Created
November 12, 2019 19:47
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Created on Cognitive Class Labs
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import matplotlib.pyplot as plt\n", | |
"import pandas as pd\n", | |
"import pylab as pl\n", | |
"import numpy as np\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 4 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# MOSTRAR TABLA\n", | |
"df = pd.read_csv(\"./FuelConsumption.csv\")\n", | |
"\n", | |
"# TABLA DATASET \n", | |
"df.head()\n", | |
"\n", | |
"cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS', 'FUELCONSUMPTION_CITY', 'FUELCONSUMPTION_COMB_MPG']]\n", | |
"cdf.head(19)\n", | |
"\n", | |
"viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_CITY']]\n", | |
"viz.hist()\n", | |
"plt.show()\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 23, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"msk = np.random.rand(len(df)) < 0.82\n", | |
"train = cdf[msk]\n", | |
"test = cdf[~msk]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 24, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# GRÁFICO VARIABLES DEPENDIENTES E INDEPENDIENTES\n", | |
"plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS, color='red')\n", | |
"plt.xlabel(\"FUELCONSUMPTION_COMB\")\n", | |
"plt.ylabel(\"Emission\")\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 25, | |
"metadata": { | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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lsHfvzLa9eztfAS3qxbzI60fDGB8f98nJybrFEHOYiYngTfrxxwO7+o9/nN0nWmCmFX2U5WyO9omSFg0U96eZdn2UaDH3FSvy+T6Gh4MZSVGKfgdRP2a2293H485pRbOY02SlZM7qGw0n/fGPg/sMZfxVRNNAtDthk7jnnvxypbFsWfr54eGZCgHyl+PMawoT/Y2UgpizdFsfOc7WfuIEnHVW8Rj7VihlEmUNuFdeGd++cWMg87FjMxVCS748iksZSQXIfCTmMElmkbGxYJDOYmgo2URz4kRnZpF58+IVQJJppugzit4/jlbobTvtM4y8yHw09+jafBRGEv1bM9seLji7wcxuKFdMIYrRbRK1rOydSat629ujJqwFC+L7JK03KEonEVLtrF4920Q2NBS0d8LGjcXaRbPJaz76KvAq4C7gLyKbELWRJyVzN2Up49I9tDuM201YR47MHnDXrEl+Ay+68KuMYjRbtsw2dZ04kZxKG9J/x9WrZz9/eLhzJSNqJmkBQ3QD7s9zXa83LV4bbLKSqPWiLGWejKRpid2KLvwqmoU1jqIJ7LJ+R2U4nXvQbZZU4D8RSXHdlE1KoV7KrPNbhQxlDFZZ3zFvRtK0Zxb9HaMrnIeHiykE