diff --git "a/model.ipynb" "b/model.ipynb"
--- "a/model.ipynb"
+++ "b/model.ipynb"
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 470,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 471,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -145,7 +145,7 @@
        "4              NaN       Yes  "
       ]
      },
-     "execution_count": 471,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 472,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 473,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +282,7 @@
        "max      89.000000       6.000000        14.000000"
       ]
      },
-     "execution_count": 473,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 474,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -304,7 +304,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 474,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 475,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +325,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 475,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 476,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +544,7 @@
        "[1964 rows x 8 columns]"
       ]
      },
-     "execution_count": 476,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 477,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -579,7 +579,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 477,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 478,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -630,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 479,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -740,7 +740,7 @@
        "4         7.528571       Yes  "
       ]
      },
-     "execution_count": 479,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -751,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 480,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -850,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 481,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -960,7 +960,7 @@
        "4         7.528571       Yes  "
       ]
      },
-     "execution_count": 481,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -971,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 482,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -980,7 +980,7 @@
        "\" \\n# target encoding due to many classes\\nencoder = TargetEncoder(cols=['Disease Type'], smoothing=0.3)\\ndf['DiseaseTypeEncoded'] = encoder.fit_transform(df['Disease Type'], df['Recovered'])\\n\\nencoder = TargetEncoder(cols=['Treatment Type'], smoothing=0.3)\\ndf['TreatmentTypeEncoded'] = encoder.fit_transform(df['Treatment Type'], df['Recovered']) \""
       ]
      },
-     "execution_count": 482,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1010,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 483,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1020,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 484,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1192,7 +1192,7 @@
        "4                          0                       1                       0  "
       ]
      },
-     "execution_count": 484,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1204,7 +1204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 485,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1214,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 505,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1229,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 506,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1389,7 +1389,7 @@
        "4                          0                       1                       0  "
       ]
      },
-     "execution_count": 506,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1407,7 +1407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 507,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1416,7 +1416,7 @@
        "((5200, 13), (5200,))"
       ]
      },
-     "execution_count": 507,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1435,13 +1435,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 509,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-28 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -1472,15 +1472,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1492,7 +1492,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1500,7 +1500,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1510,7 +1510,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1526,14 +1526,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-28 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1541,28 +1541,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-28 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1580,14 +1580,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-28 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-28 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -1600,13 +1600,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1614,13 +1614,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-28 div.sk-toggleable__content {\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1629,12 +1629,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1642,79 +1642,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-28 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-28 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-28 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-28 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1724,18 +1724,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-28 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1823,7 +1823,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-28 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1838,31 +1838,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-28 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-28 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;saga&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;saga&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression(solver='saga')"
       ]
      },
