diff --git a/CharlesBookClub.csv b/CharlesBookClub.csv
new file mode 100644
index 0000000..64a8445
--- /dev/null
+++ b/CharlesBookClub.csv
@@ -0,0 +1,4001 @@
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diff --git a/Faceplate.csv b/Faceplate.csv
new file mode 100644
index 0000000..773866e
--- /dev/null
+++ b/Faceplate.csv
@@ -0,0 +1,11 @@
+Transaction,Red,White,Blue,Orange,Green,Yellow
+1,1,1,0,0,1,0
+2,0,1,0,1,0,0
+3,0,1,1,0,0,0
+4,1,1,0,1,0,0
+5,1,0,1,0,0,0
+6,0,1,1,0,0,0
+7,1,0,1,0,0,0
+8,1,1,1,0,1,0
+9,1,1,1,0,0,0
+10,0,0,0,0,0,1
diff --git a/Lecture-Work.ipynb b/Lecture-Work.ipynb
index 0b65ab2..c98968b 100644
--- a/Lecture-Work.ipynb
+++ b/Lecture-Work.ipynb
@@ -18,72 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting package metadata (current_repodata.json): ...working... done\n",
-      "Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.\n",
-      "Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.\n",
-      "Collecting package metadata (repodata.json): ...working... done\n",
-      "Solving environment: ...working... done\n",
-      "\n",
-      "## Package Plan ##\n",
-      "\n",
-      "  environment location: C:\\Users\\isa1761\\Anaconda3\n",
-      "\n",
-      "  added / updated specs:\n",
-      "    - scikit-surprise\n",
-      "\n",
-      "\n",
-      "The following packages will be downloaded:\n",
-      "\n",
-      "    package                    |            build\n",
-      "    ---------------------------|-----------------\n",
-      "    conda-4.12.0               |   py39hcbf5309_0         1.0 MB  conda-forge\n",
-      "    scikit-surprise-1.1.1      |   py39h5d4886f_2         538 KB  conda-forge\n",
-      "    ------------------------------------------------------------\n",
-      "                                           Total:         1.5 MB\n",
-      "\n",
-      "The following NEW packages will be INSTALLED:\n",
-      "\n",
-      "  scikit-surprise    conda-forge/win-64::scikit-surprise-1.1.1-py39h5d4886f_2\n",
-      "\n",
-      "The following packages will be UPDATED:\n",
-      "\n",
-      "  conda                               4.11.0-py39hcbf5309_0 --> 4.12.0-py39hcbf5309_0\n",
-      "\n",
-      "\n",
-      "\n",
-      "Downloading and Extracting Packages\n",
-      "\n",
-      "scikit-surprise-1.1. | 538 KB    |            |   0% \n",
-      "scikit-surprise-1.1. | 538 KB    | 2          |   3% \n",
-      "scikit-surprise-1.1. | 538 KB    | ########## | 100% \n",
-      "scikit-surprise-1.1. | 538 KB    | ########## | 100% \n",
-      "\n",
-      "conda-4.12.0         | 1.0 MB    |            |   0% \n",
-      "conda-4.12.0         | 1.0 MB    | 1          |   2% \n",
-      "conda-4.12.0         | 1.0 MB    | ########## | 100% \n",
-      "conda-4.12.0         | 1.0 MB    | ########## | 100% \n",
-      "Preparing transaction: ...working... done\n",
-      "Verifying transaction: ...working... done\n",
-      "Executing transaction: ...working... done\n",
-      "\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "conda install -c conda-forge scikit-surprise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -265,7 +200,7 @@
        "10             0      0     0       0      0       1"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -279,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -287,13 +222,21 @@
      "output_type": "stream",
      "text": [
       "       antecedents   consequents  support  confidence      lift  leverage\n",
-      "12    (White, Red)       (Green)      0.2         0.5  2.500000      0.12\n",
+      "13    (White, Red)       (Green)      0.2         0.5  2.500000      0.12\n",
       "15         (Green)  (White, Red)      0.2         1.0  2.500000      0.12\n",
       "4          (Green)         (Red)      0.2         1.0  1.666667      0.08\n",
-      "13  (White, Green)         (Red)      0.2         1.0  1.666667      0.08\n",
+      "12  (White, Green)         (Red)      0.2         1.0  1.666667      0.08\n",
       "7         (Orange)       (White)      0.2         1.0  1.428571      0.06\n",
