2022-07-21 16:31:53 -04:00
{
"cells": [
{
"cell_type": "markdown",
"id": "717f94d3-edfa-4122-9902-212a3456bb8c",
"metadata": {},
"source": [
"[EDA](eda.ipynb)"
]
},
2022-08-01 11:39:46 -04:00
{
"cell_type": "markdown",
"id": "43c4fe12-eb94-434c-8808-15fd7cdcf17d",
"metadata": {},
"source": [
"# Modeling"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c47122c5-bdcd-4b6b-8d22-8958ad910eca",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:27.108272Z",
"iopub.status.busy": "2022-08-01T14:49:27.107585Z",
"iopub.status.idle": "2022-08-01T14:49:28.795162Z",
"shell.execute_reply": "2022-08-01T14:49:28.794368Z",
"shell.execute_reply.started": "2022-08-01T14:49:27.108146Z"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import QuantileTransformer\n",
"from sklearn.linear_model import Ridge,Lasso,LinearRegression\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.svm import LinearSVR\n",
"from sklearn.metrics import mean_squared_error,r2_score"
]
},
{
"cell_type": "markdown",
"id": "1cbc710d-f819-4200-9add-25bc699d8b46",
"metadata": {},
"source": [
"Load in the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "78f30ccd-7153-4d8e-9784-4dce46a46fb2",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:28.797832Z",
"iopub.status.busy": "2022-08-01T14:49:28.797191Z",
"iopub.status.idle": "2022-08-01T14:49:28.858368Z",
"shell.execute_reply": "2022-08-01T14:49:28.857624Z",
"shell.execute_reply.started": "2022-08-01T14:49:28.797801Z"
},
"tags": []
},
"outputs": [],
"source": [
"X_e = pd.read_csv('data/X_engineered.csv')\n",
"X_s = pd.read_csv('data/X_straight.csv')\n",
"y = pd.read_csv('data/y.csv').mpg"
]
},
{
"cell_type": "markdown",
"id": "046eba55-3375-4e28-8776-1bec54e4f72f",
"metadata": {},
"source": [
"Transform it, QuantileTransformer works best in my testing"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cd8eb496-d624-49a5-b1c8-0e1bed4abc1e",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:28.860006Z",
"iopub.status.busy": "2022-08-01T14:49:28.859406Z",
"iopub.status.idle": "2022-08-01T14:49:28.876483Z",
"shell.execute_reply": "2022-08-01T14:49:28.875734Z",
"shell.execute_reply.started": "2022-08-01T14:49:28.859962Z"
},
"tags": []
},
"outputs": [],
"source": [
"qt = QuantileTransformer(n_quantiles=297)\n",
"qt_eng = qt.fit_transform(X_e)\n",
"qt_str = qt.fit_transform(X_s)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9fa8177f-8491-4a10-8515-4678c25a1abe",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:28.878114Z",
"iopub.status.busy": "2022-08-01T14:49:28.877540Z",
"iopub.status.idle": "2022-08-01T14:49:28.888152Z",
"shell.execute_reply": "2022-08-01T14:49:28.887394Z",
"shell.execute_reply.started": "2022-08-01T14:49:28.878087Z"
},
"tags": []
},
"outputs": [],
"source": [
"def run(X,y,model_type):\n",
" r2_test_list = []\n",
" r2_train_list = []\n",
" rmse_test_list = []\n",
" rmse_train_list = []\n",
" \n",
" for i in range(201):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
" \n",
" model = model_type.fit(X_train,y_train)\n",
" test_predict = model.predict(X_test)\n",
" train_predict = model.predict(X_train)\n",
" \n",
" r2_test = r2_score(y_test, test_predict)\n",
" r2_train = r2_score(y_train, train_predict)\n",
" rmse_test = mean_squared_error(y_test, test_predict ,squared=False)\n",
" rmse_train = mean_squared_error(y_train, train_predict ,squared=False)\n",
" \n",
" r2_test_list.append(r2_test)\n",
" r2_train_list.append(r2_train)\n",
" rmse_test_list.append(rmse_test)\n",
" rmse_train_list.append(rmse_train)\n",
"\n",
" plt.subplots(figsize=(10,6))\n",
" plt.title('R-squared over 200 iterations')\n",
" plt.plot(r2_test_list,label='R2 Test')\n",
" plt.plot(r2_train_list,label='R2 Train')\n",
" plt.legend()\n",
" plt.show();\n",
" \n",
" avg = np.mean\n",
" print(f'''\\\n",
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: {min(r2_test_list):.2f} | Min:{min(r2_train_list):.2f} | Min: {min(rmse_test_list):.2f} | Min:{min(rmse_train_list):.2f} |\n",
"| Avg: {avg(r2_test_list):.2f} | Avg:{avg(r2_train_list):.2f} | Avg: {avg(rmse_test_list):.2f} | Avg:{avg(rmse_train_list):.2f} |\n",
"| Max: {max(r2_test_list):.2f} | Max:{max(r2_train_list):.2f} | Max: {max(rmse_test_list):.2f} | Max:{max(rmse_train_list):.2f} |\n",
"'-------------------------------------------------'\n",
" ''')\n",
" \n",
" plt.subplots(figsize=(10,5))\n",
" plt.title('RMSE over 200 iterations')\n",
" plt.plot(rmse_test_list,label='RMSE Test')\n",
" plt.plot(rmse_train_list,label='RMSE Train')\n",
" plt.legend()\n",
" plt.show();"
]
},
{
"cell_type": "markdown",
"id": "a91815c9-27db-495e-bc3d-1d8d160129de",
"metadata": {},
"source": [
"# Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9a4029e4-1e8c-442f-ac56-66a8f6aaa62d",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:28.889515Z",
"iopub.status.busy": "2022-08-01T14:49:28.889180Z",
"iopub.status.idle": "2022-08-01T14:49:29.884061Z",
"shell.execute_reply": "2022-08-01T14:49:29.883304Z",
"shell.execute_reply.started": "2022-08-01T14:49:28.889488Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression With Engineered Features\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.62 | Min:0.71 | Min: 3.16 | Min:3.65 |\n",
"| Avg: 0.73 | Avg:0.74 | Avg: 4.04 | Avg:3.96 |\n",
"| Max: 0.82 | Max:0.77 | Max: 4.88 | Max:4.23 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Linear Regression With Engineered Features')\n",
"run(qt_eng,y,LinearRegression())"
]
},
{
"cell_type": "markdown",
"id": "ec59f39b-e805-42f8-b8c8-2bad572b3e94",
"metadata": {},
"source": [
"This is my most robust model when fed unseen data despite the slightly lower relative scores"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "af02cb2a-a36b-4d57-9a8a-dea4923fd804",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:29.885712Z",
"iopub.status.busy": "2022-08-01T14:49:29.885123Z",
"iopub.status.idle": "2022-08-01T14:49:30.819624Z",
"shell.execute_reply": "2022-08-01T14:49:30.818807Z",
"shell.execute_reply.started": "2022-08-01T14:49:29.885686Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression With Vanilla Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.58 | Min:0.73 | Min: 3.10 | Min:3.51 |\n",
"| Avg: 0.74 | Avg:0.76 | Avg: 3.93 | Avg:3.86 |\n",
"| Max: 0.83 | Max:0.80 | Max: 4.81 | Max:4.11 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Linear Regression With Vanilla Features')\n",
"run(qt_str,y,LinearRegression())"
]
},
{
"cell_type": "markdown",
"id": "6221948e-2f64-4064-9745-04b3a7315b43",
"metadata": {},
"source": [
"The score is about the same as with vanilla features"
]
},
{
"cell_type": "markdown",
