commit 96b074e7a3b45d45382c39859adfcd29499f372a Author: Adam <24621027+WhiteDopeOnPunk@users.noreply.github.com> Date: Thu Jul 21 16:31:53 2022 -0400 push push diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f870c03 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.ipynb_checkpoints +*/.ipynb_checkpoints diff --git a/clean.ipynb b/clean.ipynb new file mode 100644 index 0000000..b1aeae8 --- /dev/null +++ b/clean.ipynb @@ -0,0 +1,807 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9151a000-1923-408b-bd86-16008dc95f97", + "metadata": {}, + "source": [ + "[readme](readme.md)" + ] + }, + { + "cell_type": "markdown", + "id": "cecbac86-abb3-4f6b-a101-2d9324d96274", + "metadata": {}, + "source": [ + "# Cleaning" + ] + }, + { + "cell_type": "markdown", + "id": "b67cb510-2df0-4ce4-a033-473710fdc749", + "metadata": {}, + "source": [ + "Load file and set column names" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv('data/auto-mpg.data',header=None,delim_whitespace=True)\n", + "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n", + " 'acceleration','model_year','origin','car_name']" + ] + }, + { + "cell_type": "markdown", + "id": "fdcec7e3-c65e-4d66-9a10-b500fb940234", + "metadata": {}, + "source": [ + "Attribute Information:\n", + "\n", + " 1. mpg: continuous\n", + " 2. cylinders: multi-valued discrete\n", + " 3. displacement: continuous\n", + " 4. horsepower: continuous\n", + " 5. weight: continuous\n", + " 6. acceleration: continuous\n", + " 7. model year: multi-valued discrete\n", + " 8. origin: multi-valued discrete\n", + " 9. car name: string (unique for each instance)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 398 entries, 0 to 397\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 mpg 398 non-null float64\n", + " 1 cylinders 398 non-null int64 \n", + " 2 displacement 398 non-null float64\n", + " 3 horsepower 398 non-null object \n", + " 4 weight 398 non-null float64\n", + " 5 acceleration 398 non-null float64\n", + " 6 model_year 398 non-null int64 \n", + " 7 origin 398 non-null int64 \n", + " 8 car_name 398 non-null object \n", + "dtypes: float64(4), int64(3), object(2)\n", + "memory usage: 28.1+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "id": "6a4028ed-eda3-4c50-aed0-d9503d41a8e1", + "metadata": {}, + "source": [ + "Why is horsepower not a number?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['130.0', '165.0', '150.0', '140.0', '198.0', '220.0', '215.0',\n", + " '225.0', '190.0', '170.0', '160.0', '95.00', '97.00', '85.00',\n", + " '88.00', '46.00', '87.00', '90.00', '113.0', '200.0', '210.0',\n", + " '193.0', '?', '100.0', '105.0', '175.0', '153.0', '180.0', '110.0',\n", + " '72.00', '86.00', '70.00', '76.00', '65.00', '69.00', '60.00',\n", + " '80.00', '54.00', '208.0', '155.0', '112.0', '92.00', '145.0',\n", + " '137.0', '158.0', '167.0', '94.00', '107.0', '230.0', '49.00',\n", + " '75.00', '91.00', '122.0', '67.00', '83.00', '78.00', '52.00',\n", + " '61.00', '93.00', '148.0', '129.0', '96.00', '71.00', '98.00',\n", + " '115.0', '53.00', '81.00', '79.00', '120.0', '152.0', '102.0',\n", + " '108.0', '68.00', '58.00', '149.0', '89.00', '63.00', '48.00',\n", + " '66.00', '139.0', '103.0', '125.0', '133.0', '138.0', '135.0',\n", + " '142.0', '77.00', '62.00', '132.0', '84.00', '64.00', '74.00',\n", + " '116.0', '82.00'], dtype=object)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.horsepower.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_name
3225.0498.0?2046.019.0711ford pinto
12621.06200.0?2875.017.0741ford maverick
33040.9485.0?1835.017.3802renault lecar deluxe
33623.64140.0?2905.014.3801ford mustang cobra
35434.54100.0?2320.015.8812renault 18i
37423.04151.0?3035.020.5821amc concord dl
\n", + "
" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "32 25.0 4 98.0 ? 2046.0 19.0 \n", + "126 21.0 6 200.0 ? 2875.0 17.0 \n", + "330 40.9 4 85.0 ? 1835.0 17.3 \n", + "336 23.6 4 140.0 ? 2905.0 14.3 \n", + "354 34.5 4 100.0 ? 2320.0 15.8 \n", + "374 23.0 4 151.0 ? 3035.0 20.5 \n", + "\n", + " model_year origin car_name \n", + "32 71 1 ford pinto \n", + "126 74 1 ford maverick \n", + "330 80 2 renault lecar deluxe \n", + "336 80 1 ford mustang cobra \n", + "354 81 2 renault 18i \n", + "374 82 1 amc concord dl " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.horsepower == '?']" + ] + }, + { + "cell_type": "markdown", + "id": "498d069d-b95e-43d6-bd3d-4b707fdd9635", + "metadata": {}, + "source": [ + "I'll fill in what I can find online" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# 1971 pinto kent I4\n", + "df.at[32,'horsepower'] = '75.0'\n", + "# 1974 maverick 200 I6\n", + "df.at[126,'horsepower'] = '85.0'\n", + "# 1980 renault lecar deluxe 85ci I4\n", + "df.at[330,'horsepower'] = '53.5'\n", + "# 1980 ford mustang cobra\n", + "# they seem confused between 2 different models\n", + "# 1981 renault 18i\n", + "df.at[354,'horsepower'] = '81.5'\n", + "#1982 AMC concord dl 151\n", + "df.at[374,'horsepower'] = '90'" + ] + }, + { + "cell_type": "markdown", + "id": "68d959c5-9628-437f-8f3f-0b4c7002b1f0", + "metadata": {}, + "source": [ + "We'll ignore the mustang because it's too far off from realistic, it looks like they got confused between two different models.