9+K/S9b1vahTLcqlDKXwPHAC+Kdw/3ngR3n6VrlJKdRHU1Idpw2o3Q5Web5j3toFTRogi842sn7HxYvjzy9e3LvvJIrRtVJo6ialUB9NMBlkDdoLF8bLuHBhvvvn+Y47d7rPn5+tFIaHk2cCvZ5xlT1TSPr+8+dP36MJs0oxTSlKAXgn8Hvh9o68/arcpBTqowqTQdGBI2uwShuky/qOO3cGyeXyzBbiFFcnM64FC2Zev2BBvu9T5HtFyZIx63feuXP2M82kGOqkDPPRdcAu4MPhdidwXZ6+VW5SCvVR9kyhk8Exa3DrVim0O4Bb27x52b9Da2aQlN209TsV/R3bFUIniqGTf7s0hZ31O+eZSYjeUoZSeBAYihwPAw/m6VvlJqVQH2X7FJIGqsWLO3ckd6sU8vTvVjEVlbHb7+Qeb/KaP7/zf7us9N9lyCzKJU0pFElzcUpk/1UF+ok+pOzyiUkLzp57LjmNRdY6g16QtVaijHUFVRC82yUfF+Gqq4q1i4aTpC2iG/A+4ABwE7ADeAxYm6dvlZtmCv1D3iiedjNHN2aNLPL079beXtVMIS1stYoggbTnaabQPCjJ0byUwNl8JfDP8varcpNS6B86cdhmxejnqaSWplRWrYrvv2rVbNl7ZeLK41PICjnt9bqCNWvin7dmTTXPE9l0rBSA14afb4zb0vr2YpNS6B/yhnamDXbtZA2OeWzr7YqhXSHk+V5pM4mTT46X8eSTk++ZFX2UZeNftCj+/KJFxb5bEdoVgxRCvXSjFLaHn3fHbN9I69uLTUqhXsqMPS9iPkp7828nzaxR1aKr9t9l48bqTFxxZN0zbRW21hEMBqWYj4puwMkENZYfAPYAnw7bTyMIaX00/Dw10mczsA94BLgs6xlSCvVRdvRR3nQRZQ6gVQzIRX+XOpRCnt+zjtXponekKYW8qbPfY2avCPf/nZl9xczekNHtJeCt7v7TwAXA5Wb2ZuBaYJe7ryRY+3BteN9VwFrgfOByYJuZqexHQ4krUPPCC+mZNtNIiuLJQ1okz6ZNQQ0Cs+Cz0zrEeSn7d+mEpPoGrfY8kU+9llk0h7whqf/e3Z83s58FLiOIQPrjtA6hQjoSHo6EmxM4qneE7TuAd4X7VwI3u/tL7v4YwYzhotzfRPSUpJq/eWoBx7F1K8yfP7Mtbz3ipFoFrWIyrVoD7QXqFy+O75fUnoek0NoDBzorGRpHVgnSYNI9m1b7xRfne07euhSiv8irFFolPN4OXO/uXwXmp1wPgJkNm9n9wDPAne7+beAMdz8EEH6+Orz8TOCJSPeDYZtoIFXE37cPZvPmwapVM9uWLZt+Rlw94ijbt6e3f/azMDIy89zISNBehOhsJGlAhuDcgQPwoQ91rhi6LUEKsG9fvuu6mb2JuUtepfCkmf0J8F7gNjM7KU9fdz/u7hcAy4GLzOx1KZfHvRfO+hMzsw1mNmlmk4cPH84pviibPBXAst5oo2zZAi+/PLPt5Zdh796ZbU89BaecEgzAy5enF3LJknHdOrjxxpkL8G68sdgCvPbZSB5efhmuuSbY37kz/pqk9m7NU0ND+WZzRRcBFvm3Fg0nydkQ3YBR4JeAleHxUuBtefpG7vFJ4OMETuSlkfs84tNO5s2R6+8A3pJ2Tzma6yMrcqdMh2unDtEynLjLls3st2zZzPNJayFakTxZMhQtspPnO3X6W5p1Fn0Ut8ZkZESO6iZDiqPZPG2+G2Jm5wIH3f0lM7sYeD3wBXf/YUqfJcDL7v5DM1sAfB34L8DPA8+5+3Vmdi1wmrt/wszOB/4HgR9hGYETeqW7J76DjY+P++TkZKb8onxOOgmOHp3dPn8+vPRS8LYY90Y6Ngb7989uz+s/iKOTeyb9t9+0KTAvpb35L1sGTz6Z7xlZ508/PUjl0c7ixfDss7Pb83ynTn/LoaFiM54WRb+DqB8z2+3u43Hn8pqPvgwcN7OfBD4PnEMwgKexFLjbzB4EvkPgU/gaQcbVS83sUeDS8Bh33wPcAuwFbgeuTlMI/U7Z0/Gyo3DiFEK0PclJWYXzslPndjt5TUFPPTW9361vJW4wTWvPQ6d+nfa6zXmp4juIGkmaQkQ34L7w8xPAr4X7383Tt8qtX81HZa