"id": "fb179282-11cc-42c8-bf0c-ec5165aa698d",
"metadata": {},
"source": [
"# Ridge"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7b0153c9-559e-4bb2-bfbb-88c856904560",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:30.822950Z",
"iopub.status.busy": "2022-08-01T14:49:30.822472Z",
"iopub.status.idle": "2022-08-01T14:49:31.679151Z",
"shell.execute_reply": "2022-08-01T14:49:31.678407Z",
"shell.execute_reply.started": "2022-08-01T14:49:30.822921Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ridge With Engineered Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.56 | Min:0.71 | Min: 3.14 | Min:3.65 |\n",
"| Avg: 0.73 | Avg:0.74 | Avg: 4.03 | Avg:3.99 |\n",
"| Max: 0.82 | Max:0.78 | Max: 4.92 | Max:4.25 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Ridge With Engineered Features')\n",
"run(qt_eng,y,Ridge())"
]
},
{
"cell_type": "markdown",
"id": "9247f6ce-9225-4379-922a-7d775eb794ef",
"metadata": {},
"source": [
"Ridge performs slightly worse than linear regression"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "029bb1c5-322f-45e2-840e-26325183e175",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:31.680788Z",
"iopub.status.busy": "2022-08-01T14:49:31.680205Z",
"iopub.status.idle": "2022-08-01T14:49:32.521703Z",
"shell.execute_reply": "2022-08-01T14:49:32.520950Z",
"shell.execute_reply.started": "2022-08-01T14:49:31.680761Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ridge With Vanilla Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.61 | Min:0.72 | Min: 2.94 | Min:3.49 |\n",
"| Avg: 0.74 | Avg:0.75 | Avg: 3.92 | Avg:3.87 |\n",
"| Max: 0.83 | Max:0.79 | Max: 4.90 | Max:4.15 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Ridge With Vanilla Features')\n",
"run(qt_str,y,Ridge())"
]
},
{
"cell_type": "markdown",
"id": "bb3e1e40-9979-46fa-9f05-43758bd81da2",
"metadata": {},
"source": [
"There is a slight improvement when using vanilla features"
]
},
{
"cell_type": "markdown",
"id": "b03ef52c-9f3b-4f04-8adb-eda544712a9f",
"metadata": {},
"source": [
"# Lasso"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "29f3bde6-1b2e-425b-833a-d512e0b95948",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:32.523398Z",
"iopub.status.busy": "2022-08-01T14:49:32.522759Z",
"iopub.status.idle": "2022-08-01T14:49:33.371746Z",
"shell.execute_reply": "2022-08-01T14:49:33.371016Z",
"shell.execute_reply.started": "2022-08-01T14:49:32.523371Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lasso With Engineered Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.34 | Min:0.45 | Min: 4.26 | Min:5.11 |\n",
"| Avg: 0.51 | Avg:0.51 | Avg: 5.49 | Avg:5.43 |\n",
"| Max: 0.60 | Max:0.57 | Max: 6.58 | Max:5.64 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Lasso With Engineered Features')\n",
"run(qt_eng,y,Lasso())"
]
},
{
"cell_type": "markdown",
"id": "02e7253e-e056-4731-ada6-968842ac487a",
"metadata": {},
"source": [
"Lasso peforms even worse"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "81916ad8-5658-4f66-b92a-054a951ae24f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:33.373433Z",
"iopub.status.busy": "2022-08-01T14:49:33.372800Z",
"iopub.status.idle": "2022-08-01T14:49:34.299100Z",
"shell.execute_reply": "2022-08-01T14:49:34.298327Z",
"shell.execute_reply.started": "2022-08-01T14:49:33.373405Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lasso With Vanilla Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.37 | Min:0.50 | Min: 4.39 | Min:4.96 |\n",
"| Avg: 0.54 | Avg:0.55 | Avg: 5.29 | Avg:5.26 |\n",
"| Max: 0.61 | Max:0.59 | Max: 6.64 | Max:5.48 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAE/CAYAAABin0ZUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAADyeUlEQVR4nOy9d5wdV3n//z4zt26v6l2WZFu2JXeaG82mE3oJxcDPgQT4hpCQ5BsgCSUhAZLAFwiEZkKzMc1gjAvFvRe5qkurrtX2dveWmTm/P86cKbfs7r1bpJXm83rptdp7Z+fOnXLO53yez/M8QkpJhAgRIkSIECFChNpgHO8DiBAhQoQIESJEmM+IyFSECBEiRIgQIcI0EJGpCBEiRIgQIUKEaSAiUxEiRIgQIUKECNNARKYiRIgQIUKECBGmgYhMRYgQIUKECBEiTAMRmYoQIUKEChBCrBBCjAohzON4DG8XQtx2vD4/QoQIkyMiUxEizAMIIbqEEOPuxH5UCHGtEKIh8P61QggphHh10d/9l/v6u93fE0KILwohDrr72iuE+M8Kn6P/fWXOvugUIIRICiG+LYTYJ4QYEUI8LoR4WdE2LxJCbBNCZIQQfxRCrAy8J4QQ/yaE6HP//bsQQpT7LCnlfillg5TSdv/2DiHE+2bxu61yr1cscAw/lFK+dLY+M0KECNNHRKYiRJg/eJWUsgHYDJwL/H3R+zuAd+lf3An5jcDuwDZ/D1wAXAQ0AlcAj5f7nMC/D87ot6gCLvEpHqdiwAHgMqAZ+ATwEyHEKvdvOoCfu6+3AY8A1wf+/hrgtcAm4BzglcCfzdqXCOB4KlwRIkSYPURkKkKEeQYp5VHgVhSpCuLXwPOFEK3u71cBTwJHA9tcCPxCSnlYKnRJKf+3luNwFaL/EkIcdv/9lxAi6b63VQjxysC2MSFErxDiPPf35wgh7hNCDAohnhBCXB7Y9g4hxGeFEPcCGWBN0fcfk1L+k3vsjpTyJmAvcL67yeuAZ6SUN0gps8A/AZuEEKe7778L+KKU8qCU8hDwReDdFb6jpxQJIT4LXAJ8JajYCSFOF0LcLoToF0JsF0K8KfD31woh/lsIcbMQYgy4QgjxCldNGxZCHBBC/FPgI+9yfw66n/FcIcS7hRD3BPb5PCHEw0KIIffn84rO3aeFEPe6qt1tLrlECJESQvzAVeMG3b9dWP7qRogQoRpEZCpChHkGIcQy4GXArqK3ssCvgLe4v78TKCZKDwB/JYT4cyHE2ZXCW1PEPwDPQZG6TSi16+Puez8G3hrY9kqgV0r5mBBiKfAb4DMo5eivgZ8JIToD278DpSA1AvsmOgiXEKwHnnFf2gg8od+XUo6h1LmN5d53/7+RSSCl/AfgbuCDWrETQtQDtwM/Aha43/lrQojg/t4GfNb9LvcAY6hr0wK8AviAEOK17raXuj9b3M+4v+i7tqHO3ZeBduA/gN8IIdqLPu9q93gSqPMLikQ2A8vdv30/MD7Z944QIcLkiMhUhAjzB78UQoygQlzHgH8ss83/Au8UQjSjwmC/LHr/X4F/A96OCn8dEkK8q2ibX7rKhf73/1U4nrcDn5JSHpNS9gD/jCJBoMjFq4UQde7vb3NfA/hT4GYp5c2usnS7eywvD+z7WinlM1JKS0pZqPD5CCHiwA+B70kpt7kvNwBDRZsOochMufeHgIYaieUrgS4p5XfdY30M+BnwhsA2N0op73W/a1ZKeYeU8in39ydRxPOyKX7eK4CdUsrvu5/3Y2Ab8KrANt+VUu6QUo4DP8FXMAsoEnWalNKWUj4qpRyu4TtHiBChCBGZihBh/uC1UspG4HLgdKCjeAMp5T1AJ0ohusmdUIPv21LKr0opn49SRj4LfEcIcUbR57QE/n2zwvEsIawa7XNfQ0q5C9gKvMolVK/GJ1MrgTcGCRvwAmBxYF8HJj4V4Hqpvg/kgaCvaxRoKtq8CRip8H4TMCpr6/q+Eri46Lu8HVgU2Cb0XYQQF7um+B4hxBBKISq5lhVQfM5xf18a+D0Y1s2gyCOoc3UrcJ0blv13l4xGiBBhmojIVIQI8wxSyjuBa4EvVNjkB8BHKQ3xFe9nXEr5VWAAOLOGQzmMIhMaK9zXNHSo7zXAsy7BAkUuvl9E2OqllJ8LHt5EH+yqSN8GFgKvL1KvnkGFHfW29cBa/DBg6H33/88wNRQf1wHgzqLv0iCl/MAEf/MjVDh2uZSyGfg6ICpsW4zicw7qvB+a9MClLEgp/1lKeSbwPJSq9s7J/i5ChAiTIyJTESLMT/wX8BIhxOYy730ZeAm+mdmDEOIvhRCXCyHSrqn6XajwV3FG31TwY+DjQohO1+T8SRSR07gOeCnwAXxVCnebVwkhrhRCmK4x+nLXCzZV/DdwBirzsNj38wvgLCHE64UQKfe4ngyEAf8X5RtbKoRYgiKe107xc7sJG+JvAtYLId4hhIi7/y4sUvqK0Qj0SymzQoiLUCFQjR7AKfqMIG52P+9t7vV7M4oI3zTZgQshrnB9ciYwjAr72ZP9XYQIESZHRKYiRJiHcD1K/4tK/y9+r19K+fsKYatxVPbaUaAX+AuUsrMnsM2vRbjO1C8qHMZnUF6nJ4GngMfc1/RxHAHuR6kg1wdeP4BSq/4vijwcAP6GKY5HQtWM+jOUF+ho4Djf7u6/B3g9KoQ5AFyMb8oH+AYq8/Ep4GmUofsbU/ls4EvAG4QQA0KIL0spR1CE8S0o1egoypOWnGAffw58yvW/fRLla8I99ox73Pe6YcPnBP9QStmHUpQ+CvQBHwNeKaXsncKxLwJ+iiJSW4E7CZPfCBEi1AhRm00gQoQIESJEiBAhAkTKVIQIESJEiBAhwrQQkakIESJEiBAhQoRpICJTESJEiBAhQoQI00BEpiJEiBAhQoQIEaaBiExFiBAhQoQIESJMA7Hj9cEdHR1y1apVx+vjI0SIECFChAgRpoxHH320V0rZWe6940amVq1axSOPPHK8Pj5ChAgRIkSIEGHKEEJUbLoehfkiRIgQIUKECBGmgYhMRYgQIUKECBEiTAMRmYoQIUKECBEiRJgGjptnKkKECBEiRIgwdRQKBQ4ePEg2mz3eh3JSI5VKsWzZMuLx+JT/JiJTESJEiBAhwjzAwYMHaWxsZNWqVQghjvfhnJSQUtLX18fBgwdZvXr1lP8uCvNFiBAhQoQI8wDZbJb29vaISM0ihBC0t7dXrf5FZCpChAgRIkSYJ4iI1OyjlnMckakIESJEiBAhwpRgmiabN2/mrLPO4lWvehWDg4MAdHV1IYTgE5/4hLdtb28v8XicD37wgwBs376dyy+/nM2bN3PGGWdwzTXXAHDHHXfQ3NzM5s2bvX+/+93vQp978cUXs3nzZlasWEFnZ6e3XVdX15SOe8uWLdx8883TPwEVEHmmIkSIECFChAhTQjqdZsuWLQC8613v4qtf/Sr/8A//AMCaNWu46aab+PSnPw3ADTfcwMaNG72//fCHP8xHPvIRXvOa1wDw1FNPee9dcskl3HTTTRU/98EHHwTg2muv5ZFHHuErX/lKVce9ZcsWHnnkEV7+8pdX9XdTRaRMRYgQIUKEUxqDmTxbDgwe78OYd3juc5/LoUOHvN/T6TRnnHGG193k+uuv501vepP3/pEjR1i2bJn3+9lnnz2tz9+9ezdXXXUV559/Ppdccgnbtm0DFIk766yz2LRpE5deein5fJ5PfvKTXH/99WzevJnrr79+Wp9bDpEyFSFChAgRTml87759/Pedu9j6qasiT9IUYds2v//973nve98bev0tb3kL1113HYsWLcI0TZYsWcLhw4cB+MhHPsILX/hCnve85/HSl76Uq6++mpaWFgDuvvtuNm/e7O3nZz/7GWvXrp3wGK655hq+/vWvs27dOh588EH+/M//nD/84Q986lOf4tZbb2Xp0qUMDg6SSCT41Kc+VZOiNVVEZCpChAgRIpzSGMjkyRYc8rZDMmYe78OZEv7518/w7OHhGd3nmUua+MdXbZxwm/Hxcc+rdP755/OSl7wk9P5VV13FJz7xCRYuXMib3/zm0HtXX301V155Jbf
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Lasso With Vanilla Features')\n",
"run(qt_str,y,Lasso())"
]
},
{
"cell_type": "markdown",
"id": "17dfe090-712a-44b3-861d-3e3e869842f0",
"metadata": {},
"source": [
"Again, slightly better numbers with vanilla features"
]
},
{
"cell_type": "markdown",
"id": "0e026f52-4958-48e1-a214-d64d81d309a7",
"metadata": {},
"source": [
"# SVR"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f9950905-f0bb-4db7-b893-0a27d962fcc1",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:34.300873Z",
"iopub.status.busy": "2022-08-01T14:49:34.300233Z",
"iopub.status.idle": "2022-08-01T14:49:35.125538Z",
"shell.execute_reply": "2022-08-01T14:49:35.124793Z",
"shell.execute_reply.started": "2022-08-01T14:49:34.300844Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinearSVR With Engineered Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.53 | Min:0.60 | Min: 3.48 | Min:4.22 |\n",
"| Avg: 0.65 | Avg:0.65 | Avg: 4.59 | Avg:4.60 |\n",
"| Max: 0.75 | Max:0.70 | Max: 5.68 | Max:4.85 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('LinearSVR With Engineered Features')\n",
"run(qt_eng,y,LinearSVR())"
]
},
{
"cell_type": "markdown",
"id": "6a2779d9-1a9c-4719-b5d7-24a061edc416",
"metadata": {},
"source": [
"SVR brings us back up a bit"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a1fc6b93-6fe9-4b70-a574-567890a233b1",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:35.127199Z",
"iopub.status.busy": "2022-08-01T14:49:35.126590Z",
"iopub.status.idle": "2022-08-01T14:49:35.952920Z",
"shell.execute_reply": "2022-08-01T14:49:35.952171Z",
"shell.execute_reply.started": "2022-08-01T14:49:35.127172Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinearSVR With Vanilla Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.53 | Min:0.64 | Min: 3.43 | Min:3.87 |\n",
"| Avg: 0.68 | Avg:0.69 | Avg: 4.39 | Avg:4.35 |\n",
"| Max: 0.78 | Max:0.73 | Max: 5.91 | Max:4.70 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('LinearSVR With Vanilla Features')\n",
"run(qt_str,y,LinearSVR())"
]
},
{
"cell_type": "markdown",
"id": "7a8e42de-4c8c-4603-8adc-81e97245781d",
"metadata": {},
"source": [
"And again slightly improved numbers with vanilla features on training data"
]
},
{
"cell_type": "markdown",
"id": "188ef03e-5f31-4b85-9249-32f8184f8999",
"metadata": {},
"source": [
"# KNN"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a6244b33-23a6-410f-bca2-139c7cecbbf9",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:35.954613Z",
"iopub.status.busy": "2022-08-01T14:49:35.953980Z",
"iopub.status.idle": "2022-08-01T14:49:37.141682Z",
"shell.execute_reply": "2022-08-01T14:49:37.140855Z",
"shell.execute_reply.started": "2022-08-01T14:49:35.954585Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNeighborsRegressor With Engineered Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.56 | Min:0.79 | Min: 3.15 | Min:2.78 |\n",
"| Avg: 0.73 | Avg:0.82 | Avg: 4.03 | Avg:3.27 |\n",
"| Max: 0.82 | Max:0.87 | Max: 4.99 | Max:3.53 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('KNeighborsRegressor With Engineered Features')\n",
"run(qt_eng,y,KNeighborsRegressor())"
]
},
{
"cell_type": "markdown",
"id": "4228c418-ff65-4529-8069-7e06c9ac251f",
"metadata": {},
"source": [
"This one seems to be overfitting a bit on the training data, test data performance is about the same as the rest"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "41d2acc7-4d4f-41ec-8909-8518fed7140a",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:37.143413Z",
"iopub.status.busy": "2022-08-01T14:49:37.142828Z",
"iopub.status.idle": "2022-08-01T14:49:38.314930Z",
"shell.execute_reply": "2022-08-01T14:49:38.314159Z",
"shell.execute_reply.started": "2022-08-01T14:49:37.143384Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNeighborsRegressor With Vanilla Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.64 | Min:0.80 | Min: 3.13 | Min:2.66 |\n",
"| Avg: 0.75 | Avg:0.83 | Avg: 3.91 | Avg:3.19 |\n",
"| Max: 0.83 | Max:0.87 | Max: 4.90 | Max:3.45 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('KNeighborsRegressor With Vanilla Features')\n",
"run(qt_str,y,KNeighborsRegressor())"
]
},
{
"cell_type": "markdown",
"id": "f0539b99-5d48-4e61-b5ec-0c08a53bbd8a",
"metadata": {},
"source": [
"Nearly identical performance engineered vs vanilla"
]
},
{
"cell_type": "markdown",
"id": "d194490c-6363-4363-b4bc-aaac4e0eeac4",
"metadata": {},
"source": [
"# Tree"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a6a3c5b4-6d4e-4089-8849-32b371414c15",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T14:49:38.316427Z",
"iopub.status.busy": "2022-08-01T14:49:38.316064Z",
"iopub.status.idle": "2022-08-01T14:49:39.339671Z",
"shell.execute_reply": "2022-08-01T14:49:39.338774Z",
"shell.execute_reply.started": "2022-08-01T14:49:38.316399Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DecisionTreeRegressor With Engineered Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.25 | Min:1.00 | Min: 3.53 | Min:0.00 |\n",
"| Avg: 0.59 | Avg:1.00 | Avg: 4.94 | Avg:0.18 |\n",