\n", + "\n", + "Anyway, drop all '?' horsepower" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df.drop(df[df.horsepower == '?'].index,inplace=True)\n", + "df['horsepower'] = df.horsepower.astype(float)\n", + "df.reset_index(inplace=True,drop=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b2afc76d-c428-4b81-9882-5ea19ecd04bb", + "metadata": {}, + "source": [ + "And set to floats, like the rest" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 397 entries, 0 to 396\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 mpg 397 non-null float64\n", + " 1 cylinders 397 non-null int64 \n", + " 2 displacement 397 non-null float64\n", + " 3 horsepower 397 non-null float64\n", + " 4 weight 397 non-null float64\n", + " 5 acceleration 397 non-null float64\n", + " 6 model_year 397 non-null int64 \n", + " 7 origin 397 non-null int64 \n", + " 8 car_name 397 non-null object \n", + "dtypes: float64(5), int64(3), object(1)\n", + "memory usage: 28.0+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "id": "097c4cec-eb77-46f6-8eef-89eb7c47b425", + "metadata": {}, + "source": [ + "Looks good" + ] + }, + { + "cell_type": "markdown", + "id": "151e5f1b-6409-4972-9c79-a26d132eedf5", + "metadata": {}, + "source": [ + "### Min/Max to check range" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpg\n", + "Min: 9.0 \n", + "Max: 46.6\n", + "\n", + "cylinders\n", + "Min: 3 \n", + "Max: 8\n", + "\n", + "displacement\n", + "Min: 68.0 \n", + "Max: 455.0\n", + "\n", + "horsepower\n", + "Min: 46.0 \n", + "Max: 230.0\n", + "\n", + "weight\n", + "Min: 1613.0 \n", + "Max: 5140.0\n", + "\n", + "acceleration\n", + "Min: 8.0 \n", + "Max: 24.8\n", + "\n", + "model_year\n", + "Min: 70 \n", + "Max: 82\n", + "\n", + "origin\n", + "Min: 1 \n", + "Max: 3\n", + "\n" + ] + } + ], + "source": [ + "for col in df.columns[:-1]:\n", + " print(f'''{col}\n", + "Min: {df[col].min()} \n", + "Max: {df[col].max()}\n", + "''')" + ] + }, + { + "cell_type": "markdown", + "id": "59641984-e266-4eaa-a90d-af266cb95936", + "metadata": {}, + "source": [ + "All of this makes sense" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['datsun pl510', 'amc gremlin', 'chevrolet chevelle malibu',\n", + " 'chevrolet impala', 'ford galaxie 500', 'plymouth fury iii',\n", + " 'pontiac catalina', 'amc matador', 'amc hornet', 'ford maverick',\n", + " 'plymouth duster', 'chevrolet vega', 'ford pinto',\n", + " 'toyota corolla 1200', 'ford gran torino', 'ford gran torino (sw)',\n", + " 'amc matador (sw)', 'opel manta', 'toyota corona', 'fiat 128',\n", + " 'chevrolet nova', 'ford ltd', 'volkswagen dasher', 'datsun 710',\n", + " 'audi 100ls', 'peugeot 504', 'saab 99le', 'opel 1900',\n", + " 'dodge colt', 'chevrolet chevelle malibu classic',\n", + " 'plymouth valiant', 'honda civic', 'volkswagen rabbit',\n", + " 'toyota corolla', 'toyota mark ii', 'chevrolet caprice classic',\n", + " 'chevrolet chevette', 'honda civic cvcc', 'chevrolet malibu',\n", + " 'chevrolet monte carlo landau', 'buick estate wagon (sw)',\n", + " 'ford country squire (sw)', 'oldsmobile cutlass salon brougham',\n", + " 'vw rabbit', 'chevrolet citation', 'amc concord', 'dodge aspen',\n", + " 'datsun 210', 'subaru dl', 'buick skylark', 'plymouth reliant',\n", + " 'subaru', 'mazda 626', 'buick century', 'pontiac phoenix',\n", + " 'honda accord'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.car_name.duplicated()].car_name.unique()" + ] + }, + { + "cell_type": "markdown", + "id": "81b8d5a5-d323-4a70-b951-b2fe4fb1e35f", + "metadata": {}, + "source": [ + "There are some duplicate car names, honestly I wish there were more. If I had a bunch of data with lots of duplicate car names it'd actually be easier to predict MPG I imagine, I'll say more on this later but there are some big factors that aren't represented here." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigin
count397.000000397.000000397.000000397.000000397.000000397.000000397.000000397.000000
mean23.5143585.458438193.560453104.1234262970.58942115.57128576.0000001.574307
std7.8258461.701577104.36679638.396800847.9039552.7604313.6968460.802549
min9.0000003.00000068.00000046.0000001613.0000008.00000070.0000001.000000
25%17.5000004.000000104.00000075.0000002223.00000013.80000073.0000001.000000
50%23.0000004.000000151.00000092.0000002800.00000015.50000076.0000001.000000
75%29.0000008.000000262.000000125.0000003609.00000017.20000079.0000002.000000
max46.6000008.000000455.000000230.0000005140.00000024.80000082.0000003.000000
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"execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "90fe9344-fe47-4503-be59-74d9d38cf1d3", + "metadata": {}, + "source": [ + "Everything looks proportional" + ] + }, + { + "cell_type": "markdown", + "id": "042416c1-0e56-4269-96c8-6926392e11e7", + "metadata": {}, + "source": [ + "### Save" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df.to_csv('data/clean.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": 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"audi 100ls" +23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" +22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" +25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" +33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" +28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" +25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" +25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" +26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" +27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" +17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" +16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" +15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" +14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" +22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" +22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" +24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" +22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" +29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" +24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" +29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" +33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