diff --git a/clean.ipynb b/clean.ipynb
index b1aeae8..b3e315c 100644
--- a/clean.ipynb
+++ b/clean.ipynb
@@ -13,7 +13,9 @@
    "id": "cecbac86-abb3-4f6b-a101-2d9324d96274",
    "metadata": {},
    "source": [
-    "# Cleaning"
+    "# Cleaning\n",
+    "\n",
+    "Let's get this to something we can work with"
    ]
   },
   {
@@ -30,17 +32,19 @@
    "id": "3c4bfade-d06d-4887-9eb4-ec7f5bc61625",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:36.887038Z",
-     "iopub.status.busy": "2022-07-21T20:29:36.886672Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.222976Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.222218Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:36.886962Z"
+     "iopub.execute_input": "2022-08-01T00:18:59.316785Z",
+     "iopub.status.busy": "2022-08-01T00:18:59.315438Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.307894Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.307130Z",
+     "shell.execute_reply.started": "2022-08-01T00:18:59.316695Z"
     },
     "tags": []
    },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "df = pd.read_csv('data/auto-mpg.data',header=None,delim_whitespace=True)\n",
     "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
@@ -71,11 +75,11 @@
    "id": "62bbb6bd-b5b3-4d54-a132-23cd367c4570",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.225459Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.224901Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.237624Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.236773Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.225432Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.311568Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.310921Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.322308Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.321851Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.311524Z"
     },
     "tags": []
    },
@@ -112,7 +116,7 @@
    "id": "6a4028ed-eda3-4c50-aed0-d9503d41a8e1",
    "metadata": {},
    "source": [
-    "Why is horsepower not a number?"
+    "No nulls, but why is horsepower not a number?"
    ]
   },
   {
@@ -121,11 +125,11 @@
    "id": "58fa2876-4ccb-4ef5-bc16-d25b74efb457",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.239126Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.238760Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.252035Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.251217Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.239098Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.323107Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.322921Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.333299Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.332860Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.323092Z"
     },
     "tags": []
    },
@@ -164,11 +168,11 @@
    "id": "2d99ea58-ca51-4461-a127-c6b389b056a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.253416Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.253082Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.271785Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.271054Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.253389Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.334093Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.333926Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.347963Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.347305Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.334077Z"
     },
     "tags": []
    },
@@ -314,7 +318,7 @@
    "id": "498d069d-b95e-43d6-bd3d-4b707fdd9635",
    "metadata": {},
    "source": [
-    "I'll fill in what I can find online"
+    "I'll fill in what I can with what I can find online"
    ]
   },
   {
@@ -323,11 +327,11 @@
    "id": "e53a2eaf-a8f9-4d7e-bf8b-07a125cf6f06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.273324Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.272853Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.278574Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.277496Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.273297Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.348907Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.348680Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.352582Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.351931Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.348891Z"
     },
     "tags": []
    },
@@ -363,11 +367,11 @@
    "id": "10400330-e6aa-43e0-910f-f97869c23d0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.280095Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.279777Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.286985Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.286202Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.280060Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.353597Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.353430Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.360958Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.359990Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.353582Z"
     },
     "tags": []
    },
@@ -392,11 +396,11 @@
    "id": "e0fd9a7b-6cdf-4346-8c8d-6c5f36e167f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.289881Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.289472Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.301335Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.300537Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.289852Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.365129Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.364725Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.373554Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.372817Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.365100Z"
     },
     "tags": []
    },
@@ -450,11 +454,11 @@
    "id": "769f33e7-2f2e-46e8-b6dd-8f8fb79d13b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.303380Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.302680Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.310508Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.309738Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.303336Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.374606Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.374357Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.379646Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.379055Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.374591Z"
     },
     "tags": []
    },
@@ -520,11 +524,11 @@
    "id": "7bac1a71-53d2-4081-b566-244bccd3a3c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.312020Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.311631Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.319881Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.318953Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.311992Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.380599Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.380414Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.387779Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.387145Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.380583Z"
     },
     "tags": []
    },
@@ -575,11 +579,11 @@
    "id": "87715776-3634-4ca7-bbb4-e04633fe4791",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.321678Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.321045Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.354573Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.353866Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.321651Z"
+     "iopub.execute_input": "2022-08-01T00:19:00.389256Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.388803Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.417680Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.417117Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.389227Z"
     },
     "tags": []
    },
@@ -747,6 +751,489 @@
     "Everything looks proportional"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3f68b5d6-15c7-4fe0-aa49-a04ab90c4efa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:00.418521Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.418342Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.645493Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.644587Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.418505Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(x=df.mpg)\n",
+    "plt.title('Distribution of MPG')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "506e5033-b3da-4624-bbc5-44f3d159d9e1",
+   "metadata": {},
+   "source": [
+    "Most MPG is around 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4c705ec7-9106-4f3c-a65b-5081fe1ded59",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:00.647104Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.646709Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.732581Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.732044Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.647075Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=df.mpg)\n",
+    "plt.title('Boxplot of MPG')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ab5a1cd-177f-4dcb-bd79-e9ad395d51d7",
+   "metadata": {},
+   "source": [
+    "There's one value considered an outlier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "655067f4-4c89-4e65-b65a-c1a1e4158535",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:00.733588Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.733352Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.742775Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.741517Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.733572Z"
+    },
+    "tags": []
+   },
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>46.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2110.0</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mazda glc</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "322  46.6          4          86.0        65.0  2110.0          17.9   \n",
+       "\n",
+       "     model_year  origin   car_name  \n",
+       "322          80       3  mazda glc  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.mpg > 45]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89f5906-7b78-4268-933a-ccf02e151b85",
+   "metadata": {},
+   "source": [
+    "I'm going to leave this in because it's a real value. I guess it appears as an outlier because the data set is so small"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2720ba7e-272d-4cf7-810c-0a7ec7ad2e58",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:00.744397Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.744001Z",
+     "iopub.status.idle": "2022-08-01T00:19:00.899639Z",
+     "shell.execute_reply": "2022-08-01T00:19:00.899044Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.744367Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(x=df.cylinders)\n",
+    "plt.title('Distribution of Cylinders')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d99f70d-728f-4df3-a178-023617e642af",
+   "metadata": {},
+   "source": [
+    "4 Cylinder engines outnumber the others by a lot. It would be nice if we had more info to go off of, particularly if 6 cylinders could be split between inline and V configurations. It's less important for the others cause while inline-8s and V4s exist they're so uncommon in cars of this vintage that we can assume they don't exist. They'll get different fuel economy but not by enough to sway things at the level of accuracy we're at. Inline-6 vs V6 though I think there could be something to see there and it could improve accuracy slightly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8fecb4c7-cdda-4922-8018-3e5160f6ecf1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:00.900607Z",
+     "iopub.status.busy": "2022-08-01T00:19:00.900368Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.039878Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.039298Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:00.900591Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(x=df.displacement)\n",