"| Max: 0.82 | Max:1.00 | Max: 6.49 | Max:0.28 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAE/CAYAAABSE1d1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAACj60lEQVR4nO2dd5idRdn/v3N62bN9szWbnpBGEhJICDWho4CCIIqKiqIgKtiVVwX7+yq2nxRREUUUREGKiPSW0NJ7SN1sstlsL6e3+f0xzzzlnOe0zdmW3J/r4iJ76pynzHzne99zD+OcgyAIgiAIgjBiGe0GEARBEARBjEVIJBEEQRAEQZhAIokgCIIgCMIEEkkEQRAEQRAmkEgiCIIgCIIwgUQSQRAEQRCECSSSCII4LmGMNTPG/Iwx6yi24RrG2LOj9f0EQWSHRBJBjDKMsf2MsZAyYLczxu5njJXonr+fMcYZY5emvO+XyuMfV/52MMbuYIwdVD5rH2PsFxm+R/73mxH7oXnAGHMyxv7AGGthjA0yxtYzxi5Kec05jLEdjLEgY+wlxtgk3XOMMfa/jLFu5b//Y4wxs+/inB/gnJdwzhPKe19mjH1qGH/bZOV82XRteJBzfv5wfSdBEEcHiSSCGBtcwjkvAbAQwCIA30x5/l0A18o/lIH2SgB7dK/5JoAlAE4B4AOwAsB6s+/R/XdTUX9FASiCJrUPsgFoBXAWgDIA3wbwd8bYZOU91QAeVR6vBLAGwMO6918P4H0AFgA4EcB7AXxm2H6EjtF0pAiCGB5IJBHEGIJz3g7gvxBiSc+TAE5jjFUof18IYBOAdt1rTgbwGOe8jQv2c87/PJR2KI7OLxljbcp/v2SMOZXntjPG3qt7rY0x1sUYO0n5exljbDVjrI8xtpExdrbutS8zxn7IGFsFIAhgasrvD3DOb1PanuScPwVgH4DFyksuB7CVc/4I5zwM4DYACxhjJyjPXwvgDs75Qc75IQB3APh4ht+oOjuMsR8COAPAb/QOG2PsBMbYc4yxHsbYTsbYVbr3388Yu5sx9jRjLABgBWPsPYr7NcAYa2WM3ab7yleV//cp33EqY+zjjLHXdZ+5nDH2DmOsX/n/8pRj933G2CrFZXtWEY1gjLkYY39R3LM+5b215meXIIh8IZFEEGMIxlgTgIsA7E55KgzgCQBXK39/DECqAHoTwJcYYzcyxuZnCjPlya0AlkGItQUQ7tT/KM/9DcCHdK+9AEAX53wdY6wRwL8B/ADC6fkKgH8yxmp0r/8ohOPjA9CSrRHKQD8TwFblobkANsrnOecBCDdtrtnzyr/nIgec81sBvAbgJumwMca8AJ4D8FcAE5TffBdjTP95HwbwQ+W3vA4gAHFuygG8B8ANjLH3Ka89U/l/ufIdb6T81kqIY/drAFUAfg7g34yxqpTv+4TSHgfE8QWEOCwDMFF572cBhHL9boIgskMiiSDGBv9ijA1ChJo6AHzX5DV/BvAxxlgZRDjqXynP/xjA/wK4BiIMdYgxdm3Ka/6lOA3yv09naM81AL7HOe/gnHcCuB1C3ABCNFzKGPMof39YeQwAPgLgac7504oT9JzSlot1n30/53wr5zzOOY9l+H4wxuwAHgTwJ875DuXhEgD9KS/thxApZs/3AygZomB8L4D9nPM/Km1dB+CfAD6ge83jnPNVym8Nc85f5pxvVv7eBCEoz8rz+94DYBfn/AHl+/4GYAeAS3Sv+SPn/F3OeQjA36E5jjEIcTSdc57gnK/lnA8M4TcTBKGDRBJBjA3exzn3ATgbwAkAqlNfwDl/HUANhKPzlDJQ6p9PcM7v5JyfBuFk/BDAfYyx2SnfU67773cZ2tMAo8vTojwGzvluANsBXKIIpUuhiaRJAK7UCzEApwOo131Wa/ZDASi5Sg8AiALQ5035AZSmvLwUwGCG50sB+PnQdvKeBGBpym+5BkCd7jWG38IYW6okk3cyxvohHJ20c5mB1GMO5e9G3d/68GoQQhQC4lj9F8BDSnj0/xSRSRDEUUAiiSDGEJzzVwDcD+BnGV7yFwBfRnqoLfVzQpzzOwH0ApgzhKa0QYgESbPymESG3C4DsE0RToAQDQ+kCDEv5/wn+uZl+2LF9fkDgFoAV6S4TVshwn/ytV4A06CF4wzPK//eivxIbVcrgFdSfksJ5/yGLO/5K0RYdCLnvAzAPQBYhtemknrMAXHcD+VsOOcxzvntnPM5AJZDuGAfy/U+giCyQyKJIMYevwRwHmNsoclzvwZwHrQkYBXG2M2MsbMZY24lGflaiDBU6gq3fPgbgP9hjNUoycHfgRBokocAnA/gBmguEpTXXMIYu4AxZlUSis9Wcq3y5W4AsyFW4qXm1TwGYB5j7ArGmEtp1yZdOO7PEHlZjYyxBghBeX+e33sExkTypwDMZIx9lDFmV/47OcWZS8UHoIdzHmaMnQIRipR0AkimfIeep5Xv+7By/j4IIXCfytVwxtgKJQ/NCmAAIvyWyPU+giCyQyKJIMYYSg7QnyGWuac+18M5fyFD+CgEsZqrHUAXgM9BODF7da95khnrJD2WoRk/gMgl2gRgM4B1ymOyHYcBvAHhWjyse7wVwl36FoQoaAXwVeTZ1zBR8+gzELk27bp2XqN8fieAKyBCib0AlkJLZgeA30KsBNwMYAtEIvRv8/luAL8C8AHGWC9j7Nec80EIIXg1hMvTDpHz5czyGTcC+J6SX/YdiLwhKG0PKu1epYTvlunfyDnvhnCAvgygG8DXALyXc96VR9vrAPwDQiBtB/AKjKKWIIghwIYWqicIgiAIgji2ISeJIAiCIAjCBBJJBEEQBEEQJpBIIgiCIAiCMIFEEkEQBEEQhAkkkgiCIAiCIEywDceHVldX88mTJw/HRxMEQRAEQRSVtWvXdnHOa1IfHxaRNHnyZKxZs2Y4PpogCIIgCKKoMMZMN9umcBtBEARBEIQJJJIIgiAIgiBMIJFEEARBEARhwrDkJJkRi8Vw8OBBhMPhkfrK4xKXy4WmpibY7fbRbgpBEARBjGtGTCQdPHgQPp8PkydPBmNspL72uIJzju7ubhw8eBBTpkwZ7eYQBEEQxLhmxMJt4XAYVVVVJJCGEcYYqqqqyK0jCIIgiCIwojlJJJCGHzrGBEEQBFEcjqvEbavVioULF2LevHm45JJL0NfXBwDYv38/GGP49re/rb62q6sLdrsdN910EwBg586dOPvss7Fw4ULMnj0b119/PQDg5ZdfRllZGRYuXKj+9/zzzxu+d+nSpVi4cCGam5tRU1Ojvm7//v15tXvDhg14+umnj/4AEARBEASRNyOWkzQWcLvd2LBhAwDg2muvxZ133olbb70VADB16lQ89dRT+P73vw8AeOSRRzB37lz1vV/4whdwyy234LLLLgMAbN68WX3ujDPOwFNPPZXxe9966y0AwP333481a9bgN7/5TUHt3rBhA9asWYOLL764oPcRBEEQBDF0jisnSc+pp56KQ4cOqX+73W7Mnj1brRT+8MMP46qrrlKfP3z4MJqamtS/58+ff1Tfv2fPHlx44YVYvHgxzjjjDOzYsQOAEGfz5s3DggULcOaZZyIajeI73/kOHn74YSxcuBAPP/zwUX0vQRAEQYw0nHO8+m4nkkk+2k0piOPKSZIkEgm88MILuO666wyPX3311XjooYdQV1cHq9WKhoYGtLW1AQBuueUWrFy5EsuXL8f555+PT3ziEygvLwcAvPbaa1i4cKH6Of/85z8xbdq0rG24/vrrcc8992DGjBl46623cOONN+LFF1/E9773Pfz3v/9FY2Mj+vr64HA48L3vfW9IDhRBEARBjAW2tg3gY/e9jb9ctxSnz6ge7ebkzaiIpNuf3IptbQNF/cw5DaX47iVzs74mFAqpuUCLFy/GeeedZ3j+wgsvxLe//W3U1tbigx/8oOG5T3ziE7jgggvwzDPP4PHHH8dvf/tbbNy4EUDucFsqfr8fq1evxpVXXqk+FolEAACnnXYaPv7xj+Oqq67C5ZdfnvdnEgRBEMRYZSAUAwAcGRhfq6+Pq3CbzElqaWlBNBrFnXfeaXj
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('DecisionTreeRegressor With Engineered Features')\n",
"run(qt_eng,y,DecisionTreeRegressor())"
]
},
{
"cell_type": "markdown",
"id": "1cb7b8cb-f3da-419a-8584-5c619deec302",
"metadata": {},
"source": [
"The tree is quite bad"
]
},
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{
"cell_type": "code",
2022-08-01 11:39:46 -04:00
"execution_count": 16,
"id": "ca6ce2a6-5620-4d7a-bcf4-b555ce36e324",
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"metadata": {
"execution": {
2022-08-01 11:39:46 -04:00
"iopub.execute_input": "2022-08-01T14:49:39.341245Z",
"iopub.status.busy": "2022-08-01T14:49:39.340853Z",
"iopub.status.idle": "2022-08-01T14:49:40.386890Z",
"shell.execute_reply": "2022-08-01T14:49:40.386176Z",
"shell.execute_reply.started": "2022-08-01T14:49:39.341206Z"
2022-07-21 16:31:53 -04:00
},
"tags": []
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DecisionTreeRegressor With Vanilla Features\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".-------------------------------------------------.\n",
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.33 | Min:1.00 | Min: 3.82 | Min:0.00 |\n",
"| Avg: 0.59 | Avg:1.00 | Avg: 4.96 | Avg:0.18 |\n",
"| Max: 0.74 | Max:1.00 | Max: 6.42 | Max:0.28 |\n",
"'-------------------------------------------------'\n",
" \n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAE/CAYAAABSE1d1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAACf9ElEQVR4nO2dd5icVdn/v2f6zPa+2d1sekIaqaTRQg9KURREfRXBV1RUbK+++rMhltdeUBQLih0ElC6d0BICSQghjfRNdjeb7W16Ob8/7uc888zMM2V3Z7Obzf25rr2S6ecp55zvudsRUkowDMMwDMMwiVjGugEMwzAMwzDjERZJDMMwDMMwJrBIYhiGYRiGMYFFEsMwDMMwjAkskhiGYRiGYUxgkcQwDMMwDGMCiySGYU5JhBCNQohBIYR1DNvwfiHEk2P1+wzDZIZFEsOMMUKIw0IIvzZhtwkh7hJCFBpev0sIIYUQVyR97mfa8x/SHjuEED8WQjRr33VICPHTNL+j/n55wg40B4QQTiHEnUKIJiHEgBDidSHEpUnvuUAIsUcI4RNCPCeEmGJ4TQghvi+E6NL+fiCEEGa/JaU8IqUslFJGtc+uF0L89yge21TtetkMbfiblPLi0fpNhmFGBoskhhkfXC6lLASwGMASAF9Oen0vgOvUA22ivRrAAcN7vgxgOYAVAIoAnAfgdbPfMfx9Mq9HMQQ0QZM8BtkAHAVwLoASAF8D8E8hxFTtM5UA/qU9Xw5gM4B7DJ+/EcA7ACwCcDqAywB8dNQOwsBYWqQYhhkdWCQxzDhCStkG4AmQWDLyMIAzhRBl2uN1ALYDaDO85wwA/5ZStkrisJTyz8Nph2bR+ZkQolX7+5kQwqm9tlsIcZnhvTYhRKcQYqn2eJUQYoMQolcI8YYQYq3hveuFEN8RQrwMwAdgetLxe6WUt2htj0kpHwFwCMAy7S1XAdgppbxXShkAcAuARUKI07TXrwPwYylls5SyBcCPAXwozTHqlh0hxHcAnA3gl0YLmxDiNCHEU0KIbiHEW0KIawyfv0sI8WshxGNCCC+A84QQb9esX/1CiKNCiFsMP/mC9m+v9hurhRAfEkK8ZPjONUKI14QQfdq/a5LO3beEEC9rVrYnNdEIIYRLCPFXzXrWq322xvzqMgyTKyySGGYcIYRoAHApgP1JLwUAPATgWu3xBwEkC6BXAHxOCHGTEGJhOjdTjnwFwCqQWFsEsk59VXvtHwDea3jvJQA6pZRbhRD1AB4F8G2Qped/ANwvhKgyvP8DIItPEYCmTI3QJvrZAHZqT80H8IZ6XUrpBVnT5pu9rv1/PrIgpfwKgBcBfFJZ2IQQBQCeAvB3ANXaMf9KCGH8vvcB+I52LC8B8IKuTSmAtwP4uBDiHdp7z9H+LdV+Y2PSsZaDzt1tACoA/ATAo0KIiqTfu15rjwN0fgEShyUAJmuf/RgAf7bjZhgmMyySGGZ88IAQYgDkamoH8A2T9/wZwAeFECUgd9QDSa//H4DvA3g/yA3VIoS4Luk9D2iWBvX3kTTteT+AW6WU7VLKDgDfBIkbgETDFUIIj/b4fdpzAPBfAB6TUj6mWYKe0tryNsN33yWl3CmljEgpw2l+H0IIO4C/AfiTlHKP9nQhgL6kt/aBRIrZ630ACocpGC8DcFhK+UetrVsB3A/g3Yb3PCilfFk71oCUcr2U8k3t8XaQoDw3x997O4B9Usq/aL/3DwB7AFxueM8fpZR7pZR+AP9E3OIYBomjmVLKqJRyi5SyfxjHzDCMARZJDDM+eIeUsgjAWgCnAahMfoOU8iUAVSCLziPaRGl8PSqlvF1KeSbIkvEdAH8QQsxN+p1Sw9/v0rSnDolWnibtOUgp9wPYDeByTShdgbhImgLgaqMQA3AWgEmG7zqa+VQAWqzSXwCEABjjpgYBFCe9vRjAQJrXiwEMyuHt5D0FwMqkY3k/gFrDexKORQixUgsm7xBC9IEsOinXMg3J5xza43rDY6N71QcShQCdqycA3K25R3+giUyGYUYAiySGGUdIKZ8HcBeAH6V5y18BfB6prrbk7/FLKW8H0ANg3jCa0goSCYpG7TmFcrldCWCXJpwAEg1/SRJiBVLK7xmbl+mHNavPnQBqALwrydq0E+T+U+8tADADcXdcwuva/3ciN5LbdRTA80nHUiil/HiGz/wd5BadLKUsAXAHAJHmvckkn3OAzntL1oZLGZZSflNKOQ/AGpAV7IPZPscwTGZYJDHM+ONnAC4SQiw2ee02ABchHgSsI4T4jBBirRDCrQUjXwdyQyVnuOXCPwB8VQhRpQUHfx0k0BR3A7gYwMcRtyJBe8/lQohLhBBWLaB4rRZrlSu/BjAXlImXHFfzbwALhBDvEkK4tHZtN7jj/gyKy6oXQtSBBOVdOf7ucSQGkj8CYLYQ4gNCCLv2d0aSZS6ZIgDdUsqAEGIFyBWp6AAQS/oNI49pv/c+7fq9ByRwH8nWcCHEeVocmhVAP8j9Fs32OYZhMsMiiWHGGVoM0J9Bae7Jr3VLKZ9J4z7yg7K52gB0AvgEyBJz0PCeh0VinaR/p2nGt0GxRNsBvAlgq/acascxABtBVot7DM8fBVmX/h9IFBwF8AXkONYIqnn0UVCsTZuhne/Xvr8DwLtArsQeACsRD2YHgN+AMgHfBLADFAj9m1x+G8DPAbxbCNEjhLhNSjkAEoLXgqw8baCYL2eG77gJwK1afNnXQXFD0Nru09r9sua+W2X8oJSyC2QB+jyALgBfBHCZlLIzh7bXArgPJJB2A3geiaKWYZhhIIbnqmcYhmEYhpnYsCWJYRiGYRjGBBZJDMMwDMMwJrBIYhiGYRiGMYFFEsMwDMMwjAkskhiGYRiGYUywjcaXVlZWyqlTp47GVzMMwzAMw+SVLVu2dEopq5KfHxWRNHXqVGzevHk0vpphGIZhGCavCCFMN9tmdxvDMAzDMIwJLJIYhmEYhmFMYJHEMAzDMAxjAoskhmEYhmEYE1gkMQzDMAzDmMAiiWEYhmEYxgQWSQzDMAzDMCawSGIYhmEYhjGBRRLDMAzDMIwJLJIYhmEYhhkSO1r60D4QGOtmjDoskhiGYRiGGRI3/nkzbn1411g3Y9RhkcQwDMMwzJAYCETw0v5OxGJyrJsyqrBIYnLmX1ubcbBjcKybwTAMw4wxwUgMvb4wdh3rH+umjCoskpickFLii/dtx92vHR3rpjAMwzBjSCwmEYrGAAAv7e8c49aMLiySmJwIRmKIxCSC4ehYN4VhGIYZQ5RAAoCXWSQxDDAYjAAgscQwDMOcugTDNA+47Ba8eqgbgQm8eGaRxOSEl0USwzAMAyAYIVF01sxKBCMxbGnqGeMWjR4skpic8AapU6jOwTAMw5yaqMXy2bOqYLOICR2XxCKJyQlvSLMkhdmSxDAMcyqjFsvlBQ4saSyd0HFJLJKYnGB3G8MwDAMAAW2x7LRZcObMSrzZ0oc+X3iMWzU6sEhicoLdbQzDMAwQXyw77VZMryqElEDH4MTcooRFEpMTbEkaX/hCEUg5sSvdMgwzPlGLZafNAo/dCgDwhSbmAppFEpMTHJM0fjjc6cUZ334a929tGeumMAxzCqJbkmwWeBwkkpS3YaKRk0gSQpQKIe4TQuwRQuwWQqwe7YYx44u4JWlidoSTBSklbnl4J7yhKNr6/GPdHIZhTkFUUWGnzQq3JpL84chYNmnUsOX4vp8DeFxK+W4hhAOAZxTbxIxDBvWYJLYkjSVP7TqO9W91AOBrwTDM2BCPSbLAahEATmF3mxCiGMA5AO4EACllSErZO8rtYsYZvhDHJI01/lAU33x4F2bXFMJps0zoKrcMw4xfguFUd9spK5IATAfQAeCPQojXhRC/F0IUJL9JCHGjEGKzEGJzR0dH3hvKjC36tiQ8MY8Zf954GC29ftx65QK4HVYWrAzDjAnxwG2rLpL8p7BIsgFYCuDXUsolALwAvpT8Jinlb6WUy6WUy6uqqvLcTGasOZWy2waDEXQOBse6GSnsax/EpBIXVk2vgNNm4SB6hmHGBKO7zeOgqJ1T2ZLUDKBZSrlJe3wfSDQxpxC
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
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"source": [
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"print('DecisionTreeRegressor With Vanilla Features')\n",
"run(qt_str,y,DecisionTreeRegressor())"
]
},
{
"cell_type": "markdown",
"id": "a9e31adc-22c5-4c77-bc25-a2000535cb1d",
"metadata": {},
"source": [
"Not quite sure what's going on here but I don't think this one is going to work"
]
},
{
"cell_type": "markdown",
"id": "508fbf7a-6006-40e7-8f87-a218743ac33d",
"metadata": {},
"source": [
"# Conclusion"
]
},
{
"cell_type": "markdown",
"id": "67f5823f-4d25-4e75-95a0-b11249d861e8",
"metadata": {},
"source": [
"After testing lots of models, scalers, and experimenting with different mixtures of features, I've come to the conclusion that it's futile.\n",
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"\n",
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"It's cliché, but at under 400 rows I think I can complain about not having enough data.\n",
"\n",
"QuantileTransformer is the heavy lifter and the model really doesn't seem to matter so long as it's linear. In fact, the scores go down as the models get fancier. I think because it's overthinking such a small data set, it's making connections that are coincidental.\n",
"\n",
"The best metric I found was to make predictions on my own vehicles, in other words it's unseen data that I could just pull from nothing. I had used model year as a feature and it seemed to perform quite well until I introduced a 2012, it predicted MPG into the hundreds. I think model year would be a great feature if the data spanned across decades and there was good representation. It could probably even be made categorical as it would be an indicator of tech advancing over decades. All of the vehicles here are more or less in the same \"tech era\" but the model did seem to find a signal. At any rate it's too unstable if used with data outside the training set.\n",
"\n",
"My personal vehicle predictions are actually quite close. They're a bit on the lower end, but also consider that neither one is even close to being represented in the training data. My truck is a turbo diesel (boost to efficiency) and my car is gas but has some fancy cam phasing and electronic fuel injection. Basically they have higher efficiency compared to anything in the training set."
]
},
{
"cell_type": "markdown",
"id": "4f578d1a-dd8b-45b2-920e-2422edba75b9",
"metadata": {},
"source": [
"# Unseen Data"
]
},
{
"cell_type": "markdown",
"id": "854f2789-c761-4d7d-90e4-63a53f97de1c",
"metadata": {},
"source": [
"I tried to incorporate my unseen data for predictions into the rewrite for modeling but it didn't work and I'm out of both time and energy and beginning to lose my sanity. So here's the old code to prove it works, first with engineered features:"
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]
},
{
"cell_type": "code",
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"execution_count": 20,
"id": "86675084-0072-443a-8963-952c0656148e",
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"metadata": {
"execution": {
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"iopub.execute_input": "2022-08-01T15:09:39.581456Z",
"iopub.status.busy": "2022-08-01T15:09:39.580870Z",
"iopub.status.idle": "2022-08-01T15:09:44.322330Z",
"shell.execute_reply": "2022-08-01T15:09:44.321392Z",
"shell.execute_reply.started": "2022-08-01T15:09:39.581427Z"
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},
"tags": []
},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
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"| Min: 0.59 | Min:0.71 | Min: 3.04 | Min:3.71 |\n",
"| Avg: 0.73 | Avg:0.74 | Avg: 4.04 | Avg:3.96 |\n",
"| Max: 0.83 | Max:0.77 | Max: 4.72 | Max:4.27 |\n",
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"\n"
]
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"f250 turbo diesel Avg: 13.80\n",
"2012 Mustang V8 Avg: 15.80\n",
"2022 Honda Grom Avg: 37.12\n",
"2011 Chevrolet Suburban Avg: 14.37\n",
"2014 Toyota Corolla Avg: 19.17\n",
"2004 Mustang V6 Avg: 15.17\n"
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]
}
],
"source": [
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"X = pd.read_csv('data/X_engineered.csv')\n",