" +20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" +18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" +18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" +17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" +29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" +32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" +28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" +26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" +20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" +13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" +19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" +19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" +16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" +16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" +13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" +13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" +13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" +31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" +30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" +36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" +25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" +33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" +17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" +17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" +15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" +15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" +17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" +20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" +19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" +18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" +16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" +15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" +15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" +16.0 8 351.0 149.0 4335. 14.5 77 1 "ford 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78 1 "dodge diplomat" +20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" +19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" +20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" +20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" +25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" +20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" +19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" +20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" +20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" +18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" +18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" +19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" +17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" +18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" +17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" +30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" +27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" +27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" +30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" +21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" +23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" +23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" +23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" +20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" +17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" +21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" +16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" +31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" +29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" +21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" +19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" +22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" +20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" +20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" +17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" +17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" +16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" +18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" +16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" +15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" +19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" +18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" +31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" +34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" +35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" +27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" +25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" +23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" +27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" +23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" +34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" +34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" +31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" +37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" +28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" +28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" +26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" +33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" +41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" +38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" +32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" +37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" +28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" +26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" +24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" +19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" +34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" +29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" +31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" +37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" +32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" +46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" +27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" +40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" +44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" +43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" +36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" +30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" +44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" +40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" +33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" +29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" +32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" +23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" +35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" +23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" +32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" +27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" +26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" +25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" +23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" +30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" +39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" +39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" +35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" +32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" +37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" +37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" +34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" +34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" +34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" +29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" +33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" +34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" +33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" +32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" +32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" +31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" +28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" +30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" +25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" +24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" +22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" +26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" +20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" +17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" +28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" +27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" +34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" +31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" +29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" +27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" +24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" +23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl" +36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" +37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" +31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" +38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" +36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" +36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" +36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" +34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" +38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" +32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" +38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" +25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" +38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" +26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" +22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" +32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" +36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" +27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" +27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" +44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" +32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" +28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" +31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" diff --git a/data/auto-mpg.names b/data/auto-mpg.names new file mode 100644 index 0000000..b47a865 --- /dev/null +++ b/data/auto-mpg.names @@ -0,0 +1,45 @@ +1. Title: Auto-Mpg Data + +2. Sources: + (a) Origin: This dataset was taken from the StatLib library which is + maintained at Carnegie Mellon University. The dataset was + used in the 1983 American Statistical Association Exposition. + (c) Date: July 7, 1993 + +3. Past Usage: + - See 2b (above) + - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. + In Proceedings on the Tenth International Conference of Machine + Learning, 236-243, University of Massachusetts, Amherst. Morgan + Kaufmann. + +4. Relevant Information: + + This dataset is a slightly modified version of the dataset provided in + the StatLib library. In line with the use by Ross Quinlan (1993) in + predicting the