+    "plt.title('Distribution of Displacement')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3235dc9c-d7d4-4795-8fd2-1d208be182c7",
+   "metadata": {},
+   "source": [
+    "Most engines in the data are smaller since most of our engines are 4 cylinders. The 3 groups seen here are the split between 4, 6, and 8 cylinders because they all come in generally the same sizes for automotive applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b7b4c6b4-c46c-4f61-9e2a-1243ccc39d0a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:01.041100Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.040804Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.123396Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.122989Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.041075Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=df.displacement)\n",
+    "plt.title('Boxplot of Displacement')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78eff4eb-407f-4d7b-9c7d-42fde78b4cf4",
+   "metadata": {},
+   "source": [
+    "Again most engines are on the smaller side of the spectrum ranging from around 100ci to around 260ci which is representative of the market"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0235d7ee-8882-490c-b209-8d2d2fbacee5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:01.124186Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.124021Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.256006Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.255440Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.124159Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(x=df.weight)\n",
+    "plt.title('Distribution of Weight')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8bbd1af-1c95-4fde-916e-e757f95b2f04",
+   "metadata": {},
+   "source": [
+    "Weight is a major player in fuel economy as it takes more energy to move a heavy car. An inefficient engine moving less weight than a highly efficient engine can end up burning more fuel but generally less weight means higher mpg. Most cars here are around 2000lbs, which makes sense because that's about the weight of a typical commuter/economy car from the 70s. They didn't have as much stuff packed into the interior that we have today so they're lighter. That's why some of these MPG numbers may seem high, but they're real"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "762d3940-4f1e-40e1-9fef-67073f33791b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:01.256902Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.256753Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.325922Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.325506Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.256887Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcImP9gD2xsxUXV0tSUpOTlYQBFEeEQAAB1ZLeS5s8a80VFdXa/To0Ro9enT4gAEA0Jq0lOfCFh8NAACgZSAaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC7x0R7A3phZ+H11dXUUR3JwiXis9ngMgYMGP/tAaM+fAYvi3+nfKhpqampUU1MT/nn79u3fekCN3Ued888/v9nP3yrU7pKUGO1RAPumdlf4LT/7wH/V1NSobdu2Ubnvb/X2xPTp05WWlhZ+ZWdnN9e4AABAC/OtXmm45ZZbdN1114V/3r59e7OHQ1JSUvj9rFmzlJyc3Kznj1XV1dX//b+zuBb/LhTQ0B7/3fKzj9Zuz7/T93xePNC+1bNJUlLSfh98EATh98nJyUpJSdmv9xeT9ngMgYMGP/tAo4Io/p3OpycAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwiY/2APYmOTlZc+fODb8HAKC1aSnPhS0+GoIgUEpKSrSHAQBA1LSU50LengAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4BIf7QFg/wtqd8miPYiWavfOxr9H1AW1u6I9BAD1EA2tQPuSf0R7CAeF1HefiPYQAKBF4+0JAADgwisNMSo5OVlz586N9jBaPDNTTU2NJCkpKUlBEER5RGhMcnJytIcAQERDzAqCQCkpKdEexkGhbdu20R4CABwUeHsCAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuBANAADAhWgAAAAuRAMAAHAhGgAAgAvRAAAAXIgGAADgQjQAAAAXogEAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC5EAwAAcCEaAACAC9EAAABciAYAAOBCNAAAABeiAQAAuMQ358nMTJK0ffv25jwtAADYj+qet+uex5vSrNFQWVkpScrOzm7O0wIAgAOgsrJSaWlpTe4PbG9ZsQ9qa2u1ceNGpaamKgiCBvu3b9+u7OxslZWVqUOHDs11twcN5s/8W+v8W/PcJebP/Fv+/M1MlZWVysrKUlxc01cuNOsrDXFxcTr88MP3elyHDh1a7AN3IDB/5t9a59+a5y4xf+bfsuf/Ta8w1OFCSAAA4EI0AAAAlwMaDUlJSZo2bZqSkpIO5N22GMyf+bfW+bfmuUvMn/nHzvyb9UJIAAAQu3h7AgAAuBANAADAhWgAAAAuRAMAAHDZ52h48803dc455ygrK0tBEGj27NkR+y+77DIFQRDxNXTo0IhjampqNGnSJB122GFq166dvve97+nf//53xDFbt27VpZdeqrS0NKWlpenSSy/V559/vs8TbE7Tp0/XCSecoNTUVHXu3FnnnXeeVq9eHXGMmSk/P19ZWVlKSUlRbm6uVq1aFXFMLM8/ltf/j3/8o/r16xf+Ay3Dhg3T3Llzw/2xvPbS3ucfy2tf3/Tp0xUEga655ppwW6yv/54am3+sr39+fn6D+WVkZIT7W8362z566aWXbOrUqfbMM8+YJJs1a1bE/rFjx9qZZ55pmzZtCr8qKioijhk/frx17drVXn31VVu6dKmNHDnS+vfvb7t27QqPOfPMM61Pnz5WVFRkRUVF1qdPHzv77LP3dbjN6owzzrAZM2bYypUrraSkxMaMGWNHHHGEVVVVhcfcddddlpqaas8884ytWLHCLrzwQsvMzLTt27eHx8Ty/GN5/efMmWMvvviirV692lavXm233nqrJSQk2MqVK80sttfebO/zj+W139Pbb79t3bt3t379+tmUKVPC7bG+/nWamn+sr/+0adPsO9/5TsT8tmzZEu5vLeu/z9EQceMmouHcc89t8jaff/65JSQk2BNPPBFu+89//mNxcXH28ssvm5nZe++9Z5Js4cKF4THFxcUmyT744INvM+RmtWXLFpNk8+fPNzOz2tpay8jIsLvuuis8prq62tLS0uzBBx80s9iev1nrWn8zs44dO9pf/vKXVrf2dermb9Y61r6ystKOOeYYe/XVVy0nJyd80mwt69/U/M1if/2nTZtm/fv3b3Rfa1l/M7P9ck1DYWGhOnfurJ49e+onP/mJtmzZEu5bsmSJdu7cqdNPPz3clpWVpT59+qioqEiSVFxcrLS0NJ144onhMUOHDlVaWlp4TEuwbds2SdKhhx4qSSotLVV5eXnE3JKSkpSTkxOOO5bnX6c1rP/u3bv1xBNP6IsvvtCwYcNa3drXn3+dWF/7iRMnasyYMTr11FMjtreW9W9q/nViff3XrFmjrKws9ejRQz/60Y/08ccfS2o96y818y+skqTRo0frBz/4gbp166bS0lLddtttOuWUU7RkyRIlJSWpvLxciYmJ6tixY8TtunTpovLycklSeXm5Onfu3ODcnTt3Do+JNjPTddddpxEjRqhPnz6SFI6tS5cuEcd26dJF69evD4+J1flLsb/+K1as0LBhw1RdXa327dtr1qxZ6t27d/gDHetr39T8pdhf+yeeeEJLly7V4sWLG+xrDT/73zR/KfbX/8QTT9TMmTPVs2dPbd68WXfccYeGDx+uVatWtYr1r9Ps0XDhhReG3/fp00eDBw9Wt27d9OKLL+qCCy5o8nZmFvHrtBv71dr1j4mmvLw8LV++XAsWLGiwr/4YPeOOlfnH+vofe+yxKikp0eeff65nnnlGY8eO1fz588P9sb72Tc2/d+/eMb32ZWVlmjJliubNm6fk5OQmj4vV9ffMP5bXX/o6iur07dtXw4YN01FHHaVHH300vOAzVtd/T/v9I5eZmZnq1q2b1qxZI0nKyMjQV199pa1bt0Yct2XLlrDSMjIytHnz5gbn+uSTTxqUXDRMmjRJc+bMUUFBQcSvAq+7krZ+EdafW6zOvzGxtv6JiYk6+uijNXjwYE2fPl39+/fX/fff32rWvqn5NyaW1n7JkiXasmWLBg0apPj4eMXHx2v+/Pl64IEHFB8fH44tVtd/b/PfvXt3g9vE0vo3pl27durbt6/WrFnTan7+pQMQDRUVFSorK1NmZqYkadCgQUpISNCrr74aHrNp0yatXLlSw4cPlyQNGzZM27Zt09tvvx0es2jRIm3bti08JhrMTHl5eXr22Wf1xhtvqEePHhH7e/TooYyMjIi5ffXVV5o/f3447lief2Niaf0bY2aqqamJ+bVvSt38GxNLaz9q1CitWLFCJSUl4dfgwYN1ySWXqKSkREceeWRMr//e5t+mTZsGt4ml9W9MTU2N3n//fWVmZraun/99vXKysrLSli1bZsuWLTNJdu+999qyZcts/fr1VllZaddff70VFRVZaWmpFRQU2LBhw6xr164NPnZy+OGH22uvvWZLly61U045pdGPnfTr18+Ki4utuLjY+vbtG/WPnUyYMMHS0tKssLAw4mM3O3bsCI+56667LC0tzZ599llbsWKFXXTRRY1+7CYW5x/r63/LLbfYm2++aaWlpbZ8+XK79dZbLS4uzubNm2dmsb32Zt88/1hf+8bU//RArK9/fXvOvzWs//XXX2+FhYX28ccf28KFC+3ss8+21NRUW7dunZm1nvXf52goKCgwSQ2+xo4dazt27LDTTz/d0tPTLSEhwY444ggbO3asbdiwIeIcX375peXl5dmhhx5qKSkpdvbZZzc4pqKiwi655BJLTU211NRUu+SSS2zr1q3farLfVmPzlmQzZswIj6mtrbVp06ZZRkaGJSUl2cknn2wrVqyIOE+szj/W13/cuHHWrVs3S0xMtPT0dBs1alQYDGaxvfZm3zz/WF/7xtSPhlhf//r2nH9rWP+6f3chISHBsrKy7IILLrBVq1aF+1vL+vOrsQEAgAu/ewIAALgQDQAAwIVoAAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0APhG3bt313333ec+ft26dQqCQCUlJfttTACig2gA8I0WL16sn/70p816zkceeUSHHHJIs54TwP7X7L8aG0BsSU9Pj/YQALQQvNIAxJjnn39ehxxyiGprayVJJSUlCoJAN954Y3jMVVddpYsuukiSVFRUpJNPPlkpKSnKzs7W5MmT9cUXX4TH1n974oMPPtCIESOUnJys3r1767XXXlMQBJo9e3bEOD7++GONHDlSbdu2Vf/+/VVcXCxJKiws1OWXX65t27YpCAIFQaD8/Pz982AAaFZEAxBjTj75ZFVWVmrZsmWSpPnz5+uwww7T/Pnzw2MKCwuVk5OjFStW6IwzztAFF1yg5cuX68knn9SCBQuUl5fX6Llra2t13nnnqW3btlq0aJEeeughTZ06tdFjp06dqhtuuEElJSXq2bOnLrroIu3atUvDhw/Xfffdpw4dOmjTpk3atGmTbrjhhuZ/IAA0O6IBiDFpaWkaMGCACgsLJX0dCNdee63effddVVZWqry8XB9++KFyc3N199136+KLL9Y111yjY445RsOHD9cDDzygmTNnqrq6usG5582bp48++kgzZ85U//79NWLECN15552NjuOGG27QmDFj1LNnT91+++1av3691q5dq8TERKWlpSkIAmVkZCgjI0Pt27ffnw8JgGZCNAAxKDc3V4WFhTIz/etf/9K5556rPn36aMGCBSooKFCXLl103HHHacmSJXrkkUfUvn378OuMM85QbW2tSktLG5x39erVys7OVkZGRrhtyJAhjY6hX79+4feZmZmSpC1btjTzTAEcSFwICcSg3NxcPfzww3r33XcVFxen3r17KycnR/Pnz9fWrVuVk5Mj6eu3G6666ipNnjy5wTmOOOKIBtvMTEEQuMaQkJAQfl93m7rrLAAcnIgGIAbVXddw3333KScnR0EQKCcnR9OnT9fWrVs1ZcoUSdLxxx+vVatW6eijj3ad97jjjtOGDRu0efNmdenSRdLXH8ncV4mJidq9e/c+3w5AdPH2BBCD6q5r+Nvf/qbc3FxJX4fE0qVLw+sZJOnmm29WcXGxJk6cqJKSEq1Zs0Zz5szRpEmTGj3vaaedpqOOOkpjx47V8uXL9dZbb4UXQnpfgZC+/kRGVVWVXn/9dX366afasWPHt5ovgAODaABi1MiRI7V79+4wEDp27KjevXsrPT1dvXr1kvT1dQfz58/XmjVr9N3vflcDBw7UbbfdFl6DUF+bNm00e/ZsVVVV6YQTTtCVV16pn//855Kk5ORk99iGDx+u8ePH68ILL1R6erp+9atffbvJAjggAjOzaA8CwMHrrbfe0ogRI7R27VodddRR0R4OgP2IaACwT2bNmqX27dvrmGOO0dq1azVlyhR17NhRCxYsiPbQAOxnXAgJYJ9UVlbqpptuUllZmQ477DCdeuqpuueee6I9LAAHAK80AAAAFy6EBAAALkQDAABwIRoAAIAL0QAAAFyIBgAA4EI0AAAAF6IBAAC4EA0AAMCFaAAAAC7/ByctCYt+nJveAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=df.weight)\n",
+    "plt.title('Boxplot of Weight')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cfba8d4-ac12-44bb-8917-22ed65f513b2",
+   "metadata": {},
+   "source": [
+    "Nothing out of the ordinary here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "acd58565-0abe-4245-b505-81fa56a0e106",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:01.326907Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.326552Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.473926Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.473358Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.326892Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(x=df.acceleration)\n",
+    "plt.title('Distribution of Acceleration')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dd0e9f92-fe24-4de5-ba3c-e0cdb9767e0d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:01.474821Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.474614Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.558118Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.557560Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.474806Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=df.acceleration);\n",
+    "plt.title('Boxplot of Acceleration')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1087d45a-dc2f-47f9-be25-f1f28669c2ad",
+   "metadata": {},
+   "source": [
+    "I'm not even sure what acceleration is supposed to be. I assume probably it's 0-60mph time in seconds.. While it's interesting I think it contributes nothing to calculating MPG and looks a bit less than ideal anyway. Everything is close to the same value and there are some outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "291e8653-3d29-4fb5-8105-2fea779cc5ed",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T00:19:01.559014Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.558801Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.693534Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.692969Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.558999Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(x=df.model_year);\n",
+    "plt.title('Distribution of Model Year')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7addd67-2fba-4539-88e3-347164d3cfd7",
+   "metadata": {},
+   "source": [
+    "Model year interestingly has 3 peaks. I'm not going to use this as a feature, not because of this, but because the data only spans 12 years. Model year could be a great indicator of tech but it won't work in this case because there's just not enough data and no real leaps in technology were had in these years anyway. To make predictions on unseen data if the model year is outside 1970-1982 like in the training set then it'll throw the prediction wildly off"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "042416c1-0e56-4269-96c8-6926392e11e7",
@@ -757,15 +1244,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "id": "b3b42cca-6960-4d06-b7c4-1570f09e9fe0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:37.355994Z",
-     "iopub.status.busy": "2022-07-21T20:29:37.355617Z",
-     "iopub.status.idle": "2022-07-21T20:29:37.364909Z",
-     "shell.execute_reply": "2022-07-21T20:29:37.364122Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:37.355966Z"
+     "iopub.execute_input": "2022-08-01T00:19:01.695975Z",
+     "iopub.status.busy": "2022-08-01T00:19:01.695735Z",
+     "iopub.status.idle": "2022-08-01T00:19:01.701574Z",
+     "shell.execute_reply": "2022-08-01T00:19:01.700997Z",