       "8          (Green)       (White)      0.2         1.0  1.428571      0.06\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/noah/.local/lib/python3.10/site-packages/mlxtend/frequent_patterns/fpcommon.py:111: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
@@ -311,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +291,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>13</th>\n",
        "      <td>(White, Red)</td>\n",
        "      <td>(Green)</td>\n",
        "      <td>0.4</td>\n",
@@ -372,7 +315,7 @@
        "      <td>inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>12</th>\n",
        "      <td>(White, Green)</td>\n",
        "      <td>(Red)</td>\n",
        "      <td>0.2</td>\n",
@@ -409,7 +352,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>(Red, Green)</td>\n",
+       "      <td>(Green, Red)</td>\n",
        "      <td>(White)</td>\n",
        "      <td>0.2</td>\n",
        "      <td>0.7</td>\n",
@@ -425,23 +368,23 @@
       ],
       "text/plain": [
        "       antecedents consequents  antecedent support  consequent support  \\\n",
-       "12    (White, Red)     (Green)                 0.4                 0.2   \n",
+       "13    (White, Red)     (Green)                 0.4                 0.2   \n",
        "4          (Green)       (Red)                 0.2                 0.6   \n",
-       "13  (White, Green)       (Red)                 0.2                 0.6   \n",
+       "12  (White, Green)       (Red)                 0.2                 0.6   \n",
        "7         (Orange)     (White)                 0.2                 0.7   \n",
        "8          (Green)     (White)                 0.2                 0.7   \n",
-       "14    (Red, Green)     (White)                 0.2                 0.7   \n",
+       "14    (Green, Red)     (White)                 0.2                 0.7   \n",
        "\n",
        "    support  confidence      lift  leverage  conviction  \n",
-       "12      0.2         0.5  2.500000      0.12         1.6  \n",
+       "13      0.2         0.5  2.500000      0.12         1.6  \n",
        "4       0.2         1.0  1.666667      0.08         inf  \n",
-       "13      0.2         1.0  1.666667      0.08         inf  \n",
+       "12      0.2         1.0  1.666667      0.08         inf  \n",
        "7       0.2         1.0  1.428571      0.06         inf  \n",
        "8       0.2         1.0  1.428571      0.06         inf  \n",
        "14      0.2         1.0  1.428571      0.06         inf  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -460,14 +403,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Density 0.4\n"
+      "/home/noah/.local/lib/python3.10/site-packages/mlxtend/frequent_patterns/fpcommon.py:111: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
+      "  warnings.warn(\n"
      ]
     },
     {
@@ -504,7 +448,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>13</th>\n",
        "      <td>(White, Red)</td>\n",
        "      <td>(Green)</td>\n",
        "      <td>0.4</td>\n",
@@ -540,7 +484,7 @@
        "      <td>inf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>12</th>\n",
        "      <td>(White, Green)</td>\n",
        "      <td>(Red)</td>\n",
        "      <td>0.2</td>\n",
@@ -581,31 +525,32 @@
       ],
       "text/plain": [
        "       antecedents   consequents  antecedent support  consequent support  \\\n",
-       "12    (White, Red)       (Green)                 0.4                 0.2   \n",
+       "13    (White, Red)       (Green)                 0.4                 0.2   \n",
        "15         (Green)  (White, Red)                 0.2                 0.4   \n",
        "4          (Green)         (Red)                 0.2                 0.6   \n",
-       "13  (White, Green)         (Red)                 0.2                 0.6   \n",
+       "12  (White, Green)         (Red)                 0.2                 0.6   \n",
        "7         (Orange)       (White)                 0.2                 0.7   \n",