attribute "mpg", 8 of the original instances were removed + because they had unknown values for the "mpg" attribute. The original + dataset is available in the file "auto-mpg.data-original". + + "The data concerns city-cycle fuel consumption in miles per gallon, + to be predicted in terms of 3 multivalued discrete and 5 continuous + attributes." (Quinlan, 1993) + +5. Number of Instances: 398 + +6. Number of Attributes: 9 including the class attribute + +7. Attribute Information: + + 1. mpg: continuous + 2. cylinders: multi-valued discrete + 3. displacement: continuous + 4. horsepower: continuous + 5. weight: continuous + 6. acceleration: continuous + 7. model year: multi-valued discrete + 8. origin: multi-valued discrete + 9. car name: string (unique for each instance) + +8. Missing Attribute Values: horsepower has 6 missing values + diff --git a/data/clean.csv b/data/clean.csv new file mode 100644 index 0000000..b4cab35 --- /dev/null +++ b/data/clean.csv @@ -0,0 +1,398 @@ +mpg,cylinders,displacement,horsepower,weight,acceleration,model_year,origin,car_name +18.0,8,307.0,130.0,3504.0,12.0,70,1,chevrolet chevelle malibu +15.0,8,350.0,165.0,3693.0,11.5,70,1,buick skylark 320 +18.0,8,318.0,150.0,3436.0,11.0,70,1,plymouth satellite +16.0,8,304.0,150.0,3433.0,12.0,70,1,amc rebel sst +17.0,8,302.0,140.0,3449.0,10.5,70,1,ford torino +15.0,8,429.0,198.0,4341.0,10.0,70,1,ford galaxie 500 +14.0,8,454.0,220.0,4354.0,9.0,70,1,chevrolet impala +14.0,8,440.0,215.0,4312.0,8.5,70,1,plymouth fury iii +14.0,8,455.0,225.0,4425.0,10.0,70,1,pontiac catalina +15.0,8,390.0,190.0,3850.0,8.5,70,1,amc ambassador dpl +15.0,8,383.0,170.0,3563.0,10.0,70,1,dodge challenger se +14.0,8,340.0,160.0,3609.0,8.0,70,1,plymouth 'cuda 340 +15.0,8,400.0,150.0,3761.0,9.5,70,1,chevrolet monte 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{ + "cell_type": "code", + "execution_count": 1, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from os.path import exists\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import display, Markdown\n", + "\n", + "sns.set_theme(style='darkgrid')\n", + "\n", + "df = pd.read_csv('data/clean.csv')\n", + "y = df.mpg\n", + "\n", + "def show_plots(filenames):\n", + " for j in range(0,len(filenames),2):\n", + " if (len(filenames)-j)>1:\n", + " display(Markdown(f'![]({filenames[j]})![]({filenames[j+1]})'))\n", + " else:\n", + " display(Markdown(f'![]({filenames[j]})'))\n", + "\n", + "def make_plots(df, y):\n", + " filenames = []\n", + " \n", + " for col in df.columns:\n", + " filename = 'img/%s_joint.png' % col\n", + " filenames.append(filename)\n", + " if not exists(filename):\n", + " sns.jointplot(x=df[col],y=y,kind='reg',\n", + " joint_kws={'scatter_kws':dict(alpha=0.3)})\n", + " plt.suptitle(f'{col} vs mpg')\n", + " plt.subplots_adjust(top=.93)\n", + " plt.savefig(filename,facecolor='white',transparent=False)\n", + " plt.close()\n", + " \n", + " show_plots(filenames)" + ] + }, + { + "cell_type": "markdown", + "id": "7af7dcdd-9618-4e81-88c8-d2c2cde0fdc2", + "metadata": {}, + "source": [ + "So I'm only interested in a few things:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "![](img/cylinders_joint.png)![](img/displacement_joint.png)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "![](img/horsepower_joint.png)![](img/weight_joint.png)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "make_plots(df[['cylinders','displacement',\n", + " 'horsepower','weight',]],y)" + ] + }, + { + "cell_type": "markdown", + "id": "b0f65dd4-16b6-4222-8758-71e2ecac473e", + "metadata": {}, + "source": [ + "As the number of cylinders, displacement, horsepower, or weight increase, MPG goes down." + ] + }, + { + "cell_type": "markdown", + "id": "61b1b79e-46c2-4e7b-b565-84d1e2045777", + "metadata": {}, + "source": [ + "I want to know more:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "new_features = pd.DataFrame()\n", + "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" + ] + }, + { + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "![](img/efficiency_joint.png)![](img/load_joint.png)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "![](img/bore_size_joint.png)![](img/grunt_joint.png)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "make_plots(new_features,y)\n", + "del new_features" + ] + }, + { + "cell_type": "markdown", + "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", + "* 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", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8710cba8-6b7e-4219-98b9-b7d5a1b4f4b9", + "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" + }, + "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" + ] + } + ], + "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": "d39b59e4-e596-4fc9-b886-1e6d314f597e", + "metadata": {}, + "source": [ + "### What's all that on the edges?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
7119.0370.097.02330.013.5723mazda rx2 coupe1.3857140.03004323.33333316.838488
11118.0370.090.02124.013.5733maxda rx31.2857140.03295723.33333318.148148
24321.5380.0110.02720.013.5773mazda rx-41.3750000.02941226.66666719.393939
33423.7370.0100.02420.012.5803mazda rx-7 gs1.4285710.02892623.33333316.333333
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "71 19.0 3 70.0 97.0 2330.0 13.5 \n", + "111 18.0 3 70.0 90.0 2124.0 13.5 \n", + "243 21.5 3 80.0 110.0 2720.0 13.5 \n", + "334 23.7 3 70.0 100.0 2420.0 12.5 \n", + "\n", + " model_year origin car_name efficiency load bore_size \\\n", + "71 72 3 mazda rx2 coupe 1.385714 0.030043 23.333333 \n", + "111 73 3 maxda rx3 1.285714 0.032957 23.333333 \n", + "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 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged[merged.efficiency>1]" + ] + }, + { + "cell_type": "markdown", + "id": "d1f1bf61-6c9b-498e-a5de-6fbe6bb719d3", + "metadata": {}, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "20eaa490-f70b-408e-a9fa-c4ba05c8a1ac", + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