+     "shell.execute_reply.started": "2022-08-01T00:19:01.695960Z"
     },
     "tags": []
    },
diff --git a/eda.ipynb b/eda.ipynb
index bc717be..f0c4669 100644
--- a/eda.ipynb
+++ b/eda.ipynb
@@ -30,11 +30,11 @@
    "id": "5ffa8b01-0b17-4ad8-8e85-f2656da50c9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:49.282341Z",
-     "iopub.status.busy": "2022-07-21T20:29:49.281981Z",
-     "iopub.status.idle": "2022-07-21T20:29:50.950452Z",
-     "shell.execute_reply": "2022-07-21T20:29:50.949681Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:49.282260Z"
+     "iopub.execute_input": "2022-08-01T04:20:10.964571Z",
+     "iopub.status.busy": "2022-08-01T04:20:10.964037Z",
+     "iopub.status.idle": "2022-08-01T04:20:11.911635Z",
+     "shell.execute_reply": "2022-08-01T04:20:11.911048Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:10.964486Z"
     },
     "tags": []
    },
@@ -76,6 +76,23 @@
     "    show_plots(filenames)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "416a9d7e-e2ad-41f0-a674-d13c01f41896",
+   "metadata": {},
+   "source": [
+    "## A bit on engines:\n",
+    "\n",
+    "* A most basic description of an engine is that it's an air pump\n",
+    "* Horsepower = (Torque * RPM) / 5252\n",
+    "* Torque peak is where an engine is operating most efficiently as far as air flow, applied science in action. (Fluid dynamics, resonance)\n",
+    "* Operating above or below the torque peak reduces efficiency and efficiency == fuel economy\n",
+    "* Torque peaks normally occur below 5252rpm, and horsepower peaks above that, so long as the engine can actually rev that high. On a dyno sheet (measuring torque and horsepower vs rpm) you'll see the torque/horsepower lines cross at 5252rpm\n",
+    "* As an engine spins faster, the power output increases until combustion is so inefficient and it produces so little torque that spinning faster produces no more power, if it holds together that long\n",
+    "\n",
+    "Basically an engine that makes lots of power at high rpm but relatively little low end torque (mazda rotary), is going to have poor fuel economy because it spends most of its time outside of its efficiency range. In contrast, diesel engines typically turn lower rpms and create all kinds of torque down low. So not only do they start off making more torque but they are less likely to stray very far from torque peak. This is also why horsepower numbers on a diesel appear low, because they can't rev as high. There's more to it but this should be enough to provide context"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7af7dcdd-9618-4e81-88c8-d2c2cde0fdc2",
@@ -90,11 +107,11 @@
    "id": "3e633f5f-8a7f-4776-a855-f22fcb87e88d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:50.952984Z",
-     "iopub.status.busy": "2022-07-21T20:29:50.952406Z",
-     "iopub.status.idle": "2022-07-21T20:29:50.967175Z",
-     "shell.execute_reply": "2022-07-21T20:29:50.966460Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:50.952957Z"
+     "iopub.execute_input": "2022-08-01T04:20:11.913047Z",
+     "iopub.status.busy": "2022-08-01T04:20:11.912844Z",
+     "iopub.status.idle": "2022-08-01T04:20:11.923117Z",
+     "shell.execute_reply": "2022-08-01T04:20:11.922526Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:11.913032Z"
     },
     "tags": []
    },
@@ -134,7 +151,7 @@
    "id": "b0f65dd4-16b6-4222-8758-71e2ecac473e",
    "metadata": {},
    "source": [
-    "As the number of cylinders, displacement, horsepower, or weight increase, MPG goes down."
+    "As the number of cylinders, displacement, horsepower, or weight increase, MPG goes down. There are some outliers, we'll get to that in a minute"
    ]
   },
   {
@@ -142,7 +159,7 @@
    "id": "61b1b79e-46c2-4e7b-b565-84d1e2045777",
    "metadata": {},
    "source": [
-    "I want to know more:"
+    "There are some other things I'd like to see:"
    ]
   },
   {
@@ -151,11 +168,11 @@
    "id": "7342da99-d04a-4f4f-ad3c-06840144ec48",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:50.969118Z",
-     "iopub.status.busy": "2022-07-21T20:29:50.968685Z",
-     "iopub.status.idle": "2022-07-21T20:29:50.977202Z",
-     "shell.execute_reply": "2022-07-21T20:29:50.976474Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:50.969090Z"
+     "iopub.execute_input": "2022-08-01T04:20:11.924056Z",
+     "iopub.status.busy": "2022-08-01T04:20:11.923876Z",
+     "iopub.status.idle": "2022-08-01T04:20:11.935974Z",
+     "shell.execute_reply": "2022-08-01T04:20:11.935279Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:11.924034Z"
     },
     "tags": []
    },
@@ -165,90 +182,21 @@
     "new_features['efficiency'] = df.horsepower / df.displacement\n",
     "new_features['load'] = df.displacement / df.weight\n",
     "new_features['bore_size'] = df.displacement / df.cylinders\n",
-    "new_features['grunt'] = new_features.bore_size / new_features.efficiency"
+    "new_features['grunt'] = new_features.bore_size * new_features.efficiency * df.horsepower\n",
+    "# new_features['grunt'] = (df.horsepower / new_features.bore_size) * new_features.efficiency"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "89cea145-4b6e-457b-9970-578144c1c364",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:50.979078Z",
-     "iopub.status.busy": "2022-07-21T20:29:50.978361Z",
-     "iopub.status.idle": "2022-07-21T20:29:50.984305Z",
-     "shell.execute_reply": "2022-07-21T20:29:50.983414Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:50.979043Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "merged = df.join(new_features)\n",
-    "del df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0213061d-29c8-4f47-9128-705253bc6320",
-   "metadata": {},
-   "source": [
-    "Check Correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7205bdab-a7df-41b4-9ec0-c1c9e2fe1c03",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:50.985966Z",
-     "iopub.status.busy": "2022-07-21T20:29:50.985567Z",
-     "iopub.status.idle": "2022-07-21T20:29:50.999141Z",
-     "shell.execute_reply": "2022-07-21T20:29:50.998300Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:50.985938Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "weight         -0.831745\n",
-       "displacement   -0.804456\n",
-       "horsepower     -0.777897\n",
-       "cylinders      -0.776090\n",
-       "bore_size      -0.773403\n",
-       "load           -0.714996\n",
-       "grunt          -0.712074\n",
-       "acceleration    0.420414\n",
-       "efficiency      0.509309\n",
-       "origin          0.563833\n",
-       "model_year      0.580091\n",
-       "mpg             1.000000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged.corrwith(y).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
    "id": "9fa0bf3e-d45b-4698-afac-e549db0de148",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.000637Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.000343Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.008598Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.007461Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.000610Z"
+     "iopub.execute_input": "2022-08-01T04:20:11.936853Z",
+     "iopub.status.busy": "2022-08-01T04:20:11.936679Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.329795Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.329065Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:11.936838Z"
     },
     "tags": []
    },
@@ -279,8 +227,7 @@
     }
    ],
    "source": [
-    "make_plots(new_features,y)\n",
-    "del new_features"
+    "make_plots(new_features,y)"
    ]
   },
   {
@@ -288,73 +235,39 @@
    "id": "5cbe16d7-24ef-4ceb-acd1-0dcecfdc96c2",
    "metadata": {},
    "source": [
-    "* HP per cubic inch is a measure of engine efficiency, as this increases so does MPG\n",
-    "* Load is a metric of how hard the engine has to work compared to its size. Engines that work hard use more fuel and a small engine working really hard can use more fuel than a big engine not doing much\n",
+    "* Efficiency (HP per cubic inch) is a rough measure of engine tech/efficiency, as this increases so does MPG\n",
+    "* Load is a metric of how hard the engine has to work compared to its size. Engines that work hard use more fuel and a small engine working really hard can use more fuel than a big engine that's not doing much\n",
     "* Bore_size is an attempt to describe cylinder bore diameter which gives insight on torque curve\n",
     "* Grunt is an attempt to describe the power curve of an engine, or more specifically the presence/absence of low rpm torque output"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "416a9d7e-e2ad-41f0-a674-d13c01f41896",
+   "id": "dd05abcd-9ac9-4821-b575-ffbf8544db3c",
    "metadata": {},
    "source": [
-    "## A bit on engines:\n",
-    "\n",
-    "* A most basic description of an engine is that it's an air pump\n",
-    "* Horsepower = (Torque * RPM) / 5252\n",
-    "* Torque peak is where an engine is operating most efficiently as far as air flow, applied science in action. (Fluid dynamics, resonance)\n",
-    "* Operating above or below the torque peak reduces efficiency and efficiency == fuel economy\n",
-    "* Torque peaks normally occur below 5252rpm, and horsepower peaks above that, so long as the engine can actually rev that high. On a dyno sheet (measuring torque and horsepower vs rpm) you'll see the torque/horsepower lines cross at 5252rpm\n",
-    "* As an engine spins faster, the power output increases until combustion is so inefficient and it produces so little torque that spinning faster produces no more power, if it holds together that long\n",
-    "\n",
-    "Basically an engine that makes lots of power at high rpm but relatively little low end torque (mazda rotary), is going to have poor fuel economy because it spends most of its time outside of its efficiency range. In contrast, diesel engines typically turn lower rpms and create all kinds of torque down low. So not only do they start off making more torque but they are less likely to stray very far from torque peak. This is also why horsepower numbers on a diesel appear low, because they can't rev as high. There's more to it than this but this should be enough to provide context."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15d0d27b-5f92-4648-ad5c-35cc811430b3",
-   "metadata": {},
-   "source": [
-    "## Some stats"
+    "Merge new with the old"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "8710cba8-6b7e-4219-98b9-b7d5a1b4f4b9",
+   "execution_count": 5,
+   "id": "89cea145-4b6e-457b-9970-578144c1c364",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.012153Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.011763Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.019164Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.018305Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.012124Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.331981Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.331135Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.338040Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.337017Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.331935Z"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean MPG: 23.51\n",
-      "Mean Weight: 2970.59\n",
-      "Mean Horsepower: 104.12\n",
-      "efficiency mean: 0.61\n",
-      "load mean: 0.06\n",
-      "bore_size mean: 33.36\n",
-      "grunt mean: 62.78\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(f'''Mean MPG: {y.mean():.2f}\n",
-    "Mean Weight: {merged.weight.mean():.2f}\n",
-    "Mean Horsepower: {merged.horsepower.mean():.2f}''')\n",
-    "\n",
-    "for col in merged.columns[9:]:\n",
-    "    print(f'{col} mean: {merged[col].mean():.2f}')"
+    "merged = df.join(new_features)\n",
+    "del new_features\n",
+    "del df"
    ]
   },
   {
@@ -362,20 +275,29 @@
    "id": "d39b59e4-e596-4fc9-b886-1e6d314f597e",
    "metadata": {},
    "source": [
-    "### What's all that on the edges?"