        "8          (Green)       (White)                 0.2                 0.7   \n",
        "\n",
        "    support  confidence      lift  leverage  conviction  \n",
-       "12      0.2         0.5  2.500000      0.12         1.6  \n",
+       "13      0.2         0.5  2.500000      0.12         1.6  \n",
        "15      0.2         1.0  2.500000      0.12         inf  \n",
        "4       0.2         1.0  1.666667      0.08         inf  \n",
-       "13      0.2         1.0  1.666667      0.08         inf  \n",
+       "12      0.2         1.0  1.666667      0.08         inf  \n",
        "7       0.2         1.0  1.428571      0.06         inf  \n",
        "8       0.2         1.0  1.428571      0.06         inf  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Convert data set into a sparse data frame\n",
-    "sparse_df = fp_df.to_sparse(fill_value=0)\n",
-    "print('Density {}'.format(sparse_df.density))\n",
+    "#sparse_df = fp_df.to_sparse(fill_value=0)\n",
+    "sparse_df = fp_df.astype(pd.SparseDtype(int, fill_value=0))\n",
+    "#print('Density {}'.format(sparse_df.density))\n",
     "\n",
     "# create frequent itemsets\n",
     "itemsets = apriori(sparse_df, min_support=0.2, use_colnames=True)\n",
@@ -624,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -740,7 +685,7 @@
        "4  0  0  0  0  0  0  0  0  1"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -771,7 +716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -786,6 +731,14 @@
       "5      (3, 7)         (9)     0.04         1.0  1.851852    0.0184\n",
       "6      (4, 5)         (9)     0.04         1.0  1.851852    0.0184\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/noah/.local/lib/python3.10/site-packages/mlxtend/frequent_patterns/fpcommon.py:111: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
@@ -807,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -935,7 +888,7 @@
        "4          0        0         0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -955,19 +908,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -983,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -991,37 +942,73 @@
      "output_type": "stream",
      "text": [
       "Number of rules 81\n",
-      "                      antecedents          consequents  support  confidence     lift  leverage\n",
-      "64             (RefBks, YouthBks)  (CookBks, ChildBks)  0.05525     0.68000  2.80992   0.03559\n",
-      "73             (RefBks, DoItYBks)  (CookBks, ChildBks)  0.06125     0.66216  2.73621   0.03886\n",
-      "60           (DoItYBks, YouthBks)  (CookBks, ChildBks)  0.06700     0.64891  2.68145   0.04201\n",
-      "80              (RefBks, GeogBks)  (CookBks, ChildBks)  0.05025     0.61468  2.54000   0.03047\n",
-      "69            (GeogBks, YouthBks)  (CookBks, ChildBks)  0.06325     0.60526  2.50109   0.03796\n",
-      "77            (DoItYBks, GeogBks)  (CookBks, ChildBks)  0.06050     0.59901  2.47525   0.03606\n",
-      "65   (CookBks, ChildBks, GeogBks)           (YouthBks)  0.06325     0.57763  2.42445   0.03716\n",
-      "71    (CookBks, RefBks, ChildBks)           (DoItYBks)  0.06125     0.59179  2.32301   0.03488\n",
-      "47            (DoItYBks, GeogBks)           (YouthBks)  0.05450     0.53960  2.26486   0.03044\n",
-      "61    (CookBks, RefBks, ChildBks)           (YouthBks)  0.05525     0.53382  2.24057   0.03059\n",
-      "56  (CookBks, DoItYBks, ChildBks)           (YouthBks)  0.06700     0.52446  2.20131   0.03656\n",
-      "58  (CookBks, ChildBks, YouthBks)           (DoItYBks)  0.06700     0.55833  2.19169   0.03643\n",
-      "34             (RefBks, ChildBks)           (DoItYBks)  0.07100     0.55361  2.17314   0.03833\n",
-      "75   (CookBks, ChildBks, GeogBks)           (DoItYBks)  0.06050     0.55251  2.16884   0.03260\n",
-      "19            (ChildBks, GeogBks)           (YouthBks)  0.07550     0.51624  2.16680   0.04066\n",
-      "45             (CookBks, GeogBks)           (YouthBks)  0.08025     0.51360  2.15572   0.04302\n",