27420.35131.0103.02830.015.9782audi 50000.7862600.04629026.233.322330
29725.45183.077.03530.020.1792mercedes benz 300d0.4207650.05184136.686.984416
32736.45121.067.02950.019.9802audi 5000s (diesel)0.5537190.04101724.243.704478
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" + ], + "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", + "327 36.4 5 121.0 67.0 2950.0 19.9 \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", + "\n", + " grunt \n", + "274 33.322330 \n", + "297 86.984416 \n", + "327 43.704478 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged[merged.cylinders==5]" + ] + }, + { + "cell_type": "markdown", + "id": "33fb0fbd-595d-4129-bfe0-dd4077553f9a", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
38638.06262.085.03015.017.0821oldsmobile cutlass ciera (diesel)0.3244270.08689943.666667134.596078
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "386 38.0 6 262.0 85.0 3015.0 17.0 \n", + "\n", + " 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 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.iloc[np.where((merged.mpg>35) & (merged.displacement > 250))]" + ] + }, + { + "cell_type": "markdown", + "id": "0456df70-dc3d-4879-95cb-26e268fea9aa", + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
29823.08350.0125.03900.017.4791cadillac eldorado0.3571430.08974443.75122.500000
36326.68350.0105.03725.019.0811oldsmobile cutlass ls0.3000000.09396043.75145.833333
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "298 23.0 8 350.0 125.0 3900.0 17.4 \n", + "363 26.6 8 350.0 105.0 3725.0 19.0 \n", + "\n", + " model_year origin car_name efficiency load \\\n", + "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 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.iloc[np.where((merged.mpg>20) & (merged.displacement > 340))]" + ] + }, + { + "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": 12, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
1314.08455.0225.03086.010.0701buick estate wagon (sw)0.4945050.1474456.875115.013889
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "13 14.0 8 455.0 225.0 3086.0 10.0 \n", + "\n", + " 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 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged[merged.load>0.14]" + ] + }, + { + "cell_type": "markdown", + "id": "4415be3a-f8fb-47f1-b39d-2c60a3495a1d", + "metadata": {}, + "source": [ + "Big car, big engine, terrible MPG.. That weight is way off" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "merged.at[13,'weight'] = 5000\n", + "merged['load'] = merged.displacement / merged.weight" + ] + }, + { + "cell_type": "markdown", + "id": "15d5a2c5-cb01-4a54-8ce4-375018ebc79a", + "metadata": {}, + "source": [ + "What vehicles have the lowest MPG?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
289.08304.0193.04732.018.5701hi 1200d0.6348680.06424338.00059.854922
2610.08307.0200.04376.015.0701chevy c200.6514660.07015538.37558.905625
2510.08360.0215.04615.014.0701ford f2500.5972220.07800745.00075.348837
2711.08318.0210.04382.013.5701dodge d2000.6603770.07257039.75060.192857
10311.08400.0150.04997.014.0731chevrolet impala0.3750000.08004850.000133.333333
6711.08429.0208.04633.011.0721mercury marquis0.4848480.09259753.625110.601562
12411.08350.0180.03664.011.0731oldsmobile omega0.5142860.09552443.75085.069444
4212.08383.0180.04955.011.5711dodge monaco (sw)0.4699740.07729647.875101.867361
9512.08455.0225.04951.011.0731buick electra 225 custom0.4945050.09190156.875115.013889
9012.08429.0198.04952.011.5731mercury marquis brougham0.4615380.08663253.625116.187500
6912.08350.0160.04456.013.5721oldsmobile delta 88 royale0.4571430.07854643.75095.703125
10412.08400.0167.04906.012.5731ford country0.4175000.08153350.000119.760479
10612.08350.0180.04499.012.5731oldsmobile vista cruiser0.5142860.07779543.75085.069444
8713.08350.0145.03988.013.0731chevrolet malibu0.4142860.08776343.750105.603448
7313.08307.0130.04098.014.0721chevrolet chevelle concours (sw)0.4234530.07491538.37590.624038
7413.08302.0140.04294.016.0721ford gran torino (sw)0.4635760.07033137.75081.432143
6213.08350.0165.04274.012.0721chevrolet impala0.4714290.08189143.75092.803030
4313.08400.0170.04746.012.0711ford country squire (sw)0.4250000.08428250.000117.647059
9613.08360.0175.03821.011.0731amc ambassador brougham0.4861110.09421645.00092.571429
9413.08440.0215.04735.011.0731chrysler new yorker brougham0.4886360.09292555.000112.558140
9213.08351.0158.04363.013.0731ford ltd0.4501420.08044943.87597.469146
8513.08350.0175.04100.013.0731buick century 3500.5000000.08536643.75087.500000
13713.08350.0150.04699.014.5741buick century luxus (sw)0.4285710.07448443.750102.083333
4413.08400.0175.05140.012.0711pontiac safari (sw)0.4375000.07782150.000114.285714
21513.08318.0150.03755.014.0761dodge d1000.4716980.08468739.75084.270000
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "28 9.0 8 304.0 193.0 4732.0 18.5 \n", + "26 10.0 8 307.0 200.0 4376.0 15.0 \n", + "25 10.0 8 360.0 215.0 4615.0 14.0 \n", + "27 11.0 8 318.0 210.0 4382.0 13.5 \n", + "103 11.0 8 400.0 150.0 4997.0 14.0 \n", + "67 11.0 8 429.0 208.0 4633.0 11.0 \n", + "124 11.0 8 350.0 180.0 3664.0 11.0 \n", + "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", + "\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 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.sort_values('mpg').head(25)" + ] + }, + { + "cell_type": "markdown", + "id": "146d6761-455a-407f-b627-24c13586a88f", + "metadata": {}, + "source": [ + "What vehicles have the Highest MPG?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "38935e91-3877-47a3-96d6-cd54e2704bdb", + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_nameefficiencyloadbore_sizegrunt