+    "# What's all that on the edges?\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7ee071-8aa4-4a8d-9e8e-480f3b9da9da",
+   "metadata": {},
+   "source": [
+    "## Rotaries"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "dbbfdab6-1cca-4329-a2ae-9258678ab0b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.020574Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.020285Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.043208Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.042372Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.020547Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.339782Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.339317Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.367167Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.365967Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.339751Z"
     },
     "tags": []
    },
@@ -431,7 +353,7 @@
        "      <td>1.385714</td>\n",
        "      <td>0.030043</td>\n",
        "      <td>23.333333</td>\n",
-       "      <td>16.838488</td>\n",
+       "      <td>3136.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>111</th>\n",
@@ -447,7 +369,7 @@
        "      <td>1.285714</td>\n",
        "      <td>0.032957</td>\n",
        "      <td>23.333333</td>\n",
-       "      <td>18.148148</td>\n",
+       "      <td>2700.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>243</th>\n",
@@ -463,7 +385,7 @@
        "      <td>1.375000</td>\n",
        "      <td>0.029412</td>\n",
        "      <td>26.666667</td>\n",
-       "      <td>19.393939</td>\n",
+       "      <td>4033.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>334</th>\n",
@@ -479,7 +401,7 @@
        "      <td>1.428571</td>\n",
        "      <td>0.028926</td>\n",
        "      <td>23.333333</td>\n",
-       "      <td>16.333333</td>\n",
+       "      <td>3333.333333</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -498,20 +420,21 @@
        "243          77       3       mazda rx-4    1.375000  0.029412  26.666667   \n",
        "334          80       3    mazda rx-7 gs    1.428571  0.028926  23.333333   \n",
        "\n",
-       "         grunt  \n",
-       "71   16.838488  \n",
-       "111  18.148148  \n",
-       "243  19.393939  \n",
-       "334  16.333333  "
+       "           grunt  \n",
+       "71   3136.333333  \n",
+       "111  2700.000000  \n",
+       "243  4033.333333  \n",
+       "334  3333.333333  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "merged[merged.efficiency>1]"
+    "wankels = merged[merged.efficiency>1]\n",
+    "wankels"
    ]
   },
   {
@@ -521,20 +444,98 @@
    "source": [
     "These are the Mazda rotaries, otherwise known as [Wankel Engines](https://en.wikipedia.org/wiki/Wankel_engine)\n",
     "\n",
-    "Efficient power for their size because they can rev to 7000rpm or so, and that's where they make peak power. Not good for fuel economy. Note the low gruntiness"
+    "Efficient power for their size because they can rev to 7000rpm or so, and that's where they make peak power"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5eda8f40-bff6-4715-ba54-b083c74b039d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:12.371148Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.370520Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.488456Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.488016Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.371118Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wankels.efficiency.plot(kind='bar')\n",
+    "plt.xticks(np.arange(4),wankels.car_name)\n",
+    "pd.Series([merged['efficiency'].mean() for i in range(len(wankels))]).plot(kind='line',color='red')\n",
+    "plt.title('Mazda Rotary Efficiency (red line is average)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1476617f-8097-4294-8c42-fb86ff96c1d0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:12.489303Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.489077Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.574526Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.574107Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.489288Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wankels.mpg.plot(kind='bar')\n",
+    "plt.xticks(np.arange(4),wankels.car_name)\n",
+    "pd.Series([merged['mpg'].mean() for i in range(len(wankels))]).plot(kind='line',color='red')\n",
+    "plt.title('Mazda Rotary MPG (red line is average)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f793604-5c51-44b7-9cb6-301151304400",
+   "metadata": {},
+   "source": [
+    "## Diesels"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "20eaa490-f70b-408e-a9fa-c4ba05c8a1ac",
+   "id": "c7aece22-1e78-4a48-b969-f1207ba09aad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.044643Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.044307Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.062694Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.061875Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.044616Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.575478Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.575129Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.588438Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.587822Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.575463Z"
     },
     "tags": []
    },
@@ -577,36 +578,52 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>274</th>\n",
-       "      <td>20.3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>103.0</td>\n",
-       "      <td>2830.0</td>\n",
-       "      <td>15.9</td>\n",
+       "      <th>244</th>\n",
+       "      <td>43.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>21.5</td>\n",
        "      <td>78</td>\n",
        "      <td>2</td>\n",
-       "      <td>audi 5000</td>\n",
-       "      <td>0.786260</td>\n",
-       "      <td>0.046290</td>\n",
-       "      <td>26.2</td>\n",
-       "      <td>33.322330</td>\n",
+       "      <td>volkswagen rabbit custom diesel</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.045340</td>\n",
+       "      <td>22.500000</td>\n",
+       "      <td>576.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>297</th>\n",
-       "      <td>25.4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>3530.0</td>\n",
-       "      <td>20.1</td>\n",
-       "      <td>79</td>\n",
+       "      <th>325</th>\n",
+       "      <td>44.3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2085.0</td>\n",
+       "      <td>21.7</td>\n",
+       "      <td>80</td>\n",
        "      <td>2</td>\n",
-       "      <td>mercedes benz 300d</td>\n",
-       "      <td>0.420765</td>\n",
-       "      <td>0.051841</td>\n",
-       "      <td>36.6</td>\n",
-       "      <td>86.984416</td>\n",
+       "      <td>vw rabbit c (diesel)</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.043165</td>\n",
+       "      <td>22.500000</td>\n",
+       "      <td>576.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>43.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2335.0</td>\n",
+       "      <td>23.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw dasher (diesel)</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.038544</td>\n",
+       "      <td>22.500000</td>\n",
+       "      <td>576.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>327</th>\n",
@@ -621,8 +638,56 @@
        "      <td>audi 5000s (diesel)</td>\n",
        "      <td>0.553719</td>\n",
        "      <td>0.041017</td>\n",
-       "      <td>24.2</td>\n",
-       "      <td>43.704478</td>\n",
+       "      <td>24.200000</td>\n",
+       "      <td>897.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>28.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>3230.0</td>\n",
+       "      <td>20.4</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>peugeot 505s turbo diesel</td>\n",
+       "      <td>0.567376</td>\n",
+       "      <td>0.043653</td>\n",
+       "      <td>35.250000</td>\n",
+       "      <td>1600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>30.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volvo diesel</td>\n",
+       "      <td>0.524138</td>\n",
+       "      <td>0.045886</td>\n",
+       "      <td>24.166667</td>\n",
+       "      <td>962.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>386</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>262.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3015.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
+       "      <td>0.324427</td>\n",
+       "      <td>0.086899</td>\n",
+       "      <td>43.666667</td>\n",
+       "      <td>1204.166667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -630,19 +695,31 @@
       ],
       "text/plain": [
        "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
-       "274  20.3          5         131.0       103.0  2830.0          15.9   \n",
-       "297  25.4          5         183.0        77.0  3530.0          20.1   \n",
+       "244  43.1          4          90.0        48.0  1985.0          21.5   \n",
+       "325  44.3          4          90.0        48.0  2085.0          21.7   \n",
+       "326  43.4          4          90.0        48.0  2335.0          23.7   \n",
        "327  36.4          5         121.0        67.0  2950.0          19.9   \n",
+       "358  28.1          4         141.0        80.0  3230.0          20.4   \n",
+       "359  30.7          6         145.0        76.0  3160.0          19.6   \n",
+       "386  38.0          6         262.0        85.0  3015.0          17.0   \n",
        "\n",
-       "     model_year  origin             car_name  efficiency      load  bore_size  \\\n",
-       "274          78       2            audi 5000    0.786260  0.046290       26.2   \n",
-       "297          79       2   mercedes benz 300d    0.420765  0.051841       36.6   \n",
-       "327          80       2  audi 5000s (diesel)    0.553719  0.041017       24.2   \n",
+       "     model_year  origin                           car_name  efficiency  \\\n",
+       "244          78       2    volkswagen rabbit custom diesel    0.533333   \n",
+       "325          80       2               vw rabbit c (diesel)    0.533333   \n",
+       "326          80       2                 vw dasher (diesel)    0.533333   \n",
+       "327          80       2                audi 5000s (diesel)    0.553719   \n",
+       "358          81       2          peugeot 505s turbo diesel    0.567376   \n",
+       "359          81       2                       volvo diesel    0.524138   \n",
+       "386          82       1  oldsmobile cutlass ciera (diesel)    0.324427   \n",
        "\n",
-       "         grunt  \n",
-       "274  33.322330  \n",
-       "297  86.984416  \n",
-       "327  43.704478  "
+       "         load  bore_size        grunt  \n",
+       "244  0.045340  22.500000   576.000000  \n",
+       "325  0.043165  22.500000   576.000000  \n",
+       "326  0.038544  22.500000   576.000000  \n",
+       "327  0.041017  24.200000   897.800000  \n",
+       "358  0.043653  35.250000  1600.000000  \n",
+       "359  0.045886  24.166667   962.666667  \n",
+       "386  0.086899  43.666667  1204.166667  "
       ]
      },
      "execution_count": 9,
@@ -651,28 +728,72 @@
     }
    ],
    "source": [
-    "merged[merged.cylinders==5]"
+    "diesels = merged[merged.car_name.str.contains('diesel')]\n",
+    "diesels"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "33fb0fbd-595d-4129-bfe0-dd4077553f9a",
+   "id": "79979a1e-de58-4610-8878-6f374f500d1c",
    "metadata": {},
    "source": [
-    "Look at the gruntiness and mpg of these diesels! For comparison the first Audi appears to be a gas engine. Consider the displacement and power. The one below as well"
+    "All of the diesels get higher than average MPG"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "92f0bf1a-af7b-4a26-b422-8ca01fdfde1b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:12.590154Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.589539Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.714518Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.713877Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.590124Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diesels.mpg.plot(kind='barh')\n",
+    "plt.yticks(np.arange(len(diesels)),diesels.car_name)\n",
+    "plt.axvline(merged.mpg.mean(),color='red')\n",
+    "plt.title('Diesel MPG (red line is average)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df9d8d17-46ec-4ce8-950e-aa1a24d98d7f",
+   "metadata": {},
+   "source": [
+    "# Interesting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "0fb1ed64-bba6-463c-9a0f-84af360515b5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.064733Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.064060Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.081301Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.080572Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.064704Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.715450Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.715284Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.726019Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.725441Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.715435Z"