-      "63   (RefBks, ChildBks, YouthBks)            (CookBks)  0.05525     0.89113  2.14471   0.02949\n",
-      "17           (ChildBks, YouthBks)           (DoItYBks)  0.08025     0.54407  2.13569   0.04267\n",
-      "50              (CookBks, RefBks)           (DoItYBks)  0.07450     0.53309  2.09262   0.03890\n",
-      "28                       (RefBks)  (CookBks, ChildBks)  0.10350     0.50549  2.08882   0.05395\n",
-      "70    (CookBks, DoItYBks, RefBks)           (ChildBks)  0.06125     0.82215  2.08667   0.03190\n",
-      "15                     (YouthBks)  (CookBks, ChildBks)  0.12000     0.50367  2.08129   0.06234\n",
-      "72   (RefBks, DoItYBks, ChildBks)            (CookBks)  0.06125     0.86268  2.07624   0.03175\n",
-      "23            (CookBks, ChildBks)           (DoItYBks)  0.12775     0.52789  2.07220   0.06610\n",
-      "25                     (DoItYBks)  (CookBks, ChildBks)  0.12775     0.50147  2.07220   0.06610\n"
+      "                      antecedents          consequents  support  confidence  \\\n",
+      "64             (RefBks, YouthBks)  (ChildBks, CookBks)  0.05525    0.680000   \n",
+      "73             (DoItYBks, RefBks)  (ChildBks, CookBks)  0.06125    0.662162   \n",
+      "60           (DoItYBks, YouthBks)  (ChildBks, CookBks)  0.06700    0.648910   \n",
+      "80              (RefBks, GeogBks)  (ChildBks, CookBks)  0.05025    0.614679   \n",
+      "69            (YouthBks, GeogBks)  (ChildBks, CookBks)  0.06325    0.605263   \n",
+      "77            (DoItYBks, GeogBks)  (ChildBks, CookBks)  0.06050    0.599010   \n",
+      "68   (ChildBks, CookBks, GeogBks)           (YouthBks)  0.06325    0.577626   \n",
+      "72    (RefBks, ChildBks, CookBks)           (DoItYBks)  0.06125    0.591787   \n",
+      "48            (DoItYBks, GeogBks)           (YouthBks)  0.05450    0.539604   \n",
+      "63    (RefBks, ChildBks, CookBks)           (YouthBks)  0.05525    0.533816   \n",
+      "58  (DoItYBks, ChildBks, CookBks)           (YouthBks)  0.06700    0.524462   \n",
+      "59  (YouthBks, ChildBks, CookBks)           (DoItYBks)  0.06700    0.558333   \n",
+      "34             (RefBks, ChildBks)           (DoItYBks)  0.07100    0.553606   \n",
+      "76   (ChildBks, CookBks, GeogBks)           (DoItYBks)  0.06050    0.552511   \n",
+      "21            (ChildBks, GeogBks)           (YouthBks)  0.07550    0.516239   \n",
+      "46             (CookBks, GeogBks)           (YouthBks)  0.08025    0.513600   \n",
+      "61   (RefBks, YouthBks, ChildBks)            (CookBks)  0.05525    0.891129   \n",
+      "17           (YouthBks, ChildBks)           (DoItYBks)  0.08025    0.544068   \n",
+      "51              (RefBks, CookBks)           (DoItYBks)  0.07450    0.533095   \n",
+      "28                       (RefBks)  (ChildBks, CookBks)  0.10350    0.505495   \n",
+      "71    (DoItYBks, RefBks, CookBks)           (ChildBks)  0.06125    0.822148   \n",
+      "15                     (YouthBks)  (ChildBks, CookBks)  0.12000    0.503673   \n",
+      "70   (DoItYBks, RefBks, ChildBks)            (CookBks)  0.06125    0.862676   \n",
+      "24            (ChildBks, CookBks)           (DoItYBks)  0.12775    0.527893   \n",
+      "25                     (DoItYBks)  (ChildBks, CookBks)  0.12775    0.501472   \n",
+      "\n",
+      "        lift  leverage  \n",
+      "64  2.809917  0.035588  \n",
+      "73  2.736207  0.038865  \n",
+      "60  2.681448  0.042014  \n",
+      "80  2.539995  0.030467  \n",
+      "69  2.501087  0.037961  \n",
+      "77  2.475248  0.036058  \n",
+      "68  2.424452  0.037162  \n",
+      "72  2.323013  0.034883  \n",
+      "48  2.264864  0.030437  \n",
+      "63  2.240573  0.030591  \n",
+      "58  2.201309  0.036564  \n",
+      "59  2.191691  0.036430  \n",
+      "34  2.173135  0.038328  \n",
+      "76  2.168838  0.032605  \n",
+      "21  2.166797  0.040656  \n",
+      "46  2.155719  0.043023  \n",
+      "61  2.144715  0.029489  \n",