32246.6486.065.02110.017.9803mazda glc0.7558140.04075821.50000028.446154
32944.6491.067.01850.013.8803honda civic 1500 gl0.7362640.04918922.75000030.899254
32544.3490.048.02085.021.7802vw rabbit c (diesel)0.5333330.04316522.50000042.187500
39344.0497.052.02130.024.6822vw pickup0.5360820.04554024.25000045.235577
32643.4490.048.02335.023.7802vw dasher (diesel)0.5333330.03854422.50000042.187500
24443.1490.048.01985.021.5782volkswagen rabbit custom diesel0.5333330.04534022.50000042.187500
30941.5498.076.02144.014.7802vw rabbit0.7755100.04570924.50000031.592105
33040.9485.053.51835.017.3802renault lecar deluxe0.6294120.04632221.25000033.761682
32440.8485.065.02110.019.2803datsun 2100.7647060.04028421.25000027.788462
24739.4485.070.02070.018.6783datsun b210 gx0.8235290.04106321.25000025.803571
34239.1479.058.01755.016.9813toyota starlet0.7341770.04501419.75000026.900862
34339.0486.064.01875.016.4811plymouth champ0.7441860.04586721.50000028.890625
31038.1489.060.01968.018.8803toyota corolla tercel0.6741570.04522422.25000033.004167
38438.0491.067.01995.016.2823datsun 310 gx0.7362640.04561422.75000030.899254
38238.0491.067.01965.015.0823honda civic0.7362640.04631022.75000030.899254
38638.06262.085.03015.017.0821oldsmobile cutlass ciera (diesel)0.3244270.08689943.666667134.596078
37738.04105.063.02125.014.7821plymouth horizon miser0.6000000.04941226.25000043.750000
34737.7489.062.02050.017.3813toyota tercel0.6966290.04341522.25000031.939516
30437.3491.069.02130.014.7792fiat strada custom0.7582420.04272322.75000030.003623
31237.2486.065.02019.016.4803datsun 3100.7558140.04259521.50000028.446154
37537.0491.068.02025.018.2823mazda glc custom l0.7472530.04493822.75000030.444853
34637.0485.065.01975.019.4813datsun 210 mpg0.7647060.04303821.25000027.788462
32037.04119.092.02434.015.0803datsun 510 hatchback0.7731090.04889129.75000038.480978
32736.45121.067.02950.019.9802audi 5000s (diesel)0.5537190.04101724.20000043.704478
24836.1491.060.01800.016.4783honda civic cvcc0.6593410.05055622.75000034.504167
\n", + "
" + ], + "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 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.sort_values('mpg',ascending=False).head(25)" + ] + }, + { + "cell_type": "markdown", + "id": "27e89d6b-7603-403c-8235-e9bad49040b3", + "metadata": {}, + "source": [ + "Pick a few to toss into the model and get some numbers out" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n", + " 'acceleration', 'model_year', 'origin', 'car_name', 'efficiency',\n", + " 'load', 'bore_size', 'grunt'],\n", + " dtype='object')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "X = merged[[\\\n", + " 'horsepower', # overall power\n", + " 'bore_size', # \"torque curve\"\n", + " 'grunt',\n", + " 'load', # load\n", + " ]]\n", + "\n", + "X.to_csv('data/X.csv',index=False)\n", + "y.to_csv('data/y.csv',index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "4802d1fd-079c-4053-88f2-b5dca7cf8dae", + "metadata": {}, + "source": [ + "[Modeling](model.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/img/bore_size_joint.png b/img/bore_size_joint.png new file mode 100644 index 0000000..82b7924 Binary files /dev/null and b/img/bore_size_joint.png 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b/img/grunt_joint.png differ diff --git a/img/gruntiness_joint.png b/img/gruntiness_joint.png new file mode 100644 index 0000000..f8a54ec Binary files /dev/null and b/img/gruntiness_joint.png differ diff --git a/img/horsepower_joint.png b/img/horsepower_joint.png new file mode 100644 index 0000000..46dd6af Binary files /dev/null and b/img/horsepower_joint.png differ diff --git a/img/hp_per_ci_joint.png b/img/hp_per_ci_joint.png new file mode 100644 index 0000000..b2a12b3 Binary files /dev/null and b/img/hp_per_ci_joint.png differ diff --git a/img/load_joint.png b/img/load_joint.png new file mode 100644 index 0000000..6bd79fb Binary files /dev/null and b/img/load_joint.png differ diff --git a/img/weight_joint.png b/img/weight_joint.png new file mode 100644 index 0000000..102b9ef Binary files /dev/null and b/img/weight_joint.png differ diff --git a/model.ipynb b/model.ipynb new file mode 100644 index 0000000..2c4f823 --- /dev/null +++ b/model.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "717f94d3-edfa-4122-9902-212a3456bb8c", + "metadata": {}, + "source": [ + "[EDA](eda.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c47122c5-bdcd-4b6b-8d22-8958ad910eca", + "metadata": { + "execution": { + "iopub.execute_input": "2022-07-21T20:30:04.392164Z", + "iopub.status.busy": "2022-07-21T20:30:04.391759Z", + "iopub.status.idle": "2022-07-21T20:30:06.057099Z", + "shell.execute_reply": "2022-07-21T20:30:06.056318Z", + "shell.execute_reply.started": "2022-07-21T20:30:04.392085Z" + }, + "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 (StandardScaler,\n", + " QuantileTransformer,\n", + " Normalizer,\n", + " MinMaxScaler,\n", + " RobustScaler,\n", + " PowerTransformer)\n", + "\n", + "from sklearn.linear_model import (Lars,\n", + " Ridge,\n", + " Lasso,\n", + " LarsCV,\n", + " LassoCV,\n", + " RidgeCV,\n", + " LassoLars,\n", + " ElasticNet,\n", + " LassoLarsCV,\n", + " LassoLarsIC,\n", + " ElasticNetCV,\n", + " SGDRegressor,\n", + " LinearRegression,\n", + " OrthogonalMatchingPursuit,\n", + " OrthogonalMatchingPursuitCV)\n", + "\n", + "from sklearn.neighbors import KNeighborsRegressor\n", + "from sklearn.svm import LinearSVR\n", + "from sklearn.metrics import