     },
     "tags": []
    },
@@ -728,7 +849,7 @@
        "      <td>0.324427</td>\n",
        "      <td>0.086899</td>\n",
        "      <td>43.666667</td>\n",
-       "      <td>134.596078</td>\n",
+       "      <td>1204.166667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -741,11 +862,11 @@
        "     model_year  origin                           car_name  efficiency  \\\n",
        "386          82       1  oldsmobile cutlass ciera (diesel)    0.324427   \n",
        "\n",
-       "         load  bore_size       grunt  \n",
-       "386  0.086899  43.666667  134.596078  "
+       "         load  bore_size        grunt  \n",
+       "386  0.086899  43.666667  1204.166667  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -756,25 +877,39 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0456df70-dc3d-4879-95cb-26e268fea9aa",
+   "id": "1e1ec508-df30-42c6-a63f-aea36c12d2e8",
    "metadata": {},
    "source": [
-    "This is an interesting engine. In fact, [these cars are rumored to be the reason why diesel cars are so unpopular in North America](https://www.autotrader.com/car-news/when-diesel-was-dreadful-oldsmobile-diesels-259997). [Here is a more technical write-up](https://www.dieselworldmag.com/diesel-engines/oldsmobile-350-v8)\n",
-    "\n",
-    "But that's a bit beside the point, the engines above and below for sake of conversation are basically the same, the V6 being the same as the V8 but with 2 less cylinders. So compare the stats between them as gas and diesel\n"
+    "This is an interesting engine. In fact, [these cars are rumored to be the reason why diesel cars are so unpopular in North America](https://www.autotrader.com/car-news/when-diesel-was-dreadful-oldsmobile-diesels-259997). [Here is a more technical write-up](https://www.dieselworldmag.com/diesel-engines/oldsmobile-350-v8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9858dee-1de0-46ab-b46d-baa4cafc0efc",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8625227-6fca-4e92-ba0c-271bbea53c23",
+   "metadata": {},
+   "source": [
+    "Big lazy engines in big heavy cars don't have to have poor MPG!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "c0c4f183-ef44-42ee-b64c-a75c63450d7b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.083196Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.082422Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.103099Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.102309Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.083153Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.727961Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.727510Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.755277Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.754339Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.727919Z"
     },
     "tags": []
    },
@@ -830,7 +965,7 @@
        "      <td>0.357143</td>\n",
        "      <td>0.089744</td>\n",
        "      <td>43.75</td>\n",
-       "      <td>122.500000</td>\n",
+       "      <td>1953.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>363</th>\n",
@@ -846,7 +981,7 @@
        "      <td>0.300000</td>\n",
        "      <td>0.093960</td>\n",
        "      <td>43.75</td>\n",
-       "      <td>145.833333</td>\n",
+       "      <td>1378.125</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -861,12 +996,12 @@
        "298          79       1      cadillac eldorado    0.357143  0.089744   \n",
        "363          81       1  oldsmobile cutlass ls    0.300000  0.093960   \n",
        "\n",
-       "     bore_size       grunt  \n",
-       "298      43.75  122.500000  \n",
-       "363      43.75  145.833333  "
+       "     bore_size     grunt  \n",
+       "298      43.75  1953.125  \n",
+       "363      43.75  1378.125  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -877,23 +1012,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8625227-6fca-4e92-ba0c-271bbea53c23",
+   "id": "2ccec1cb-db88-430c-a118-351da41a23c1",
    "metadata": {},
    "source": [
-    "Big lazy engines in big heavy cars don't have to have poor MPG!"
+    "But some still do"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "8f51f87e-fb76-4c8a-b4bc-05f147fc8efa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.104577Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.104277Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.120670Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.119898Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.104550Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.756201Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.756022Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.770660Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.769833Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.756186Z"
     },
     "tags": []
    },
@@ -949,7 +1084,7 @@
        "      <td>0.494505</td>\n",
        "      <td>0.14744</td>\n",
        "      <td>56.875</td>\n",
-       "      <td>115.013889</td>\n",
+       "      <td>6328.125</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -962,11 +1097,11 @@
        "    model_year  origin                 car_name  efficiency     load  \\\n",
        "13          70       1  buick estate wagon (sw)    0.494505  0.14744   \n",
        "\n",
-       "    bore_size       grunt  \n",
-       "13     56.875  115.013889  "
+       "    bore_size     grunt  \n",
+       "13     56.875  6328.125  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -985,15 +1120,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "7d556866-da6d-48dd-b37a-e59c3155085d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.122000Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.121688Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.127163Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.126389Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.121973Z"
+     "iopub.execute_input": "2022-08-01T04:20:12.772414Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.771774Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.776947Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.776094Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.772385Z"
     },
     "tags": []
    },
@@ -1005,23 +1140,396 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15d5a2c5-cb01-4a54-8ce4-375018ebc79a",
+   "id": "146d6761-455a-407f-b627-24c13586a88f",
    "metadata": {},
    "source": [
-    "What vehicles have the lowest MPG?"
+    "## What vehicles have the Highest MPG?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
+   "id": "558d450a-2649-4005-bbe8-5f8cc509f965",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:12.778488Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.778076Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.930503Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.929878Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.778459Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_mpg = merged.sort_values('mpg').tail(10)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize = (6,5))\n",
+    "ax.barh(top_mpg.car_name,top_mpg.mpg)\n",
+    "for i in ax.patches:\n",
+    "    plt.text(i.get_width()+0.2, i.get_y()+0.5,\n",
+    "             str(round((i.get_width()), 2)),\n",
+    "             fontsize = 10, fontweight ='bold',\n",
+    "             color ='grey')\n",
+    "ax.set_title('Top 10 MPG (red line is average)')\n",
+    "plt.axvline(merged.mpg.mean(),color='red')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "260484cb-5145-4c0f-8952-8f6ba652c8a5",
+   "metadata": {},
+   "source": [
+    "In more detail:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "38935e91-3877-47a3-96d6-cd54e2704bdb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:12.931405Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.931238Z",
+     "iopub.status.idle": "2022-08-01T04:20:12.947214Z",
+     "shell.execute_reply": "2022-08-01T04:20:12.946618Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.931389Z"
+    },
+    "tags": []
+   },
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>car_name</th>\n",
+       "      <th>efficiency</th>\n",
+       "      <th>load</th>\n",
+       "      <th>bore_size</th>\n",
+       "      <th>grunt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>46.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2110.0</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mazda glc</td>\n",
+       "      <td>0.755814</td>\n",
+       "      <td>0.040758</td>\n",
+       "      <td>21.50</td>\n",
+       "      <td>1056.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>44.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>1850.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>honda civic 1500 gl</td>\n",
+       "      <td>0.736264</td>\n",
+       "      <td>0.049189</td>\n",
+       "      <td>22.75</td>\n",
+       "      <td>1122.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>44.3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2085.0</td>\n",
+       "      <td>21.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw rabbit c (diesel)</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.043165</td>\n",
+       "      <td>22.50</td>\n",
+       "      <td>576.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw pickup</td>\n",
+       "      <td>0.536082</td>\n",
+       "      <td>0.045540</td>\n",
+       "      <td>24.25</td>\n",
+       "      <td>676.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>43.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2335.0</td>\n",
+       "      <td>23.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw dasher (diesel)</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.038544</td>\n",
+       "      <td>22.50</td>\n",
+       "      <td>576.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>43.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volkswagen rabbit custom diesel</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.045340</td>\n",
+       "      <td>22.50</td>\n",
+       "      <td>576.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>41.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>2144.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw rabbit</td>\n",
+       "      <td>0.775510</td>\n",
+       "      <td>0.045709</td>\n",
+       "      <td>24.50</td>\n",
+       "      <td>1444.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>40.9</td>\n",
+       "      <td>4</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.5</td>\n",
+       "      <td>1835.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>renault lecar deluxe</td>\n",
+       "      <td>0.629412</td>\n",
+       "      <td>0.046322</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>715.5625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>40.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2110.0</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>datsun 210</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.040284</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>1056.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>39.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>2070.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>78</td>\n",
+       "      <td>3</td>\n",
+       "      <td>datsun b210 gx</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>0.041063</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>1225.0000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "322  46.6          4          86.0        65.0  2110.0          17.9   \n",
+       "329  44.6          4          91.0        67.0  1850.0          13.8   \n",
+       "325  44.3          4          90.0        48.0  2085.0          21.7   \n",
+       "393  44.0          4          97.0        52.0  2130.0          24.6   \n",
+       "326  43.4          4          90.0        48.0  2335.0          23.7   \n",
+       "244  43.1          4          90.0        48.0  1985.0          21.5   \n",
+       "309  41.5          4          98.0        76.0  2144.0          14.7   \n",
+       "330  40.9          4          85.0        53.5  1835.0          17.3   \n",
+       "324  40.8          4          85.0        65.0  2110.0          19.2   \n",
+       "247  39.4          4          85.0        70.0  2070.0          18.6   \n",
+       "\n",
+       "     model_year  origin                         car_name  efficiency  \\\n",
+       "322          80       3                        mazda glc    0.755814   \n",
+       "329          80       3              honda civic 1500 gl    0.736264   \n",
+       "325          80       2             vw rabbit c (diesel)    0.533333   \n",
+       "393          82       2                        vw pickup    0.536082   \n",
+       "326          80       2               vw dasher (diesel)    0.533333   \n",
+       "244          78       2  volkswagen rabbit custom diesel    0.533333   \n",
