    -0.178995\n"
+     ]
+    }
+   ],
+   "source": [
+    "'''When I tried running the regression model, I got an error because some columns are colinear (matching),\n",
+    "so I had to do some extra work first to avoid this problem.'''\n",
+    "\n",
+    "# Drop any column that’s constant (all 0s or all 1s)\n",
+    "X = X.loc[:, X.nunique() > 1]\n",
+    "\n",
+    "# Drop any columns that are exact duplicates of others\n",
+    "X = X.loc[:, ~X.T.duplicated(keep='first')]\n",
+    "\n",
+    "# Simplify the independent variables\n",
+    "simple_cols = [col for col in X.columns\n",
+    "    if col.startswith(\"Gender_\")\n",
+    "    or col.startswith(\"Race_\")\n",
+    "    or col.startswith(\"Highest level of education completed_\")\n",
+    "    or col.startswith(\"Employment Status_\")\n",
+    "    or col.startswith(\"Program: Program Name_\")]\n",
+    "\n",
+    "X_simple = X[simple_cols]\n",
+    "\n",
+    "# Running regression model using sklearn logistic regression with regularization to prevent colinearity\n",
+    "model = make_pipeline(\n",
+    "    StandardScaler(with_mean=False),\n",
+    "    LogisticRegression(max_iter=1000, solver='liblinear')\n",
+    ")\n",
+    "\n",
+    "# Fit the model\n",
+    "model.fit(X_simple, y)\n",
+    "\n",
+    "# Create a table of coefficients\n",
+    "coef_df = pd.DataFrame({\n",
+    "    'Variable': X_simple.columns,\n",
+    "    'Coefficient': model.named_steps['logisticregression'].coef_[0]\n",
+    "}).sort_values(by='Coefficient', ascending=False)\n",
+    "\n",
+    "# Display top and bottom predictors\n",
+    "print(\"Top positive coefficients (increase completion odds):\")\n",
+    "print(coef_df.head(10))\n",
+    "print(\"\\nTop negative coefficients (decrease completion odds):\")\n",
+    "print(coef_df.tail(10))\n",
+    "\n",
+    "'''The top results were mostly programs, so this could and probably should be redone to exclude program and find better predictors.'''\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "859cf674",
@@ -2240,7 +2681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "81009a87",
    "metadata": {},
    "outputs": [],
@@ -2285,6 +2726,81 @@
     "    plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2cc940c6",
+   "metadata": {},
+   "source": [
+    "### Factors Influencing Program Completion\n",
+    "A Logistic Regression Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36bce427",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coef_df_sorted = coef_df.sort_values(by='Coefficient')\n",
+    "\n",
+    "# Show the 15 highest and 15 lowest coefficients\n",
+    "top_bottom = pd.concat([coef_df_sorted.head(15), coef_df_sorted.tail(15)])\n",
+    "\n",
+    "label_map = {\n",
+    "    # Programs\n",
+    "    \"Program: Program Name_Tech Louisville 20-21\": \"Tech Louisville 20-21\",\n",
+    "    \"Program: Program Name_Tech Louisville 21-22\": \"Tech Louisville 21-22\",\n",
+    "    \"Program: Program Name_Code Louisville 20-21\": \"Code Louisville 20-21\",\n",
+    "    \"Program: Program Name_Code Louisville 21-22\": \"Code Louisville 21-22\",\n",
+    "    \"Program: Program Name_Code Kentucky 23-24\": \"Code KY 23-24\",\n",
+    "    \"Program: Program Name_Code Kentucky 24-25\": \"Code KY 24-25\",\n",
+    "    \"Program: Program Name_Reimage 21-22\": \"Reimage 21-22\",\n",
+    "    \"Program: Program Name_Reimage 22-23\": \"Reimage 22-23\",\n",
+    "    \"Program: Program Name_Reimage 23-24\": \"Reimage 23-24\",\n",
+    "    \"Program: Program Name_Reimage 24-25\": \"Reimage 24-25\",\n",
+    "    \"Program: Program Name_Connecting Young Adults 24-25\": \"Connecting YA 24-25\",\n",
+    "    \"Program: Program Name_Metro 24-25\": \"Metro 24-25\",\n",
+    "    \"Program: Program Name_Metro 2025\": \"Metro 2025\",\n",
+    "    \"Program: Program Name_OSHN 23-24\": \"OSHN 23-24\",\n",
+    "\n",
+    "    # Demographics\n",
+    "    \"Race_White\": \"White\",\n",
+    "    \"Race_Black or African American\": \"Black\",\n",
+    "    \"Race_Asian\": \"Asian\",\n",
+    "    \"Race_American Indian or Alaskan Native\": \"Am. Indian / AK Native\",\n",
+    "    \"Race_American Indian or Alaskan Native; Native Hawaiian or Other Pacific Islander\": \"Am. Ind. / Pac. Isl.\",\n",
+    "    \"Race_American Indian or Alaskan Native; Asian; Black or African American; White\": \"Multi-race\",\n",
+    "    \"Gender_Transgender\": \"Transgender\",\n",
+    "    \"Gender_Transgender male to female\": \"Trans M→F\",\n",
+    "    \"Highest level of education completed_Bachelor's Degree\": \"Bachelor’s Degree\",\n",
+    "    \"Highest level of education completed_GED\": \"GED\",\n",
+    "    \"Highest level of education completed_Vocational Training\": \"Voc. Training\",\n",
+    "    \"Highest level of education completed_12th grade, no diploma\": \"12th No Diploma\",\n",
+    "    \"Employment Status_Self-employed\": \"Self-Employed\",\n",
+    "}\n",
+    "\n",
+    "coef_df[\"Variable\"] = coef_df[\"Variable\"].replace(label_map)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.barh(top_bottom['Variable'], top_bottom['Coefficient'], color='skyblue')\n",
+    "plt.title('Top and Bottom Predictors of Program Completion')\n",
+    "plt.xlabel('Coefficient')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f905708f",
@@ -2295,7 +2811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "id": "d4fc7116",
    "metadata": {},
    "outputs": [
@@ -2363,7 +2879,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -2377,7 +2893,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,