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"y = pd.read_csv('data/y.csv').mpg\n",
"\n",
"eff = 1\n",
"\n",
"v6_s197_05_hp = 216\n",
"v6_s197_05_ci = 245\n",
"v6_s197_05_cl = 6\n",
"v6_s197_05_weight = 3300\n",
"v6_s197_05_eff = (v6_s197_05_hp/\\\n",
" v6_s197_05_ci)*eff\n",
"v6_s197_05 = {'horsepower':v6_s197_05_hp,\n",
" 'bore_size':v6_s197_05_ci/v6_s197_05_cl,\n",
" 'grunt':(v6_s197_05_ci/v6_s197_05_cl)/v6_s197_05_eff,\n",
" 'load':v6_s197_05_ci/v6_s197_05_weight}\n",
"mustang_hp = 400\n",
"mustang_ci = 302\n",
"mustang_cl = 8\n",
"mustang_weight = 3600\n",
"mustang_eff = (mustang_hp/\\\n",
" mustang_ci)*eff\n",
"mustang = {'horsepower':mustang_hp,\n",
" 'bore_size':mustang_ci/mustang_cl,\n",
" 'grunt':(mustang_ci/mustang_cl)/mustang_eff,\n",
" 'load':mustang_ci/mustang_weight}\n",
"corolla_hp = 140\n",
"corolla_ci = 110\n",
"corolla_cl = 4\n",
"corolla_weight = 2800\n",
"corolla_eff = (corolla_hp/\\\n",
" corolla_ci)*eff\n",
"corolla = {'horsepower':corolla_hp,\n",
" 'bore_size':corolla_ci/corolla_cl,\n",
" 'grunt':(corolla_ci/corolla_cl)/corolla_eff,\n",
" 'load':corolla_ci/corolla_weight}\n",
"truck_hp = 500\n",
"truck_ci = 359\n",
"truck_cl = 6\n",
"truck_weight = 6500\n",
"truck_eff = (truck_hp/\\\n",
" truck_ci)*eff\n",
"truck = {'horsepower':truck_hp,\n",
" 'bore_size':truck_ci/truck_cl,\n",
" 'grunt':(truck_ci/truck_cl)/truck_eff,\n",
" 'load':truck_ci/truck_weight}\n",
"grom_hp = 12\n",
"grom_ci = 7.6\n",
"grom_cl = 1\n",
"grom_weight = 400\n",
"grom_eff = (grom_hp/\\\n",
" grom_ci)*eff\n",
"grom = {'horsepower':grom_hp,\n",
" 'bore_size':grom_ci/grom_cl,\n",
" 'grunt':(grom_ci/grom_cl)/grom_eff,\n",
" 'load':grom_ci/grom_weight}\n",
"burb_hp = 320\n",
"burb_ci = 325\n",
"burb_cl = 8\n",
"burb_weight = 6000\n",
"burb_eff = (burb_hp/\\\n",
" burb_ci)*eff\n",
"burb = {'horsepower':burb_hp,\n",
" 'bore_size':burb_ci/burb_cl,\n",
" 'grunt':(burb_ci/burb_cl)/burb_eff,\n",
" 'load':burb_ci/burb_weight}\n",
"\n",
"mdf = pd.DataFrame(mustang,index=[0])\n",
"cdf = pd.DataFrame(corolla,index=[0])\n",
"tdf = pd.DataFrame(truck,index=[0])\n",
"gdf = pd.DataFrame(grom,index=[0])\n",
"bdf = pd.DataFrame(burb,index=[0])\n",
"sm5 = pd.DataFrame(v6_s197_05,index=[0])\n",
"\n",
"mustang_predicts = []\n",
"corolla_predicts = []\n",
"truck_predicts = []\n",
"grom_predicts = []\n",
"burb_predicts = []\n",
"v6_s197_05_predicts = []\n",
"\n",
"r2_test_list = []\n",
"r2_train_list = []\n",
"rmse_test_list = []\n",
"rmse_train_list = []\n",
"\n",
"for i in range(201):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
"\n",
" pipe = Pipeline([\n",
" # ('minmax', MinMaxScaler()),\n",
" # ('ss', StandardScaler()),\n",
" ('qt', QuantileTransformer(n_quantiles=297)),\n",
" # ('rob', RobustScaler()),\n",
" \n",
" ('linreg', LinearRegression()),\n",
" # ('lasso', Lasso()),\n",
" # ('lassocv', LassoCV()),\n",
" # ('ridge', Ridge()),\n",
" # ('ridgeCV', RidgeCV),\n",
" # ('lsvr', LinearSVR())\n",
" ])\n",
"\n",
" model = pipe.fit(X_train,y_train)\n",
" test_predict = model.predict(X_test)\n",
" train_predict = model.predict(X_train)\n",
"\n",
" r2_test = r2_score(y_test, test_predict)\n",
" r2_train = r2_score(y_train, train_predict)\n",
" rmse_test = mean_squared_error(y_test, test_predict ,squared=False)\n",
" rmse_train = mean_squared_error(y_train, train_predict ,squared=False)\n",
"\n",
" r2_test_list.append(r2_test)\n",
" r2_train_list.append(r2_train)\n",
" rmse_test_list.append(rmse_test)\n",
" rmse_train_list.append(rmse_train)\n",
" truck_predicts.append(model.predict(tdf)[0])\n",
" mustang_predicts.append(model.predict(mdf)[0])\n",
" grom_predicts.append(model.predict(gdf)[0])\n",
" burb_predicts.append(model.predict(bdf)[0])\n",
" corolla_predicts.append(model.predict(cdf)[0])\n",
" v6_s197_05_predicts.append(model.predict(sm5)[0])\n",
"\n",
"plt.subplots(figsize=(10,6))\n",
"plt.title('R-squared over 200 iterations')\n",
"plt.plot(r2_test_list,label='R2 Test')\n",
"plt.plot(r2_train_list,label='R2 Train')\n",
"plt.legend()\n",
"plt.show();\n",
"\n",
"avg = np.mean\n",
"print(f'''| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: {min(r2_test_list):.2f} | Min:{min(r2_train_list):.2f} | Min: {min(rmse_test_list):.2f} | Min:{min(rmse_train_list):.2f} |\n",
"| Avg: {avg(r2_test_list):.2f} | Avg:{avg(r2_train_list):.2f} | Avg: {avg(rmse_test_list):.2f} | Avg:{avg(rmse_train_list):.2f} |\n",
"| Max: {max(r2_test_list):.2f} | Max:{max(r2_train_list):.2f} | Max: {max(rmse_test_list):.2f} | Max:{max(rmse_train_list):.2f} |\n",
"''')\n",
"plt.subplots(figsize=(10,5))\n",
"plt.title('RMSE over 200 iterations')\n",
"plt.plot(rmse_test_list,label='RMSE Test')\n",
"plt.plot(rmse_train_list,label='RMSE Train')\n",
"plt.legend()\n",
"plt.show();\n",
"\n",
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"print(f'f250 turbo diesel Avg: {avg(truck_predicts):.2f}')\n",
"print(f'2012 Mustang V8 Avg: {avg(mustang_predicts):.2f}')\n",
"print(f'2022 Honda Grom Avg: {avg(grom_predicts):.2f}')\n",
"print(f'2011 Chevrolet Suburban Avg: {avg(burb_predicts):.2f}')\n",
"print(f'2014 Toyota Corolla Avg: {avg(corolla_predicts):.2f}')\n",
"print(f'2004 Mustang V6 Avg: {avg(v6_s197_05_predicts):.2f}')"
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]
},
{
"cell_type": "markdown",
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"id": "89e10849-d1b2-40ce-8325-acc472d5dc1c",
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"metadata": {},
"source": [
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"and with straight:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a95d6a9f-f76d-4965-8a8f-c6a8b4774311",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-01T15:10:15.600302Z",
"iopub.status.busy": "2022-08-01T15:10:15.599550Z",
"iopub.status.idle": "2022-08-01T15:10:20.329138Z",
"shell.execute_reply": "2022-08-01T15:10:20.328104Z",