+      "17  2.135693  0.042674  \n",
+      "51  2.092619  0.038899  \n",
+      "28  2.088820  0.053950  \n",
+      "71  2.086669  0.031897  \n",
+      "15  2.081292  0.062344  \n",
+      "70  2.076236  0.031749  \n",
+      "24  2.072198  0.066101  \n",
+      "25  2.072198  0.066101  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/noah/.local/lib/python3.10/site-packages/mlxtend/frequent_patterns/fpcommon.py:111: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
    "source": [
     "# create frequent itemsets and rules\n",
+    "pd.options.display.max_rows = 100\n",
     "itemsets = apriori(count_books, min_support=200/4000, use_colnames=True)\n",
     "rules = association_rules(itemsets, metric='confidence', min_threshold=0.5)\n",
     "\n",
@@ -1030,15 +1017,12 @@
     "# Display 25 rules with highest lift\n",
     "rules.sort_values(by=['lift'], ascending=False).head(25)\n",
     "\n",
-    "pd.set_option('precision', 5)\n",
-    "pd.set_option('display.width', 100)\n",
-    "print(rules.sort_values(by=['lift'], ascending=False).drop(columns=['antecedent support', 'consequent support', 'conviction']).head(25))\n",
-    "pd.set_option('precision', 6)"
+    "print(rules.sort_values(by=['lift'], ascending=False).drop(columns=['antecedent support', 'consequent support', 'conviction']).head(25))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1075,7 +1059,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
+       "      <th>48</th>\n",
        "      <td>(DoItYBks, GeogBks)</td>\n",
        "      <td>(YouthBks)</td>\n",
        "      <td>0.10100</td>\n",
@@ -1099,7 +1083,7 @@
        "      <td>1.669490</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>21</th>\n",
        "      <td>(ChildBks, GeogBks)</td>\n",
        "      <td>(YouthBks)</td>\n",
        "      <td>0.14625</td>\n",
@@ -1111,7 +1095,7 @@
        "      <td>1.574642</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>45</th>\n",
+       "      <th>46</th>\n",
        "      <td>(CookBks, GeogBks)</td>\n",
        "      <td>(YouthBks)</td>\n",
        "      <td>0.15625</td>\n",
@@ -1124,7 +1108,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>(ChildBks, YouthBks)</td>\n",
+       "      <td>(YouthBks, ChildBks)</td>\n",
        "      <td>(DoItYBks)</td>\n",
        "      <td>0.14750</td>\n",
        "      <td>0.25475</td>\n",
@@ -1135,8 +1119,8 @@
        "      <td>1.634563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>(CookBks, RefBks)</td>\n",
+       "      <th>51</th>\n",
+       "      <td>(RefBks, CookBks)</td>\n",
        "      <td>(DoItYBks)</td>\n",
        "      <td>0.13975</td>\n",
        "      <td>0.25475</td>\n",
@@ -1147,8 +1131,8 @@
        "      <td>1.596148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>(CookBks, ChildBks)</td>\n",
+       "      <th>24</th>\n",
+       "      <td>(ChildBks, CookBks)</td>\n",
        "      <td>(DoItYBks)</td>\n",
        "      <td>0.24200</td>\n",
        "      <td>0.25475</td>\n",
@@ -1160,7 +1144,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
-       "      <td>(GeogBks, YouthBks)</td>\n",
+       "      <td>(YouthBks, GeogBks)</td>\n",
        "      <td>(DoItYBks)</td>\n",
        "      <td>0.10450</td>\n",
        "      <td>0.25475</td>\n",
@@ -1171,8 +1155,8 @@
        "      <td>1.557573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>(CookBks, YouthBks)</td>\n",
+       "      <th>42</th>\n",
+       "      <td>(YouthBks, CookBks)</td>\n",
        "      <td>(DoItYBks)</td>\n",
        "      <td>0.16100</td>\n",
        "      <td>0.25475</td>\n",
@@ -1199,32 +1183,32 @@
        "</div>"
       ],
       "text/plain": [
-       "             antecedents consequents  antecedent support  consequent support  support  confidence  \\\n",
-       "47   (DoItYBks, GeogBks)  (YouthBks)             0.10100             0.23825  0.05450    0.539604   \n",
-       "34    (RefBks, ChildBks)  (DoItYBks)             0.12825             0.25475  0.07100    0.553606   \n",