+       "309          80       2                        vw rabbit    0.775510   \n",
+       "330          80       2             renault lecar deluxe    0.629412   \n",
+       "324          80       3                       datsun 210    0.764706   \n",
+       "247          78       3                   datsun b210 gx    0.823529   \n",
+       "\n",
+       "         load  bore_size      grunt  \n",
+       "322  0.040758      21.50  1056.2500  \n",
+       "329  0.049189      22.75  1122.2500  \n",
+       "325  0.043165      22.50   576.0000  \n",
+       "393  0.045540      24.25   676.0000  \n",
+       "326  0.038544      22.50   576.0000  \n",
+       "244  0.045340      22.50   576.0000  \n",
+       "309  0.045709      24.50  1444.0000  \n",
+       "330  0.046322      21.25   715.5625  \n",
+       "324  0.040284      21.25  1056.2500  \n",
+       "247  0.041063      21.25  1225.0000  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.sort_values('mpg',ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15d5a2c5-cb01-4a54-8ce4-375018ebc79a",
+   "metadata": {},
+   "source": [
+    "## What vehicles have the lowest MPG?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "65588fe3-762f-42b0-9427-feb64275b792",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:12.948105Z",
+     "iopub.status.busy": "2022-08-01T04:20:12.947948Z",
+     "iopub.status.idle": "2022-08-01T04:20:13.102377Z",
+     "shell.execute_reply": "2022-08-01T04:20:13.101717Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:12.948090Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "low_mpg = merged.sort_values('mpg', ascending=False).tail(10)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize = (6,5))\n",
+    "ax.barh(low_mpg.car_name,low_mpg.mpg)\n",
+    "for i in ax.patches:\n",
+    "    plt.text(i.get_width()+0.2, i.get_y()+0.5,\n",
+    "             str(round((i.get_width()), 2)),\n",
+    "             fontsize = 10, fontweight ='bold',\n",
+    "             color ='grey')\n",
+    "ax.set_title('Bottom 10 MPG (red line is average)')\n",
+    "plt.axvline(merged.mpg.mean(),color='red')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d1c5be8-b63f-496e-a2cd-475a47e7a542",
+   "metadata": {},
+   "source": [
+    "In more detail:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "id": "1497a48c-42a3-447e-b1fb-e3a5b78902da",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.128643Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.128285Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.164545Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.163776Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.128603Z"
+     "iopub.execute_input": "2022-08-01T04:20:13.103850Z",
+     "iopub.status.busy": "2022-08-01T04:20:13.103446Z",
+     "iopub.status.idle": "2022-08-01T04:20:13.131408Z",
+     "shell.execute_reply": "2022-08-01T04:20:13.130488Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:13.103820Z"
     },
     "tags": []
    },
@@ -1077,7 +1585,7 @@
        "      <td>0.634868</td>\n",
        "      <td>0.064243</td>\n",
        "      <td>38.000</td>\n",
-       "      <td>59.854922</td>\n",
+       "      <td>4656.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
@@ -1093,7 +1601,7 @@
        "      <td>0.651466</td>\n",
        "      <td>0.070155</td>\n",
        "      <td>38.375</td>\n",
-       "      <td>58.905625</td>\n",
+       "      <td>5000.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
@@ -1109,7 +1617,7 @@
        "      <td>0.597222</td>\n",
        "      <td>0.078007</td>\n",
        "      <td>45.000</td>\n",
-       "      <td>75.348837</td>\n",
+       "      <td>5778.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
@@ -1125,7 +1633,7 @@
        "      <td>0.660377</td>\n",
        "      <td>0.072570</td>\n",
        "      <td>39.750</td>\n",
-       "      <td>60.192857</td>\n",
+       "      <td>5512.500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>103</th>\n",
@@ -1141,7 +1649,7 @@
        "      <td>0.375000</td>\n",
        "      <td>0.080048</td>\n",
        "      <td>50.000</td>\n",
-       "      <td>133.333333</td>\n",
+       "      <td>2812.500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>67</th>\n",
@@ -1157,7 +1665,7 @@
        "      <td>0.484848</td>\n",
        "      <td>0.092597</td>\n",
        "      <td>53.625</td>\n",
-       "      <td>110.601562</td>\n",
+       "      <td>5408.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>124</th>\n",
@@ -1173,7 +1681,7 @@
        "      <td>0.514286</td>\n",
        "      <td>0.095524</td>\n",
        "      <td>43.750</td>\n",
-       "      <td>85.069444</td>\n",
+       "      <td>4050.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
@@ -1189,7 +1697,7 @@
        "      <td>0.469974</td>\n",
        "      <td>0.077296</td>\n",
        "      <td>47.875</td>\n",
-       "      <td>101.867361</td>\n",
+       "      <td>4050.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>95</th>\n",
@@ -1205,7 +1713,7 @@
        "      <td>0.494505</td>\n",
        "      <td>0.091901</td>\n",
        "      <td>56.875</td>\n",
-       "      <td>115.013889</td>\n",
+       "      <td>6328.125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>90</th>\n",
@@ -1221,247 +1729,7 @@
        "      <td>0.461538</td>\n",
        "      <td>0.086632</td>\n",
        "      <td>53.625</td>\n",
-       "      <td>116.187500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>4456.0</td>\n",
-       "      <td>13.5</td>\n",
-       "      <td>72</td>\n",
-       "      <td>1</td>\n",
-       "      <td>oldsmobile delta 88 royale</td>\n",
-       "      <td>0.457143</td>\n",
-       "      <td>0.078546</td>\n",
-       "      <td>43.750</td>\n",
-       "      <td>95.703125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>104</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>167.0</td>\n",
-       "      <td>4906.0</td>\n",
-       "      <td>12.5</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford country</td>\n",
-       "      <td>0.417500</td>\n",
-       "      <td>0.081533</td>\n",
-       "      <td>50.000</td>\n",
-       "      <td>119.760479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>106</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>4499.0</td>\n",
-       "      <td>12.5</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>oldsmobile vista cruiser</td>\n",
-       "      <td>0.514286</td>\n",
-       "      <td>0.077795</td>\n",
-       "      <td>43.750</td>\n",
-       "      <td>85.069444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>3988.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet malibu</td>\n",
-       "      <td>0.414286</td>\n",
-       "      <td>0.087763</td>\n",
-       "      <td>43.750</td>\n",
-       "      <td>105.603448</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>307.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>4098.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet chevelle concours (sw)</td>\n",
-       "      <td>0.423453</td>\n",
-       "      <td>0.074915</td>\n",
-       "      <td>38.375</td>\n",
-       "      <td>90.624038</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>302.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>4294.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford gran torino (sw)</td>\n",
-       "      <td>0.463576</td>\n",
-       "      <td>0.070331</td>\n",
-       "      <td>37.750</td>\n",
-       "      <td>81.432143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>165.0</td>\n",
-       "      <td>4274.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet impala</td>\n",
-       "      <td>0.471429</td>\n",
-       "      <td>0.081891</td>\n",
-       "      <td>43.750</td>\n",
-       "      <td>92.803030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>4746.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>71</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford country squire (sw)</td>\n",
-       "      <td>0.425000</td>\n",
-       "      <td>0.084282</td>\n",
-       "      <td>50.000</td>\n",
-       "      <td>117.647059</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>360.0</td>\n",
-       "      <td>175.0</td>\n",
-       "      <td>3821.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>amc ambassador brougham</td>\n",
-       "      <td>0.486111</td>\n",
-       "      <td>0.094216</td>\n",
-       "      <td>45.000</td>\n",
-       "      <td>92.571429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>440.0</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>4735.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chrysler new yorker brougham</td>\n",
-       "      <td>0.488636</td>\n",
-       "      <td>0.092925</td>\n",
-       "      <td>55.000</td>\n",
-       "      <td>112.558140</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>351.0</td>\n",
-       "      <td>158.0</td>\n",
-       "      <td>4363.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford ltd</td>\n",
-       "      <td>0.450142</td>\n",
-       "      <td>0.080449</td>\n",
-       "      <td>43.875</td>\n",
-       "      <td>97.469146</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>175.0</td>\n",
-       "      <td>4100.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>buick century 350</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.085366</td>\n",
-       "      <td>43.750</td>\n",
-       "      <td>87.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>137</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>4699.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>74</td>\n",
-       "      <td>1</td>\n",
-       "      <td>buick century luxus (sw)</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.074484</td>\n",
-       "      <td>43.750</td>\n",
-       "      <td>102.083333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>175.0</td>\n",
-       "      <td>5140.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>71</td>\n",
-       "      <td>1</td>\n",
-       "      <td>pontiac safari (sw)</td>\n",
-       "      <td>0.437500</td>\n",
-       "      <td>0.077821</td>\n",
-       "      <td>50.000</td>\n",
-       "      <td>114.285714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>215</th>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>318.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3755.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>76</td>\n",
-       "      <td>1</td>\n",
-       "      <td>dodge d100</td>\n",
-       "      <td>0.471698</td>\n",
-       "      <td>0.084687</td>\n",
-       "      <td>39.750</td>\n",
-       "      <td>84.270000</td>\n",
+       "      <td>4900.500</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1479,640 +1747,146 @@
        "42   12.0          8         383.0       180.0  4955.0          11.5   \n",
        "95   12.0          8         455.0       225.0  4951.0          11.0   \n",
        "90   12.0          8         429.0       198.0  4952.0          11.5   \n",
-       "69   12.0          8         350.0       160.0  4456.0          13.5   \n",
-       "104  12.0          8         400.0       167.0  4906.0          12.5   \n",
-       "106  12.0          8         350.0       180.0  4499.0          12.5   \n",
-       "87   13.0          8         350.0       145.0  3988.0          13.0   \n",
-       "73   13.0          8         307.0       130.0  4098.0          14.0   \n",
-       "74   13.0          8         302.0       140.0  4294.0          16.0   \n",
-       "62   13.0          8         350.0       165.0  4274.0          12.0   \n",
-       "43   13.0          8         400.0       170.0  4746.0          12.0   \n",
-       "96   13.0          8         360.0       175.0  3821.0          11.0   \n",
-       "94   13.0          8         440.0       215.0  4735.0          11.0   \n",
-       "92   13.0          8         351.0       158.0  4363.0          13.0   \n",
-       "85   13.0          8         350.0       175.0  4100.0          13.0   \n",
-       "137  13.0          8         350.0       150.0  4699.0          14.5   \n",
-       "44   13.0          8         400.0       175.0  5140.0          12.0   \n",
-       "215  13.0          8         318.0       150.0  3755.0          14.0   \n",
        "\n",
-       "     model_year  origin                          car_name  efficiency  \\\n",
-       "28           70       1                          hi 1200d    0.634868   \n",
-       "26           70       1                         chevy c20    0.651466   \n",
-       "25           70       1                         ford f250    0.597222   \n",
-       "27           70       1                        dodge d200    0.660377   \n",
-       "103          73       1                  chevrolet impala    0.375000   \n",
-       "67           72       1                   mercury marquis    0.484848   \n",
-       "124          73       1                  oldsmobile omega    0.514286   \n",
-       "42           71       1                 dodge monaco (sw)    0.469974   \n",