8BKKOubztZZoyi+XU6NXm0zB6dyNhOmikoqX/Wb5t1j6IyVmk+SlsEmEbR7yDqhxKypL5sZu8DPgh8LWwbSbledEHZse5ZUThVUDSDaXs4ahE82wKai05MJ9u2BRFQSRFRVYS9ZjE21lm/pNDeLOr4jqI68iqFDwFvAba6+2Nmdg6QEB8huqVs00ueSKGiZC2QKppaO8kc1USWLZt5vHp1EAkVFxFVVthri40bs9vjFHLLdAiB4lq1Kn9obxaf/exspT5/fuffUdRM0hRiLmz9aj4qOydPVoK0TijbZFDEdJP3mUV/xzypNtqTz+Ux9aXliOokg2haPqe4Zy5ePDs6qOw0FqrBPLegi4R4t4Sf3yOovtbavocqr1VG2Uqh2wL2cZStFLpRCEnPLDrg5n1WntoErXKcWQNkL+oXV1E/Qcxt0pRCakiqmS1190NmFmuldPeS4j46o19DUoeG4u3kZp1FiJR9v1bfJFL+SyWSFNaYl7hnzpsXbyIbHoZjx/Jfn9Y/6beNMjqabDorGrrbCVX8+4u5TcchqT6djuJAqAD+AXg+sokKyCrxWPf95gpFfSl5Ha3R/nl+w7QggV6E7vbi318rmvuHvFlSrzKzpwlMR7vDrf9e0RtCXHK4+fM7rz3chFrGWXQb024WbJdcMt1WdA1BeyRREtHzcb9tHElrKXoxYFf9719GPibRHPJGH30cON/dV7j7OeH2E1UKNui0T/c7Mcm0yBsJVPXbXi/eJnftmlYMSW/+eWcESSayaP/23zaJJEXTC4VdNBKsKE1IFy5KJMnZEN0IVhiP5rm2l1u/OprrcAxWXRymmwL33Tie80TqRK9Nu2dW/05+l9ZvUyRyp2mRPr2u+Sy6hxJyH70BuBH4NkHxnJZC+fVqVFU+5GgujzJzFbVkn5gI3hYffzw5r07r/t3kPkqToQhlOM+rzgPUMtVE38zTHNm9oBfOclEuZeQ++hPgG8DfMe1T2F2OeKKdJBt1Htt1p5Tt8Gy3Myc5d8vKW9QU/umfirUXpYmmmrngsxL5yasUjrn7b7r7je6+o7VVKtkA8+KLxdrLoGyHZ9zgFUc3RXmSWLOm/Hvm5cc/LtYOxXwtvUw0mJeqfRait+RVCneHxW2Wmtlpra1SyQaYJBNRlTHlZb/t5Z0BdJNqI441a+Cuuzrrm5W6owqKRu40Nbx43brAVHTiRPAphTB3yasU/hVBEZy/QSGpc4L2t89Nm9LfRst+28s7AygraVrLvdmuEIq8hX/kI8Xa4yiqWIqag2SqEZWT5IGeC1u/Rh91EsESJS7Sp30bGuouaiVLxrxRQq3UHd1GG8VF4nSSgrxItFInv0s7nUTuNC36yL2ZMolk6CL30Sci++9pO/fbaX17sUkpxJMU0tq+VZn7KCkJX7dKJGubP396QKojtLfoM/shL1HZ9T9E9aQphSzz0drI/ua2c5eXNFkRbXSbnz6vPT/N+dktRfwfQ3mNmDk4ehSuuSbYr8MpW9S80w/moCZGRInOyfpztIT9uOOZJ83OMrO7zexhM9tjZteE7Z8ysyfN7P5wuyLSZ7OZ7TOzR8zsskLfpI8YtPz0wcSzPFrrBOpwyhb1zfRD5E4TI6JEFyRNIYIZRlCGs30/7jim71LgjeH+K4D/C6wCPgV8POb6VcADwEkENaD/HhhOe0a/mo/cu7PR5jW1tNdTKPLMLHNQntoEVW6t7yOzRvX0gwls0KAL89FPm9mPzOx54PXhfuv4pzKUzSF3vy/cfx54GDgzpcuVwM3u/pK7PwbsAy7KkK9v6UWI31VXTe+XndSs7Lf/TuiHt/C5QD+YwMQ0Wamzh939le7+CnefF+63jnPXaDazFcAbCNJkAHzUzB40sxvM7NSw7UzgiUi3g6Qrkb7mkkumM3+2Z//slrjyi2XbheuszxsN/xzU+PleprKW8u0vcuU+6uoBZouAvyKo7/wVMzsDeBZw4D8CS939w2b234G/dfedYb/PA7e5+5fb7rcB2ABw9tlnX3ig3/IkECiAXbtmt+ddmNVJDp+i+ZayntFt0ZxuacJMpS6amB9JNIsych91+uAR4MvAhLt/BcDdn3b34+5+AvhTpk1EB4GzIt2XA0+139Pdt7v7uLuPL1mypErxayNOIaS1l0HZTtkf/KBzWUR3KBpIdENlSsHMDPg88LC7/0GkfWnksncDD4X7twJrzewkMzsHWAncW5V8YiZl24VPUxKU2lA0kOiGKmcKq4EPAG9tCz/9HTP7npk9CPwC8BsA7r4HuAXYS1C/4Wp3LzkzjuhVmou051eZS0g0Nz+SmBvMq+rG7v5N4tcy3JbSZyugmIUuWbgweWFaNLIIOh/0Fy2CI0fi2yHZfOQe+CiqVAx1OrmbwNat8T4FRQOJPFTqUxD18OY3Z1/TbmOemIAPfnBmSOoHP5gctfKmN6W31/W2OjLSv4v88qJoINENUgoV0cuQwHa+8Y1810VtzFddNTvK6MSJmWsZ8jyj1X7FFfHnk9q7YdGi6cHvxhs1+MHghuKK7qnMfDTItIcElmGuaTE0FLxtb92afK+84ZhRx3LR4jBJz2i135ZgJExq74YXX6y21oQQg0Tl6xSqpKk1mrutWZvH3p4Wd57XXh+tm1x0bUPW9VnrHppQk1mIQaW2dQqDSi9CAsuIO6/y7bqXPoUqSnoKMahIKVRArwbEJsedZ617KHMgb5nmhBDdI6VQAb1ysjY57jwrAua887p/RlwOJyFEd8inUAFJeX8WL4Znn83u3yufAkzb4svOfZTFvHnT/oyi5P0dhRDxyKfQY5ISwZWRIK6quPMyitYXoVOFAHDBBeXJIYSYiUJS5xhVOYdbJpjt24MBe3g4sNVXZZoZHu5cMdxzT6miCCEiaKZQE5s2BSYUs+Bz06by7n3KKZ3127YNjh0LzD/HjlVrq+/GOdzNLEMIkY6UQgUk2dtb7Zs2wfXXTw9ux48Hx2Uphh/+sJz7pNHKcZS3vZ1t2wIncSdRSApBFaI6pBQqIGu17/bt8eeT2ntFkdQcWbmP8hCdmaxalb+fQlCFqA75FGogyfxRp1mkaGqOrNxHRUlKp7FoUZDGohd+DiGEQlIrIStcMykcc3g4eHPuNtyzk5DUoqk5upWx6vsJIZJRSGqPyfIpXHxx/Pmk9qpYsGB6v+5qXUl+AvkPhOgtUgoVkOVTuP/++PNJ7VXx4ovT+0VTc3TraG6niSY1IQaRKms0n2Vmd5vZw2a2x8yuCdtPM7M7zezR8PPUSJ/NZrbPzB4xs8uqkq1uqlzc1ilFazT/8R8HZrAo8+YF7Z0wNlasXQhRDVXOFI4B/9rd/znwZuBqM1sFXAvscveVwK7wmEBdQ50AAA8XSURBVPDcWuB84HJgm5k10nhQZwGdPCxbVrxP0Wpd69bBTTfNvP6mmzpfZd3LojxCiGR65mg2s68C/y3cLnb3Q2a2FLjH3c8zs80A7v6fw+vvAD7l7n+bdM86HM3tUTowOw9RltO02/NZdOJorptua1AIIfJTu6PZzFYAbwC+DZzh7ocAws9Xh5edCTwR6XYwbGsUW7bMVAgQHK9fPz1zEMWp29EthAioXCmY2SLgy8DH3P1HaZfGtM16jzWzDWY2aWaThw8fLkvM3MS9zULgEG0VvK+SppqsuqWXRXmEEMlUqhTMbIRAIUy4+1fC5qdDsxHh5zNh+0HgrEj35cBT7fd09+3uPu7u40uWLKlO+ATqDpFsKZ4NG/pLMRR1dAshqqHK6CMDPg887O5/EDl1K7A+3F8PfDXSvtbMTjKzc4CVwL1VydcpTQmRLKMcZ7eU6XAv6ugWQlRDlTOF1cAHgLea2f3hdgVwHXCpmT0KXBoe4+57gFuAvcDtwNXu3pAhuFzaQzmz2pNIsrcvXJivfzeznpbD/cCB8mYv3/oWHDwY3O/gweBYCNFblOaiIEUie+LoNvooSlJkTisLaxYLF8KRI/me1U7Z0UJJMqvcphDlkxZ9JKVQkKYohbRynEnlQJOe1wlFy3dmkZUPSghRHrWHpIryyGNv78XK6LKjhZTmQohmoNTZc4yqynEWZevW+EV8nUYLJZXnrDvaS4hBQzOFPmSoB/+qZUcLJRXOUUEdIXqLZgp9SN7ZxOLF3T1n3bryQkZbzuTt21VQR4g60UyhD8mbWfS9761WDiHE3ENKoQ+JWx0cxxe+UL0seWmFpLb8CsePB8ebNtUrlxCDhpRCH9Ju708iqS5yHWzfXqxdCFENUgo1kGTL79bGH