mean_squared_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8c3b98e1-9a8f-4d42-8a29-5d030934a4ed", + "metadata": { + "execution": { + "iopub.execute_input": "2022-07-21T20:30:06.059480Z", + "iopub.status.busy": "2022-07-21T20:30:06.059058Z", + "iopub.status.idle": "2022-07-21T20:30:11.086526Z", + "shell.execute_reply": "2022-07-21T20:30:11.085657Z", + "shell.execute_reply.started": "2022-07-21T20:30:06.059453Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + 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\n", 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" + ] + }, + "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: 0.62 | Min:0.72 | Min: 3.24 | Min:3.57 |\n", + "| Avg: 0.73 | Avg:0.75 | Avg: 4.01 | Avg:3.93 |\n", + "| Max: 0.82 | Max:0.78 | Max: 5.04 | Max:4.15 |\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Truck Avg: 16.23\n", + "Mustang Avg: 20.68\n", + "Grom Avg: 36.07\n", + "Burb Avg: 15.86\n", + "Corolla Avg: 21.06\n", + "Steve Avg: 16.37\n" + ] + } + ], + "source": [ + "X = pd.read_csv('data/X.csv')\n", + "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", + "print(f'Truck Avg: {avg(truck_predicts):.2f}')\n", + "print(f'Mustang Avg: {avg(mustang_predicts):.2f}')\n", + "print(f'Grom Avg: {avg(grom_predicts):.2f}')\n", + "print(f'Burb Avg: {avg(burb_predicts):.2f}')\n", + "print(f'Corolla Avg: {avg(corolla_predicts):.2f}')\n", + "print(f'Steve Avg: {avg(v6_s197_05_predicts):.2f}')" + ] + }, + { + "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", + "\n", + "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." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..7b817bf --- /dev/null +++ b/readme.md @@ -0,0 +1,112 @@ +# Analyzing Fuel Economy +Predicting MPG of a vehicle using a linear regression model. Success will be evaluated using r-squared and root mean square error scores over time as well as testing with some unseen, futuristic, and very different data compared to training set. + +If you're curious about what goes into fuel economy, this should provide some insight on how it all works. + +# Contents +1. [Clean](clean.ipynb) - Quick look to find any missing values, data of wrong types. Make sure data is in ranges that make sense +2. [EDA](eda.ipynb) - Investigate outliers and other items of interest. Manufacture some new features +3. [Model](model.ipynb) - Model and make predictions. Introduce some outside data + +# Libs +* pandas +* numpy +* seaborn +* os +* matplotlib +* Ipython +* sklearn + +# The Data +Using https://archive-beta.ics.uci.edu/ml/datasets/auto+mpg +1. Title: Auto-Mpg Data + +2. Sources: + (a) Origin: This dataset was taken from the StatLib library which is + maintained at Carnegie Mellon University. The dataset was + used in the 1983 American Statistical Association Exposition. + (c) Date: July 7, 1993 + +3. Past Usage: + - See 2b (above) + - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. + In Proceedings on the Tenth International Conference of Machine + Learning, 236-243, University of Massachusetts, Amherst. Morgan + Kaufmann. + +4. Relevant Information: + + This dataset is a slightly modified version of the dataset provided in + the StatLib library. In line with the use by Ross Quinlan (1993) in + predicting the attribute "mpg", 8 of the original instances were removed + because they had unknown values for the "mpg" attribute. The original + dataset is available in the file "auto-mpg.data-original". + + "The data concerns city-cycle fuel consumption in miles per gallon, + to be predicted in terms of 3 multivalued discrete and 5 continuous + attributes." (Quinlan, 1993) + +5. Number of Instances: 398 + +6. Number of Attributes: 9 including the class attribute + +7. Attribute Information: + + 1. mpg: continuous + 2. cylinders: multi-valued discrete + 3. displacement: continuous + 4. horsepower: continuous + 5. weight: continuous + 6. acceleration: continuous + 7. model year: multi-valued discrete + 8. origin: multi-valued discrete + 9. car name: string (unique for each instance) + +8. Missing Attribute Values: horsepower has 6 missing values + +# Summary + +## A bit on engines: + +* A most basic description of an engine is that it's an air pump +* Horsepower = (Torque * RPM) / 5252 +* Torque peak is where an engine is operating most efficiently as far as air flow, applied science in action. (Fluid dynamics, resonance) +* Operating above or below the torque peak reduces efficiency and efficiency == fuel economy +* 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 +* 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 + +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 during normal driving. 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 during normal driving. 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. + +# Features +From the original features I chose: +* mpg: target +* number of cylinders +* engine displacement (in cubic inches) +* Horsepower +* Total weight of vehicle + +From these I then calculated: +* Efficiency: HP per cubic inch - as this increases so does MPG +* Load: cubic inches per lb of weight - 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 +* Bore_size: cubic inches per cylinder - best attempt to describe cylinder bore diameter which gives insight on torque curve +* Grunt: bore size divided by efficiency - an attempt to describe the power curve of an engine, or more specifically the presence/absence of low rpm torque output + +All of these features are continuous. + +# Model + +Into the model: +* Horsepower +* Displacement +* Number of cylinders +* Weight + +Which then gets turned into: +* Horsepower +* Bore_size +* Grunt +* Load + +Then sent through: +* Quantile Transformer +* Linear Regression \ No newline at end of file