"shell.execute_reply.started": "2022-08-01T15:10:15.600272Z"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAF1CAYAAABPmFZlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9d5xlWV3uj7/X3idXrurcPTM9eYZJPcwwJEVAchBR9AIKqFy5fn94Ub9+VdTrlXtFLyqGiw4iCoygJEGSZIYcJ/aEntjT09M5VK46cYf1+2OttcM5+1Sd6q7qqq5ez+vVr9N1zj47nbXXetbzeT6fJaSUWFhYWFhYWFhYnDk4q30CFhYWFhYWFhbnGiwBs7CwsLCwsLA4w7AEzMLCwsLCwsLiDMMSMAsLCwsLCwuLMwxLwCwsLCwsLCwszjAsAbOwsLCwsLCwOMOwBMzCwmLVIYR4thDi0Gqfx2pCCPEeIcQfrfI57BFCPHs1z8HC4lyBJWAWFmscQoj9Qoi6EGJeCHFMCHGLEKJ/tc/rXIAQ4qVCiO8KIab1vf8nIcRA4vOiEOL9QohZ/fn/2/b9XUKIO4UQNf26q9uxpJS/JqX8E/29FSekuh29ve0crpJSfnMlj2thYaFgCZiFxdmBl0sp+4FdwPXA76/u6SwMIYS72uewVAghchlvDwFvB7YBVwI7gL9MfP424FLgAuA5wO8KIV6k91cAPgP8KzAC/AvwGf3+iqLLtVhYWKwhWAJmYXEWQUp5DPgyiohlQgjxS0KIfUKIOSHE40KIX9Dvu0KIdwohxvXnbxZCSDNYa6XteYn9vE0I8a+Jv/9dqzwzQohvCyGuSnx2ixDiH4QQXxBCVIHnCCG2CSE+KYQ4qc/jLYnty/o7U0KIB4CnLHTdQohnCCFu18e+XQjxDP3+q4UQd7Rt+1tCiM/q/xf1NR8QQhzXYb6y/uzZQohDQojfE0IcAz6Qcb8/LKX8kpSyJqWcAv4JeGZik9cDfyKlnJJSPqg//yX92bOBHPC3UsqmlPJdgACe2+UabxFCvF0I0Qd8EdimVc95fS8dIcRbhRCPCSEmhBAfF0KM6u/u1L/lG4UQB4CvL/SbCSHeBPwCijDOCyE+p9+P2oC+d38rhDii//2tEKLYdu9+WwhxQghxVAjxy4lreYkQ4gHdBg8LIf6/hX5fC4tzEZaAWVicRRBC7ABeDOzt8nkf8C7gxVLKAeAZwG798a8CL0MpaDcCr1ri4b+IUns2AXcB/9b2+WuBPwUGgO8DnwPuAbYDPwn8phDihXrbPwYu1v9eCLyh20E1yfi8vq4x4K+BzwshxoDPApcLIS5tO48P6///OXAZirBeos/lfya23QKMohSsNy1+C3gWsEef1whKGbsn8fk9gCGmVwH3yvR6b/cmPs+ElLKK+o2PSCn79b8jwFuAnwZ+Qh93Cri57es/gVLqzH3O/M2klO/V//8Lvf+XZ5zKHwJPQ92764CbgP+R+HwLSiHcDrwRuFnfE4D3Af9Nt8Gr0YTQwsIihiVgFhZnBz4thJgDDgInUASmG0LgaiFEWUp5VEq5R7//8yg15qCUchL4P0s5ASnl+6WUc1LKJir0dp0QYiixyWeklN+TUobANcBGKeX/llK2pJT7UOrQqxPn8qdSykkp5UEUueqGlwKPSik/JKX0pZQfAR5ChWVrqDDfawA0EbsC+KwQQqBI52/p48wBf5Y4B3Ov/lgrVPWFrl8I8XwUUTQEzvjwZhKbzaAIqPk8+Vn750vFfwP+UEp5KPEbvKot3Pg2KWXVXEsPv9lC+AXgf0spT0gpTwL/C3hd4nNPf+5JKb8AzAOXJz57khBiUKuDd53aJVtYrF9YAmZhcXbgp7Wa8GwUwdgAUeacCVP9gVZP/gvwa8BRIcTnhRBX6H1sQxE4gyd6PbgOX75Dh79mgf36ow2JzZL7vgAVQps2/4A/ADafwrlsy/j8CZTyAkrteo3+/2uBT2tithGoAHcmzuFL+n2Dk1LKxgLHBkAI8TR9nFdJKR/Rb8/r18HEpoPAXOLz5Gftny8VFwCfSlzLg0BAfE8hcU97/M0WQvt9f0K/ZzAhpfQTf9eISenPAi8BnhBCfEsI8fQej2lhcc7AEjALi7MIUspvAbcA79R//1oiTPVn+r0vSymfD2xFKUX/pL9+FDgvsbvz23ZfRREWgy2J/78WeAXwPFTYaad+XyRPL/H/g8DjUsrhxL8BKeVLejyXJI6gyEcS5wOH9f+/AmwQKsPwNcThx3GgDlyVOIchncyQdc6ZEEJcjwp1/oqU8tboi8oTdhQVnjO4Dh2i1K/XaiXO4NrE5wsh67wOokLLyXtaklIe7vK9xX6zxa69/b6fr99b/OSlvF1K+QpU6PPTwMd7+Z6FxbkES8AsLM4+/C3wfJFR0kAIsVkI8VPaC9ZEqTCB/vjjwFuEEDu0V+etbV/fDbxaCJEXQrR7xAb0/iZQJO3PFjnH24BZbXAvazXmaiGEMdt/HPh9IcSI9rX99wX29QXgMiHEa4UQOSHEfwGeBPwngFZhPoHKThwFvqrfD1Hk82+EEJv0/dme8KEtCiHE1SjV7L9LKT+XsckHgf+hr+MKVMjzFv3ZN1H3/i3a0P7r+v1e/FDHgbG2cOF7gD8VQlygz22jEOIVC+xjsd/sOHDRAt//COraNgohNqBCr/+6wPbo8yoIIX5BCDEkpfSAWeI2aGFhoWEJmIXFWQbtx/kgkFW00wF+G6VUTKJM2f8//dk/oTIo70EZsv+j7bt/hDLFT6H8Ph9OfPZBVAjqMPAA8MNFzjEAXo4ycD+OUqP+GaXEoPf/hP7sK8CHFtjXBCp54LdRZOJ3gZdJKccTm30YpfT8e1tY7PdQCQs/1GG4rxH7lHrBb6NClu9LhHqTCtYfA4/pa/kW8JdSyi/p826hTPOvB6aBX0GFkluLHVRK+RCKAO3TIcdtwP9FKXFf0X7AHwJPXWA3i/1m70P5tKaFEJ/O+P7bgTtQiQP3odrM2zO2y8LrgP36nv8a8Is9fs/C4pyBSCfoWFhYnCsQQuxEEaB8G2mxsLCwsFhhWAXMwsLCwsLCwuIMwxIwCwsLCwsLC4szDBuCtLCwsLCwsLA4w7AKmIWFhYWFhYXFGYYlYBYWFhYWFhYWZxi5xTdZO9iwYYPcuXPnap+GhYWFhYWFhcWiuPPOO8ellBuzPjurCNjOnTu54447Vvs0LCwsLCwsLCwWhRCi6zJrNgRpYWFhYWFhYXGGYQmYhYWFhYWFhcUZhiVgFhYWFhYWFhZnGGeVB8zCwsLCwsLizMDzPA4dOkSj0VjtU1nzKJVK7Nixg3w+3/N3LAGzsLCwsLCw6MChQ4cYGBhg586dCCFW+3TWLKSUTExMcOjQIS688MKev2dDkBYWFhYWFhYdaDQajI2NWfK1CIQQjI2NLVkptATMwsLCwsLCIhOWfPWGU7lPloBZWFhYWFhYrEm4rsuuXbu4+uqrefnLX8709DQAu3fv5ulPfzpXXXUV1157LR/72Mc6vvvmN7+ZXbt28aQnPYlyucyuXbvYtWsXn/jEJ3o69vT0NO9+97uX83JS6ImACSFeJIR4WAixVwjx1ozPh4QQnxNC3COE2COE+OXEZ/uFEPcJIXYLIe5IvD8qhPiqEOJR/TqyPJdkYWFhYWFhsR5QLpfZvXs3999/P6Ojo9x8880AVCoVPvjBD7Jnzx6+9KUv8Zu/+ZsROTO4+eab2b17N1/4whe4+OKL2b17N7t37+ZVr3pVT8dedQImhHCBm4EXA08CXiOEeFLbZm8GHpBSXgc8G/grIUQh8flzpJS7pJQ3Jt57K3CrlPJS4Fb9t4WFhYWFhYVFB57+9Kdz+PBhAC677DIuvfRSALZt28amTZs4efLkovuoVqv8yq/8Ck95ylO4/vrr+cxnPgPAnj17uOmmm9i1axfXXnstjz76KG9961t57LHH2LVrF7/zO7+z7NfTSxbkTcBeKeU+ACHER4FXAA8ktpHAgFBB0H5gEvAX2e8rUGQN4F+AbwK/1+uJW1hYWFhYWJwZ/K/P7eGBI7PLus8nbRvkj19+VU/
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: 0.66 | Min:0.72 | Min: 2.97 | Min:3.51 |\n",
"| Avg: 0.74 | Avg:0.75 | Avg: 3.93 | Avg:3.86 |\n",
"| Max: 0.85 | Max:0.78 | Max: 4.99 | Max:4.13 |\n",
"\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAE/CAYAAAB1vdadAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAD0pElEQVR4nOy9d5gkV3n2/TtVHacnh8272qBVWoldBZBAKAAWEibIgEnGJvqVwWBsXmxsfxhsA44Ymxcjm2iykUhCIJJAIKGAkFbSKmu1Oc3OTp7p3F1V5/vj1Kmq7q6esDOzM6ut+7rmmpnu6u6q6qpz7nM/9/M8QkpJhAgRIkSIECFChPmFsdg7ECFChAgRIkSI8ExERLIiRIgQIUKECBEWABHJihAhQoQIESJEWABEJCtChAgRIkSIEGEBEJGsCBEiRIgQIUKEBUBEsiJEiBAhQoQIERYAEcmKECFChOOAEGKdECInhDAXcR/eKIS4dbE+P0KECFMjIlkRIpzkEELsF0IU3Ql/QAjxJSFEa+D5LwkhpBDiFXWv+4T7+Fvc/xNCiI8LIQ6777VPCPEfTT5H/3zqhB3oDCCESAohviCEOCCEyAohHhJCvKRumxcJIZ4SQhSEEL8UQpwWeE4IIf5FCDHi/vyrEEKEfZaU8qCUslVKabuvvV0I8YcLeGzr3e8rFtiHr0spX7xQnxkhQoS5ISJZESI8M/ByKWUrsA04H/jruuefBt6s/3En6tcAewLb/DVwEfAcoA14AfBQ2OcEft49r0cxC7iEqH4MiwGHgCuADuCDwDeFEOvd1/QC33Uf7wa2AzcGXn8d8DvAVuBZwMuAP1qwgwhgMRWxCBEiLAwikhUhwjMIUsoB4KcoshXED4BLhRBd7v/XAI8AA4Ftng3cJKXslwr7pZRfOZ79cBWlTwgh+t2fTwghku5zTwohXhbYNiaEGBZCXOD+f4kQ4h4hxLgQ4mEhxJWBbW8XQvyDEOJuoABsrDv+vJTy79x9d6SUtwD7gAvdTV4FPC6l/JaUsgT8HbBVCHGW+/ybgY9LKQ9LKY8AHwfe0uQYPWVJCPEPwGXAp4IKnxDiLCHEz4QQo0KInUKI1wZe/yUhxH8LIX4khMgDLxBCvNRV3yaFEIeEEH8X+Mhfub/H3c94rhDiLUKIuwLv+TwhxP1CiAn39/Pqzt1HhBB3uyrfrS7pRAiREkJ8zVXvxt3XLg//diNEiDBTRCQrQoRnEIQQa4CXALvrnioB3wde7/7/JqCeQN0L/F8hxB8LIc5rFiabIT4AXIIie1tR6tjfuM99A3hDYNurgWEp5YNCiNXAD4GPopSmPwe+I4ToC2z/ByjFqQ04MNVOuEThDOBx96EtwMP6eSllHqXmbQl73v17C9NASvkB4E7g3VrhE0JkgJ8B/wssc4/5v4QQwff7PeAf3GO5C8ijvptO4KXAO4UQv+Nue7n7u9P9jF/XHWs36tx9EugB/h34oRCip+7z3uruTwJ1fkGRyw5grfvadwDF6Y47QoQIUyMiWREiPDPwPSFEFhUqGwT+NmSbrwBvEkJ0oMJp36t7/p+AfwHeiAqjHRFCvLlum++5Sof++T9N9ueNwIellINSyiHg71HkCBTpeIUQosX9//fcxwB+H/iRlPJHrhL1M3dffjvw3l+SUj4upbSklNUmn48QIg58HfiylPIp9+FWYKJu0wkUyQl7fgJoPU7C+TJgv5Tyi+6+Pgh8B/jdwDY3Synvdo+1JKW8XUr5qPv/IyhCesUMP++lwC4p5Vfdz/sG8BTw8sA2X5RSPi2lLALfxFc8qyhydbqU0pZSPiClnDyOY44QIUIAEcmKEOGZgd+RUrYBVwJnAb31G0gp7wL6UIrSLe5EG3zellJeL6W8FKWk/APwP0KIs+s+pzPw87km+7OKWpXpgPsYUsrdwJPAy12i9Qp8knUa8JogkQOeD6wMvNehqU8FuF6trwIVIOgbywHtdZu3A9kmz7cDOSmlnO4zQ3AacHHdsbwRWBHYpuZYhBAXu2b8ISHEBEpRavgum6D+nOP+vzrwfzA8XECRSlDn6qfADW54919dkhohQoQ5ICJZESI8gyClvAP4EvBvTTb5GvA+GkOF9e9TlFJeD4wB5xzHrvSjSIbGOvcxDR0yvBZ4wiVeoEjHV+uIXEZK+c/B3Zvqg13V6QvAcuDVdWrX46jwpd42A2zCDyfWPO/+/TgzQ/1+HQLuqDuWVinlO6d4zf+iwrprpZQdwKcB0WTbetSfc1Dn/ci0Oy5lVUr591LKc4DnoVS4N033uggRIkyNiGRFiPDMwyeAq4QQ20Ke+yRwFb6J2oMQ4s+EEFcKIdKumfvNqDBafYbhTPAN4G+EEH2uufpDKIKncQPwYuCd+CoW7jYvF0JcLYQwXUP2la7XbKb4b+BsVCZkva/oJuBcIcSrhRApd78eCYQTv4Lypa0WQqxCEdIvzfBzj1FrxL8FOEMI8QdCiLj78+w6ZbAebcColLIkhHgOKpSqMQQ4dZ8RxI/cz/s99/t7HYog3zLdjgshXuD68ExgEhU+tKd7XYQIEaZGRLIiRHiGwfVAfQVVpqD+uVEp5W1Nwl9FVDbdADAMvAulBO0NbPMDUVsn66Ymu/FRlJfqEeBR4EH3Mb0fR4Ffo1STGwOPH0KpW/8filQcAv6CGY5VQtW8+iOU12ggsJ9vdN9/CHg1KhQ6BlyMnwwA8BlUJuajwGMoI/lnZvLZwP8DflcIMSaE+KSUMosikq9HqUwDKM9bcor3+GPgw66/7kMo3xTuvhfc/b7bDT9eEnyhlHIEpUC9DxgB3g+8TEo5PIN9XwF8G0WwngTuoJYUR4gQ4Tggjs9qECFChAgRIkSIEGEqREpWhAgRIkSIECHCAiAiWREiRIgQIUKECAuAiGRFiBAhQoQIESIsACKSFSFChAgRIkSIsACISFaECBEiRIgQIcICILbYOxCG3t5euX79+sXejQgRIkSIECFChGnxwAMPDEsp++ofX5Ika/369Wzfvn2xdyNChAgRIkSIEGFaCCFCm9VH4cIIESJEiBAhQoQFQESyIkSIECFChAgRFgARyYoQIUKECBEiRFgALElPVoQIESJEiBBhZqhWqxw+fJhSqbTYu/KMRyqVYs2aNcTj8RltH5GsCBEiRIgQ4STG4cOHaWtrY/369QghFnt3nrGQUjIyMsLhw4fZsGHDjF4ThQsjRIgQIUKEkxilUomenp6IYC0whBD09PTMSjGcEckSQuwXQjwqhNghhGiorSAUPimE2C2EeEQIcUHguWuEEDvd5/5qxnsWIUKECBEiRJgRIoJ1YjDb8zwbJesFUsptUsqLQp57CbDZ/bkO+G93Z0zgevf5c4A3CCHOmdUeRogQIUKECBGWNEzTZNu2bZx77rm8/OUvZ3x8HID9+/cjhOCDH/ygt+3w8DDxeJx3v/vdAOzcuZMrr7ySbdu2cfbZZ3PdddcBcPvtt9PR0cG2bdu8n5///Oc1n3vxxRezbds21q1bR19fn7fd/v37Z7TfO3bs4Ec/+tHcT0ATzJcn61rgK1JKCdwrhOgUQqwE1gO7pZR7AYQQN7jbPjFPnxshQoQIESJEWGSk02l27NgBwJvf/Gauv/56PvCBDwCwceNGbrnlFj7ykY8A8K1vfYstW7Z4r33Pe97De9/7Xq699loAHn30Ue+5yy67jFtuuaXp5/7mN78B4Etf+hLbt2/nU5/61Kz2e8eOHWzfvp3f/u3fntXrZoqZKlkSuFUI8YAQ4rqQ51cDhwL/H3Yfa/Z4A4QQ1wkhtgshtg8NDc1wtyJEiBAhQoTjx/7hPPuH84u9G88oPPe5z+XIkSPe/+l0mrPPPtvr5HLjjTfy2te+1nv+6NGjrFmzxvv/vPPOm9Pn79mzh2uuuYYLL7yQyy67jKeeegpQ5O7cc89l69atXH755VQqFT70oQ9x4403sm3bNm688cY5fW4YZqpkXSql7BdCLAN+JoR4Skr5q8DzYUFKOcXjjQ9K+VngswAXXXRR6DYRIkSIECHCfOKDNz+GlPC1P7x4sXflGQHbtrntttt4+9vfXvP461//em6