-     "execution_count": 509,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1898,7 +1898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 510,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1947,7 +1947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 511,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1974,7 +1974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 512,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2001,7 +2001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 513,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2028,7 +2028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 514,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2040,7 +2040,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -2055,7 +2055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 515,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2082,20 +2082,231 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 520,
+   "execution_count": 28,
    "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>Age</th>\n",
+       "      <th>Hospital Visits</th>\n",
+       "      <th>Lung Capacity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Smoking Status</th>\n",
+       "      <th>Recovered</th>\n",
+       "      <th>Disease Type_Asthma</th>\n",
+       "      <th>Disease Type_Bronchitis</th>\n",
+       "      <th>Disease Type_COPD</th>\n",
+       "      <th>Disease Type_Lung Cancer</th>\n",
+       "      <th>Disease Type_Pneumonia</th>\n",
+       "      <th>Treatment Type_Medication</th>\n",
+       "      <th>Treatment Type_Surgery</th>\n",
+       "      <th>Treatment Type_Therapy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>71</td>\n",
+       "      <td>14</td>\n",
+       "      <td>4.490000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3.501865</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>80</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.950000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.501865</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>43</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "['models\\\\LogisticRegression.pkl']"
+       "   Age  Hospital Visits  Lung Capacity  Gender  Smoking Status  Recovered  \\\n",
+       "0   71               14       4.490000       0               0          1   \n",
+       "1   34                7       3.501865       0               1          0   \n",
+       "2   80                4       1.950000       1               1          1   \n",
+       "3   40                1       3.501865       0               1          0   \n",
+       "4   43                7       4.600000       1               1          1   \n",
+       "\n",
+       "   Disease Type_Asthma  Disease Type_Bronchitis  Disease Type_COPD  \\\n",
+       "0                    0                        0                  1   \n",
+       "1                    0                        1                  0   \n",
+       "2                    0                        0                  1   \n",
+       "3                    0                        1                  0   \n",
+       "4                    0                        0                  1   \n",
+       "\n",
+       "   Disease Type_Lung Cancer  Disease Type_Pneumonia  \\\n",
+       "0                         0                       0   \n",
+       "1                         0                       0   \n",
+       "2                         0                       0   \n",
+       "3                         0                       0   \n",
+       "4                         0                       0   \n",
+       "\n",
+       "   Treatment Type_Medication  Treatment Type_Surgery  Treatment Type_Therapy  \n",
+       "0                          0                       0                       1  \n",
+       "1                          0                       1                       0  \n",
+       "2                          1                       0                       0  \n",
+       "3                          1                       0                       0  \n",
+       "4                          0                       1                       0  "
       ]
      },
-     "execution_count": 520,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "df_oh.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def prediction(model, age: int, gender: str,\n",
+    "               smoke_status: str, lung_capacity: float,\n",
+    "               disease_type: str, treatment_type: str,\n",
+    "               hospital_visits: int\n",
+    "               ) -> int:\n",
+    "    \n",
+    "    df_input = pd.DataFrame(\n",
+    "        {'Age': [age],\n",
+    "         'Hospital Visits': [hospital_visits],\n",
+    "         'Lung Capacity': [lung_capacity],\n",
+    "         'Gender': [1 if gender == \"Male\" else 0],\n",
+    "         'Smoking Status': [1 if smoke_status == \"Yes\" else 0],\n",
+    "         'Disease Type_Asthma': [1 if disease_type in 'Disease Type_Asthma' else 0],\n",
+    "         'Disease Type_Bronchitis': [1 if disease_type in 'Disease Type_Bronchitis' else 0],\n",
+    "        'Disease Type_COPD': [1 if disease_type in 'Disease Type_COPD' else 0],\n",
+    "        'Disease Type_Lung Cancer': [1 if disease_type in 'Disease Type_Lung Cancer' else 0],\n",
+    "        'Disease Type_Pneumonia': [1 if disease_type in 'Disease Type_Pneumonia' else 0],\n",
+    "        \n",
+    "        'Treatment Type_Medication': [1 if treatment_type in 'Treatment Type_Medication' else 0],\n",
+    "        'Treatment Type_Surgery': [1 if treatment_type in 'Treatment Type_Surgery' else 0],\n",
+    "        'Treatment Type_Therapy': [1 if treatment_type in 'Treatment Type_Therapy' else 0]\n",
+    "         }\n",
+    "    )\n",
+    "    \n",
+    "    input_arr = np.array(df_input)\n",
+    "    \n",
+    "    prediction = model.predict(input_arr)[0]\n",
+    "    \n",
+    "    return prediction.item()\n",
+    "    \n",
+    "    \n",
+    "prediction(lg, 20, 'Male', 'Yes', 7.14, 'COPD', 'Surgery', 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Saving the models\n",
     "from joblib import dump\n",
@@ -2112,7 +2323,8 @@
     "dump(rf, model_dir/'RandomForests.pkl')\n",
     "dump(xgb, model_dir/'XGBoost.pkl')\n",
     "dump(svm, model_dir/'SVM.pkl')\n",
-    "dump(lg, model_dir/'LogisticRegression.pkl')\n"
+    "dump(lg, model_dir/'LogisticRegression.pkl')\n",
+    "df_oh.to_csv('preprocessed_data.csv', index=False)\n"
    ]
   }
  ],