-       "95           73       1          buick electra 225 custom    0.494505   \n",
-       "90           73       1          mercury marquis brougham    0.461538   \n",
-       "69           72       1        oldsmobile delta 88 royale    0.457143   \n",
-       "104          73       1                      ford country    0.417500   \n",
-       "106          73       1          oldsmobile vista cruiser    0.514286   \n",
-       "87           73       1                  chevrolet malibu    0.414286   \n",
-       "73           72       1  chevrolet chevelle concours (sw)    0.423453   \n",
-       "74           72       1             ford gran torino (sw)    0.463576   \n",
-       "62           72       1                  chevrolet impala    0.471429   \n",
-       "43           71       1          ford country squire (sw)    0.425000   \n",
-       "96           73       1           amc ambassador brougham    0.486111   \n",
-       "94           73       1      chrysler new yorker brougham    0.488636   \n",
-       "92           73       1                          ford ltd    0.450142   \n",
-       "85           73       1                 buick century 350    0.500000   \n",
-       "137          74       1          buick century luxus (sw)    0.428571   \n",
-       "44           71       1               pontiac safari (sw)    0.437500   \n",
-       "215          76       1                        dodge d100    0.471698   \n",
+       "     model_year  origin                  car_name  efficiency      load  \\\n",
+       "28           70       1                  hi 1200d    0.634868  0.064243   \n",
+       "26           70       1                 chevy c20    0.651466  0.070155   \n",
+       "25           70       1                 ford f250    0.597222  0.078007   \n",
+       "27           70       1                dodge d200    0.660377  0.072570   \n",
+       "103          73       1          chevrolet impala    0.375000  0.080048   \n",
+       "67           72       1           mercury marquis    0.484848  0.092597   \n",
+       "124          73       1          oldsmobile omega    0.514286  0.095524   \n",
+       "42           71       1         dodge monaco (sw)    0.469974  0.077296   \n",
+       "95           73       1  buick electra 225 custom    0.494505  0.091901   \n",
+       "90           73       1  mercury marquis brougham    0.461538  0.086632   \n",
        "\n",
-       "         load  bore_size       grunt  \n",
-       "28   0.064243     38.000   59.854922  \n",
-       "26   0.070155     38.375   58.905625  \n",
-       "25   0.078007     45.000   75.348837  \n",
-       "27   0.072570     39.750   60.192857  \n",
-       "103  0.080048     50.000  133.333333  \n",
-       "67   0.092597     53.625  110.601562  \n",
-       "124  0.095524     43.750   85.069444  \n",
-       "42   0.077296     47.875  101.867361  \n",
-       "95   0.091901     56.875  115.013889  \n",
-       "90   0.086632     53.625  116.187500  \n",
-       "69   0.078546     43.750   95.703125  \n",
-       "104  0.081533     50.000  119.760479  \n",
-       "106  0.077795     43.750   85.069444  \n",
-       "87   0.087763     43.750  105.603448  \n",
-       "73   0.074915     38.375   90.624038  \n",
-       "74   0.070331     37.750   81.432143  \n",
-       "62   0.081891     43.750   92.803030  \n",
-       "43   0.084282     50.000  117.647059  \n",
-       "96   0.094216     45.000   92.571429  \n",
-       "94   0.092925     55.000  112.558140  \n",
-       "92   0.080449     43.875   97.469146  \n",
-       "85   0.085366     43.750   87.500000  \n",
-       "137  0.074484     43.750  102.083333  \n",
-       "44   0.077821     50.000  114.285714  \n",
-       "215  0.084687     39.750   84.270000  "
+       "     bore_size     grunt  \n",
+       "28      38.000  4656.125  \n",
+       "26      38.375  5000.000  \n",
+       "25      45.000  5778.125  \n",
+       "27      39.750  5512.500  \n",
+       "103     50.000  2812.500  \n",
+       "67      53.625  5408.000  \n",
+       "124     43.750  4050.000  \n",
+       "42      47.875  4050.000  \n",
+       "95      56.875  6328.125  \n",
+       "90      53.625  4900.500  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "merged.sort_values('mpg').head(25)"
+    "merged.sort_values('mpg').head(10)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "146d6761-455a-407f-b627-24c13586a88f",
+   "id": "15d0d27b-5f92-4648-ad5c-35cc811430b3",
    "metadata": {},
    "source": [
-    "What vehicles have the Highest MPG?"
+    "## Some stats"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "38935e91-3877-47a3-96d6-cd54e2704bdb",
+   "execution_count": 19,
+   "id": "8710cba8-6b7e-4219-98b9-b7d5a1b4f4b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.166013Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.165720Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.199742Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.199027Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.165986Z"
+     "iopub.execute_input": "2022-08-01T04:20:13.132876Z",
+     "iopub.status.busy": "2022-08-01T04:20:13.132574Z",
+     "iopub.status.idle": "2022-08-01T04:20:13.142096Z",
+     "shell.execute_reply": "2022-08-01T04:20:13.141321Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:13.132851Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean MPG: 23.51\n",
+      "Mean Weight: 2975.41\n",
+      "Mean Horsepower: 104.12\n",
+      "efficiency mean: 0.61\n",
+      "load mean: 0.06\n",
+      "bore_size mean: 33.36\n",
+      "grunt mean: 2060.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'''Mean MPG: {y.mean():.2f}\n",
+    "Mean Weight: {merged.weight.mean():.2f}\n",
+    "Mean Horsepower: {merged.horsepower.mean():.2f}''')\n",
+    "\n",
+    "for col in merged.columns[9:]:\n",
+    "    print(f'{col} mean: {merged[col].mean():.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0213061d-29c8-4f47-9128-705253bc6320",
+   "metadata": {},
+   "source": [
+    "Check Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7205bdab-a7df-41b4-9ec0-c1c9e2fe1c03",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-08-01T04:20:13.143792Z",
+     "iopub.status.busy": "2022-08-01T04:20:13.143335Z",
+     "iopub.status.idle": "2022-08-01T04:20:13.153208Z",
+     "shell.execute_reply": "2022-08-01T04:20:13.152547Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:13.143758Z"
     },
     "tags": []
    },
    "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>mpg</th>\n",
-       "      <th>cylinders</th>\n",
-       "      <th>displacement</th>\n",
-       "      <th>horsepower</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>acceleration</th>\n",
-       "      <th>model_year</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>car_name</th>\n",
-       "      <th>efficiency</th>\n",
-       "      <th>load</th>\n",
-       "      <th>bore_size</th>\n",
-       "      <th>grunt</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>322</th>\n",
-       "      <td>46.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2110.0</td>\n",
-       "      <td>17.9</td>\n",
-       "      <td>80</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc</td>\n",
-       "      <td>0.755814</td>\n",
-       "      <td>0.040758</td>\n",
-       "      <td>21.500000</td>\n",
-       "      <td>28.446154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>329</th>\n",
-       "      <td>44.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1850.0</td>\n",
-       "      <td>13.8</td>\n",
-       "      <td>80</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic 1500 gl</td>\n",
-       "      <td>0.736264</td>\n",
-       "      <td>0.049189</td>\n",
-       "      <td>22.750000</td>\n",
-       "      <td>30.899254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>325</th>\n",
-       "      <td>44.3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>2085.0</td>\n",
-       "      <td>21.7</td>\n",
-       "      <td>80</td>\n",
-       "      <td>2</td>\n",
-       "      <td>vw rabbit c (diesel)</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.043165</td>\n",
-       "      <td>22.500000</td>\n",
-       "      <td>42.187500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>393</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>2130.0</td>\n",
-       "      <td>24.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>2</td>\n",
-       "      <td>vw pickup</td>\n",
-       "      <td>0.536082</td>\n",
-       "      <td>0.045540</td>\n",
-       "      <td>24.250000</td>\n",
-       "      <td>45.235577</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>326</th>\n",
-       "      <td>43.4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>2335.0</td>\n",
-       "      <td>23.7</td>\n",
-       "      <td>80</td>\n",
-       "      <td>2</td>\n",
-       "      <td>vw dasher (diesel)</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.038544</td>\n",
-       "      <td>22.500000</td>\n",
-       "      <td>42.187500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244</th>\n",
-       "      <td>43.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>1985.0</td>\n",
-       "      <td>21.5</td>\n",
-       "      <td>78</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volkswagen rabbit custom diesel</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.045340</td>\n",
-       "      <td>22.500000</td>\n",
-       "      <td>42.187500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>309</th>\n",
-       "      <td>41.5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>2144.0</td>\n",
-       "      <td>14.7</td>\n",
-       "      <td>80</td>\n",
-       "      <td>2</td>\n",
-       "      <td>vw rabbit</td>\n",
-       "      <td>0.775510</td>\n",
-       "      <td>0.045709</td>\n",
-       "      <td>24.500000</td>\n",
-       "      <td>31.592105</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>330</th>\n",
-       "      <td>40.9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>53.5</td>\n",
-       "      <td>1835.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>80</td>\n",
-       "      <td>2</td>\n",
-       "      <td>renault lecar deluxe</td>\n",
-       "      <td>0.629412</td>\n",
-       "      <td>0.046322</td>\n",
-       "      <td>21.250000</td>\n",
-       "      <td>33.761682</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>324</th>\n",
-       "      <td>40.8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2110.0</td>\n",
-       "      <td>19.2</td>\n",
-       "      <td>80</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 210</td>\n",
-       "      <td>0.764706</td>\n",
-       "      <td>0.040284</td>\n",
-       "      <td>21.250000</td>\n",
-       "      <td>27.788462</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>247</th>\n",
-       "      <td>39.4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2070.0</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>78</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun b210 gx</td>\n",
-       "      <td>0.823529</td>\n",
-       "      <td>0.041063</td>\n",
-       "      <td>21.250000</td>\n",
-       "      <td>25.803571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>39.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>16.9</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota starlet</td>\n",
-       "      <td>0.734177</td>\n",
-       "      <td>0.045014</td>\n",
-       "      <td>19.750000</td>\n",
-       "      <td>26.900862</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>343</th>\n",
-       "      <td>39.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1875.0</td>\n",
-       "      <td>16.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>plymouth champ</td>\n",
-       "      <td>0.744186</td>\n",
-       "      <td>0.045867</td>\n",
-       "      <td>21.500000</td>\n",
-       "      <td>28.890625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>310</th>\n",
-       "      <td>38.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>18.8</td>\n",
-       "      <td>80</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota corolla tercel</td>\n",
-       "      <td>0.674157</td>\n",
-       "      <td>0.045224</td>\n",
-       "      <td>22.250000</td>\n",
-       "      <td>33.004167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>384</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1995.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 310 gx</td>\n",
-       "      <td>0.736264</td>\n",
-       "      <td>0.045614</td>\n",
-       "      <td>22.750000</td>\n",
-       "      <td>30.899254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>382</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1965.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic</td>\n",
-       "      <td>0.736264</td>\n",
-       "      <td>0.046310</td>\n",
-       "      <td>22.750000</td>\n",
-       "      <td>30.899254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>386</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>262.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>3015.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