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f250 turbo diesel Avg: 11.85\n",
"2012 Mustang V8 Avg: 14.84\n",
"2022 Honda Grom Avg: 35.66\n",
"2011 Chevrolet Suburban Avg: 11.76\n",
"2014 Toyota Corolla Avg: 21.13\n",
"2004 Mustang V6 Avg: 16.28\n"
]
}
],
"source": [
"X = pd.read_csv('data/X_straight.csv')\n",
"y = pd.read_csv('data/y.csv').mpg\n",
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"\n",
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"v6_s197_05 = {'horsepower':216,\n",
" 'weight':3300,\n",
" 'displacement':245,\n",
" 'cylinders':6}\n",
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"\n",
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"mustang = {'horsepower':400,\n",
" 'weight':3600,\n",
" 'displacement':302,\n",
" 'cylinders':8}\n",
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"\n",
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"corolla = {'horsepower':140,\n",
" 'weight':2800,\n",
" 'displacement':110,\n",
" 'cylinders':4}\n",
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"\n",
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"truck = {'horsepower':500,\n",
" 'weight':6500,\n",
" 'displacement':359,\n",
" 'cylinders':6}\n",
"\n",
"grom = {'horsepower':12,\n",
" 'weight':400,\n",
" 'displacement':7.6,\n",
" 'cylinders':1}\n",
"\n",
"burb = {'horsepower':320,\n",
" 'weight':6000,\n",
" 'displacement':325,\n",
" 'cylinders':8}\n",
"\n",
"mdf = pd.DataFrame(mustang,index=[0])\n",
"cdf = pd.DataFrame(corolla,index=[0])\n",
"tdf = pd.DataFrame(truck,index=[0])\n",
"gdf = pd.DataFrame(grom,index=[0])\n",
"bdf = pd.DataFrame(burb,index=[0])\n",
"sm5 = pd.DataFrame(v6_s197_05,index=[0])\n",
"\n",
"mustang_predicts = []\n",
"corolla_predicts = []\n",
"truck_predicts = []\n",
"grom_predicts = []\n",
"burb_predicts = []\n",
"v6_s197_05_predicts = []\n",
"\n",
"r2_test_list = []\n",
"r2_train_list = []\n",
"rmse_test_list = []\n",
"rmse_train_list = []\n",
"\n",
"for i in range(201):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
"\n",
" pipe = Pipeline([\n",
" # ('minmax', MinMaxScaler()),\n",
" # ('ss', StandardScaler()),\n",
" ('qt', QuantileTransformer(n_quantiles=297)),\n",
" # ('rob', RobustScaler()),\n",
" \n",
" ('linreg', LinearRegression()),\n",
" # ('lasso', Lasso()),\n",
" # ('lassocv', LassoCV()),\n",
" # ('ridge', Ridge()),\n",
" # ('ridgeCV', RidgeCV),\n",
" # ('lsvr', LinearSVR())\n",
" ])\n",
"\n",
" model = pipe.fit(X_train,y_train)\n",
" test_predict = model.predict(X_test)\n",
" train_predict = model.predict(X_train)\n",
"\n",
" r2_test = r2_score(y_test, test_predict)\n",
" r2_train = r2_score(y_train, train_predict)\n",
" rmse_test = mean_squared_error(y_test, test_predict ,squared=False)\n",
" rmse_train = mean_squared_error(y_train, train_predict ,squared=False)\n",
"\n",
" r2_test_list.append(r2_test)\n",
" r2_train_list.append(r2_train)\n",
" rmse_test_list.append(rmse_test)\n",
" rmse_train_list.append(rmse_train)\n",
" truck_predicts.append(model.predict(tdf)[0])\n",
" mustang_predicts.append(model.predict(mdf)[0])\n",
" grom_predicts.append(model.predict(gdf)[0])\n",
" burb_predicts.append(model.predict(bdf)[0])\n",
" corolla_predicts.append(model.predict(cdf)[0])\n",
" v6_s197_05_predicts.append(model.predict(sm5)[0])\n",
"\n",
"plt.subplots(figsize=(10,6))\n",
"plt.title('R-squared over 200 iterations')\n",
"plt.plot(r2_test_list,label='R2 Test')\n",
"plt.plot(r2_train_list,label='R2 Train')\n",
"plt.legend()\n",
"plt.show();\n",
"\n",
"avg = np.mean\n",
"print(f'''| R2 Test | R2 Train | RMSE Test | RMSE Train |\n",
"|-----------|----------|-------------|------------|\n",
"| Min: {min(r2_test_list):.2f} | Min:{min(r2_train_list):.2f} | Min: {min(rmse_test_list):.2f} | Min:{min(rmse_train_list):.2f} |\n",
"| Avg: {avg(r2_test_list):.2f} | Avg:{avg(r2_train_list):.2f} | Avg: {avg(rmse_test_list):.2f} | Avg:{avg(rmse_train_list):.2f} |\n",
"| Max: {max(r2_test_list):.2f} | Max:{max(r2_train_list):.2f} | Max: {max(rmse_test_list):.2f} | Max:{max(rmse_train_list):.2f} |\n",
"''')\n",
"plt.subplots(figsize=(10,5))\n",
"plt.title('RMSE over 200 iterations')\n",
"plt.plot(rmse_test_list,label='RMSE Test')\n",
"plt.plot(rmse_train_list,label='RMSE Train')\n",
"plt.legend()\n",
"plt.show();\n",
"\n",
"print(f'f250 turbo diesel Avg: {avg(truck_predicts):.2f}')\n",
"print(f'2012 Mustang V8 Avg: {avg(mustang_predicts):.2f}')\n",
"print(f'2022 Honda Grom Avg: {avg(grom_predicts):.2f}')\n",
"print(f'2011 Chevrolet Suburban Avg: {avg(burb_predicts):.2f}')\n",
"print(f'2014 Toyota Corolla Avg: {avg(corolla_predicts):.2f}')\n",
"print(f'2004 Mustang V6 Avg: {avg(v6_s197_05_predicts):.2f}')"
]
},
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"Predictions are low but relative to each other make sense. For instance they all rank as they should, first the trucks, then the Mustangs, then the Corolla ahead by a decent amount, and then the Grom way out ahead.\n",
"\n",
"There is no real signal of technology here, I would like to get some more data into the future to use model_year as a feature, it gave a nice boost to the scores but when given a 2000s vehicle after being trained on 70s data it predicted MPG around 600 or so.\n",
"\n",
"Each of these predictions should be scaled up by about 30-40% with the exception of the Grom which should be around like 120MPG at least. But the grom is so far off from anything in the training set that I can't complain too much"
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]
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"source": []
2022-07-21 16:31:53 -04:00
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