-       "19   (ChildBks, GeogBks)  (YouthBks)             0.14625             0.23825  0.07550    0.516239   \n",
-       "45    (CookBks, GeogBks)  (YouthBks)             0.15625             0.23825  0.08025    0.513600   \n",
-       "17  (ChildBks, YouthBks)  (DoItYBks)             0.14750             0.25475  0.08025    0.544068   \n",
-       "50     (CookBks, RefBks)  (DoItYBks)             0.13975             0.25475  0.07450    0.533095   \n",
-       "23   (CookBks, ChildBks)  (DoItYBks)             0.24200             0.25475  0.12775    0.527893   \n",
-       "49   (GeogBks, YouthBks)  (DoItYBks)             0.10450             0.25475  0.05450    0.521531   \n",
-       "41   (CookBks, YouthBks)  (DoItYBks)             0.16100             0.25475  0.08375    0.520186   \n",
-       "43    (RefBks, YouthBks)   (CookBks)             0.08125             0.41550  0.06825    0.840000   \n",
+       "             antecedents consequents  antecedent support  consequent support  \\\n",
+       "48   (DoItYBks, GeogBks)  (YouthBks)             0.10100             0.23825   \n",
+       "34    (RefBks, ChildBks)  (DoItYBks)             0.12825             0.25475   \n",
+       "21   (ChildBks, GeogBks)  (YouthBks)             0.14625             0.23825   \n",
+       "46    (CookBks, GeogBks)  (YouthBks)             0.15625             0.23825   \n",
+       "17  (YouthBks, ChildBks)  (DoItYBks)             0.14750             0.25475   \n",
+       "51     (RefBks, CookBks)  (DoItYBks)             0.13975             0.25475   \n",
+       "24   (ChildBks, CookBks)  (DoItYBks)             0.24200             0.25475   \n",
+       "49   (YouthBks, GeogBks)  (DoItYBks)             0.10450             0.25475   \n",
+       "42   (YouthBks, CookBks)  (DoItYBks)             0.16100             0.25475   \n",
+       "43    (RefBks, YouthBks)   (CookBks)             0.08125             0.41550   \n",
        "\n",
-       "        lift  leverage  conviction  \n",
-       "47  2.264864  0.030437    1.654554  \n",
-       "34  2.173135  0.038328    1.669490  \n",
-       "19  2.166797  0.040656    1.574642  \n",
-       "45  2.155719  0.043023    1.566098  \n",
-       "17  2.135693  0.042674    1.634563  \n",
-       "50  2.092619  0.038899    1.596148  \n",
-       "23  2.072198  0.066101    1.578560  \n",
-       "49  2.047227  0.027879    1.557573  \n",
-       "41  2.041948  0.042735    1.553207  \n",
-       "43  2.021661  0.034491    3.653125  "
+       "    support  confidence      lift  leverage  conviction  \n",
+       "48  0.05450    0.539604  2.264864  0.030437    1.654554  \n",
+       "34  0.07100    0.553606  2.173135  0.038328    1.669490  \n",
+       "21  0.07550    0.516239  2.166797  0.040656    1.574642  \n",
+       "46  0.08025    0.513600  2.155719  0.043023    1.566098  \n",
+       "17  0.08025    0.544068  2.135693  0.042674    1.634563  \n",
+       "51  0.07450    0.533095  2.092619  0.038899    1.596148  \n",
+       "24  0.12775    0.527893  2.072198  0.066101    1.578560  \n",
+       "49  0.05450    0.521531  2.047227  0.027879    1.557573  \n",
+       "42  0.08375    0.520186  2.041948  0.042735    1.553207  \n",
+       "43  0.06825    0.840000  2.021661  0.034491    3.653125  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1246,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1255,7 +1239,7 @@
      "text": [
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "user: 823519     item: 30         r_ui = 4.00   est = 3.54   {'was_impossible': True, 'reason': 'User and/or item is unkown.'}\n"
+      "user: 823519     item: 30         r_ui = 4.00   est = 3.54   {'was_impossible': True, 'reason': 'User and/or item is unknown.'}\n"
      ]
     }
    ],
@@ -1291,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1421,7 +1405,7 @@
        "Prediction(uid=383, iid=7, r_ui=None, est=2.3661840936304324, details={'actual_k': 4, 'was_impossible': False})"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1454,7 +1438,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,