-       "      <td>0.324427</td>\n",
-       "      <td>0.086899</td>\n",
-       "      <td>43.666667</td>\n",
-       "      <td>134.596078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>377</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>2125.0</td>\n",
-       "      <td>14.7</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>plymouth horizon miser</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.049412</td>\n",
-       "      <td>26.250000</td>\n",
-       "      <td>43.750000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>37.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>2050.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota tercel</td>\n",
-       "      <td>0.696629</td>\n",
-       "      <td>0.043415</td>\n",
-       "      <td>22.250000</td>\n",
-       "      <td>31.939516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>304</th>\n",
-       "      <td>37.3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>2130.0</td>\n",
-       "      <td>14.7</td>\n",
-       "      <td>79</td>\n",
-       "      <td>2</td>\n",
-       "      <td>fiat strada custom</td>\n",
-       "      <td>0.758242</td>\n",
-       "      <td>0.042723</td>\n",
-       "      <td>22.750000</td>\n",
-       "      <td>30.003623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>312</th>\n",
-       "      <td>37.2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>16.4</td>\n",
-       "      <td>80</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 310</td>\n",
-       "      <td>0.755814</td>\n",
-       "      <td>0.042595</td>\n",
-       "      <td>21.500000</td>\n",
-       "      <td>28.446154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>375</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>2025.0</td>\n",
-       "      <td>18.2</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc custom l</td>\n",
-       "      <td>0.747253</td>\n",
-       "      <td>0.044938</td>\n",
-       "      <td>22.750000</td>\n",
-       "      <td>30.444853</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>346</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>1975.0</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 210 mpg</td>\n",
-       "      <td>0.764706</td>\n",
-       "      <td>0.043038</td>\n",
-       "      <td>21.250000</td>\n",
-       "      <td>27.788462</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>320</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>119.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>2434.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>80</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 510 hatchback</td>\n",
-       "      <td>0.773109</td>\n",
-       "      <td>0.048891</td>\n",
-       "      <td>29.750000</td>\n",
-       "      <td>38.480978</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>327</th>\n",
-       "      <td>36.4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>121.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>2950.0</td>\n",
-       "      <td>19.9</td>\n",
-       "      <td>80</td>\n",
-       "      <td>2</td>\n",
-       "      <td>audi 5000s (diesel)</td>\n",
-       "      <td>0.553719</td>\n",
-       "      <td>0.041017</td>\n",
-       "      <td>24.200000</td>\n",
-       "      <td>43.704478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>248</th>\n",
-       "      <td>36.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1800.0</td>\n",
-       "      <td>16.4</td>\n",
-       "      <td>78</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic cvcc</td>\n",
-       "      <td>0.659341</td>\n",
-       "      <td>0.050556</td>\n",
-       "      <td>22.750000</td>\n",
-       "      <td>34.504167</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
-       "322  46.6          4          86.0        65.0  2110.0          17.9   \n",
-       "329  44.6          4          91.0        67.0  1850.0          13.8   \n",
-       "325  44.3          4          90.0        48.0  2085.0          21.7   \n",
-       "393  44.0          4          97.0        52.0  2130.0          24.6   \n",
-       "326  43.4          4          90.0        48.0  2335.0          23.7   \n",
-       "244  43.1          4          90.0        48.0  1985.0          21.5   \n",
-       "309  41.5          4          98.0        76.0  2144.0          14.7   \n",
-       "330  40.9          4          85.0        53.5  1835.0          17.3   \n",
-       "324  40.8          4          85.0        65.0  2110.0          19.2   \n",
-       "247  39.4          4          85.0        70.0  2070.0          18.6   \n",
-       "342  39.1          4          79.0        58.0  1755.0          16.9   \n",
-       "343  39.0          4          86.0        64.0  1875.0          16.4   \n",
-       "310  38.1          4          89.0        60.0  1968.0          18.8   \n",
-       "384  38.0          4          91.0        67.0  1995.0          16.2   \n",
-       "382  38.0          4          91.0        67.0  1965.0          15.0   \n",
-       "386  38.0          6         262.0        85.0  3015.0          17.0   \n",
-       "377  38.0          4         105.0        63.0  2125.0          14.7   \n",
-       "347  37.7          4          89.0        62.0  2050.0          17.3   \n",
-       "304  37.3          4          91.0        69.0  2130.0          14.7   \n",
-       "312  37.2          4          86.0        65.0  2019.0          16.4   \n",
-       "375  37.0          4          91.0        68.0  2025.0          18.2   \n",
-       "346  37.0          4          85.0        65.0  1975.0          19.4   \n",
-       "320  37.0          4         119.0        92.0  2434.0          15.0   \n",
-       "327  36.4          5         121.0        67.0  2950.0          19.9   \n",
-       "248  36.1          4          91.0        60.0  1800.0          16.4   \n",
-       "\n",
-       "     model_year  origin                           car_name  efficiency  \\\n",
-       "322          80       3                          mazda glc    0.755814   \n",
-       "329          80       3                honda civic 1500 gl    0.736264   \n",
-       "325          80       2               vw rabbit c (diesel)    0.533333   \n",
-       "393          82       2                          vw pickup    0.536082   \n",
-       "326          80       2                 vw dasher (diesel)    0.533333   \n",
-       "244          78       2    volkswagen rabbit custom diesel    0.533333   \n",
-       "309          80       2                          vw rabbit    0.775510   \n",
-       "330          80       2               renault lecar deluxe    0.629412   \n",
-       "324          80       3                         datsun 210    0.764706   \n",
-       "247          78       3                     datsun b210 gx    0.823529   \n",
-       "342          81       3                     toyota starlet    0.734177   \n",
-       "343          81       1                     plymouth champ    0.744186   \n",
-       "310          80       3              toyota corolla tercel    0.674157   \n",
-       "384          82       3                      datsun 310 gx    0.736264   \n",
-       "382          82       3                        honda civic    0.736264   \n",
-       "386          82       1  oldsmobile cutlass ciera (diesel)    0.324427   \n",
-       "377          82       1             plymouth horizon miser    0.600000   \n",
-       "347          81       3                      toyota tercel    0.696629   \n",
-       "304          79       2                 fiat strada custom    0.758242   \n",
-       "312          80       3                         datsun 310    0.755814   \n",
-       "375          82       3                 mazda glc custom l    0.747253   \n",
-       "346          81       3                     datsun 210 mpg    0.764706   \n",
-       "320          80       3               datsun 510 hatchback    0.773109   \n",
-       "327          80       2                audi 5000s (diesel)    0.553719   \n",
-       "248          78       3                   honda civic cvcc    0.659341   \n",
-       "\n",
-       "         load  bore_size       grunt  \n",
-       "322  0.040758  21.500000   28.446154  \n",
-       "329  0.049189  22.750000   30.899254  \n",
-       "325  0.043165  22.500000   42.187500  \n",
-       "393  0.045540  24.250000   45.235577  \n",
-       "326  0.038544  22.500000   42.187500  \n",
-       "244  0.045340  22.500000   42.187500  \n",
-       "309  0.045709  24.500000   31.592105  \n",
-       "330  0.046322  21.250000   33.761682  \n",
-       "324  0.040284  21.250000   27.788462  \n",
-       "247  0.041063  21.250000   25.803571  \n",
-       "342  0.045014  19.750000   26.900862  \n",
-       "343  0.045867  21.500000   28.890625  \n",
-       "310  0.045224  22.250000   33.004167  \n",
-       "384  0.045614  22.750000   30.899254  \n",
-       "382  0.046310  22.750000   30.899254  \n",
-       "386  0.086899  43.666667  134.596078  \n",
-       "377  0.049412  26.250000   43.750000  \n",
-       "347  0.043415  22.250000   31.939516  \n",
-       "304  0.042723  22.750000   30.003623  \n",
-       "312  0.042595  21.500000   28.446154  \n",
-       "375  0.044938  22.750000   30.444853  \n",
-       "346  0.043038  21.250000   27.788462  \n",
-       "320  0.048891  29.750000   38.480978  \n",
-       "327  0.041017  24.200000   43.704478  \n",
-       "248  0.050556  22.750000   34.504167  "
+       "weight         -0.832707\n",
+       "displacement   -0.804456\n",
+       "horsepower     -0.777897\n",
+       "cylinders      -0.776090\n",
+       "bore_size      -0.773403\n",
+       "load           -0.724271\n",
+       "grunt          -0.644081\n",
+       "acceleration    0.420414\n",
+       "efficiency      0.509309\n",
+       "origin          0.563833\n",
+       "model_year      0.580091\n",
+       "mpg             1.000000\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "merged.sort_values('mpg',ascending=False).head(25)"
+    "merged.corrwith(y).sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8889b56-a87c-4901-b654-aaf5a4b9fb14",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "Math says to use weight, displacement, horsepower, cylinders...\n",
+    "\n",
+    "While I agree that these are the most important features, there's more to it than just these numbers. Like how a stew is not just a sum of its ingredients."
    ]
   },
   {
@@ -2120,20 +1894,20 @@
    "id": "27e89d6b-7603-403c-8235-e9bad49040b3",
    "metadata": {},
    "source": [
-    "Pick a few to toss into the model and get some numbers out"
+    "I'll test both"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "id": "52d0ffbf-55aa-49b9-b99f-8160bf09cc79",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.201570Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.200911Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.207432Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.206526Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.201525Z"
+     "iopub.execute_input": "2022-08-01T04:20:13.154483Z",
+     "iopub.status.busy": "2022-08-01T04:20:13.154106Z",
+     "iopub.status.idle": "2022-08-01T04:20:13.159628Z",
+     "shell.execute_reply": "2022-08-01T04:20:13.158886Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:13.154458Z"
     },
     "tags": []
    },
@@ -2147,7 +1921,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2158,29 +1932,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "id": "6a4a9e48-57a1-48b6-b289-58bc43584112",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-07-21T20:29:51.209219Z",
-     "iopub.status.busy": "2022-07-21T20:29:51.208620Z",
-     "iopub.status.idle": "2022-07-21T20:29:51.220516Z",
-     "shell.execute_reply": "2022-07-21T20:29:51.219766Z",
-     "shell.execute_reply.started": "2022-07-21T20:29:51.209190Z"
+     "iopub.execute_input": "2022-08-01T04:20:13.163437Z",
+     "iopub.status.busy": "2022-08-01T04:20:13.163108Z",
+     "iopub.status.idle": "2022-08-01T04:20:13.175359Z",
+     "shell.execute_reply": "2022-08-01T04:20:13.174579Z",
+     "shell.execute_reply.started": "2022-08-01T04:20:13.163422Z"
     },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "X = merged[[\\\n",
-    "        'horsepower', # overall power\n",
-    "        'bore_size', # \"torque curve\"\n",
-    "        'grunt',\n",
-    "        'load',  # load\n",
-    "       ]]\n",
+    "y.to_csv('data/y.csv',index=False)\n",
     "\n",
-    "X.to_csv('data/X.csv',index=False)\n",
-    "y.to_csv('data/y.csv',index=False)"
+    "merged[[\\\n",
+    "    'horsepower',\n",
+    "    'bore_size',\n",
+    "    'grunt',\n",
+    "    'load',\n",
+    "   ]].to_csv('data/X_engineered.csv',index=False)\n",
+    "\n",
+    "merged[[\\\n",
+    "    'horsepower',\n",
+    "    'weight',\n",
+    "    'displacement',\n",
+    "    'cylinders',\n",
+    "   ]].to_csv('data/X_straight.csv',index=False)"
    ]
   },
   {