2XdumARWVOilbJQSKoQzUBKoQaSbPmt9jVr4s8ntWeRFI3UiyglIcTcQsNCD2lVRLvllvjzrfZ9++LPJ7Vn8drXFmsXQgwuUgoF6ebtemQk+Myq0ZxUk6HTWg2PPFKsvQ5Uo1mIZiClUJBubPR1VRGbC/Z61VMQohlIKcSwaVOQoM0s+IyGRXaTduG007qXrROSZG5SCgnVUxCiGUgptJEVL9+kt+u8zJUUEtGIqf37pRCEqAMphTay4uW7sXH/4Aed9+2GbduCugStmcHwsOoUCCHikVJoI8v+fsUVnd+7ziL027YFdQncg08pBCFEHFXWaL7BzJ4xs4cibZ8ysyfbynO2zm02s31m9oiZXVaVXJBeWzjL/n7bbZ09c2Rk2mmatMq41T5/fvz5pHYhhCiLKmcKNwGXx7T/obtfEG63AZjZKmAtcH7YZ5uZVeIGzaotnGV/zxNBNDIyu95yVBEkVTtrtd9ww2zFYRa0CyFElVSmFNz9r4G8VvQrgZvd/SV3fwzYB1xUhVxbtswsDAPB8ZYtwX6W/T3JBDQ8PB0188pXzi4hefTo9DOyYvLXrYMvfnFmJM4XvyjHqxCieurwKXzUzB4MzUunhm1nAk9ErjkYtpVO0pt+tH31ali+PBiQly8PjlskxdPv2DEdNZPkUG49I09MviJxhBB10GulcD1wLnABcAj4/bA9zsoea2Qxsw1mNmlmk4cPHy4sQNJagVZ7lnkpTzx9Vv3iPPdI83vkodv+QogBxd0r24AVwENZ54DNwObIuTuAt2Td/8ILL/SiLF7sHgz3M7fFi4PzY2Px54eH3c2C8zt3pj9j50730dGZ/UdHs/uV2X/+/Jn958/P318I0d8Ak54wrvZ0pmBmSyOH7wZakUm3AmvN7CQzOwdYCdxbhQxJpp1We5J56fjx+JlDHN2uzs3ye2RxzTWBDyPK0aNBuxBCpGGeFArT7Y3NvgRcDJwOPA18Mjy+gMA0tB+4yt0PhddvAT4MHAM+5u5/mfWM8fFxn5ycLCTXihXxieXGxgLbfdL5pOurYGgoPkLJLF/upbTCOmaBGWvrVvkphBhUzGy3u4/Hnasy+uh97r7U3Ufcfbm7f97dP+DuP+Xur3f3d7YUQnj9Vnc/193Py6MQOiXLyZu3lGV0RlG2/T7LJ9ENeWc7QojBZOBWNGeZdtrPJy1maw3QWY7pTug2Y2ieCm5FzFFCiMGhMvNRL+jEfFSU1qAftfGPjk4rkixzVDfP3bIlmJEUNfdMTMCHPgQvv5x+XV5zlBCiv0gzH82LaxTTtAbipAE6z7qHTp/bqc2/XeahoficTnXmYhJCNJOBMx91QtpCsk7t/1WvI4jKnJS6o5vkfkKI/kRKoUs6sf9X4YdIIymJX6fJ/YQQ/YuUQpd0siYhzzqEMmcSVZm4hBD9h5RCB7QP2FAsT1HSOohWe9kziSpDXIUQ/YWUQkHKGLCzajZ0u6K5nW5DXIUQg4OUQkHKGLCzqruVbe7pNu2GEGJwUEhqQcoYsMfGktc2QGDWiTvfjbmnmxBXIcTgoJlCQcqwz3eSakPmHiFEL5BSKEgZA3bRVBsy9wgheoXSXHRANykohBCibmrJktrPlFEqU5XRhBBNREqhBIoO8L1e0SyEEHmRUuiSTgb4stchCCFEWUgpdEknA7zSTgghmkplSsHMbjCzZ8zsoUjbaWZ2p5k9Gn6eGjm32cz2mdkjZnZZVXKVTScDvNJOCCGaSpUzhZuAy9vargV2uftKYFd4jJmtAtYC54d9tplZQjKIZtHJAK91CEKIplJljea/Bn7Q1nwlsCPc3wG8K9J+s7u/5O6PAfuAi6qSrUw6GeC1DkEI0VR6nebiDHc/BODuh8zs1WH7mcDfRa47GLY1nqzKbGn9pASEEE2jKbmPLKYtdlWdmW0ANgCc3RAjvAZ4IUS/0Ovoo6fNbClA+PlM2H4QOCty3XLgqbgbuPt2dx939/ElS5ZUKqwQQgwavVYKtwLrw/31wFcj7WvN7CQzOwdYCdzbY9mEEGLgqcx8ZGZfAi4GTjezg8AngeuAW8zsV4DHgfcAuPseM7sF2AscA65294SqA0IIIaqiMqXg7u9LOLUm4fqtgIIyhRCiRrSiWQghxBRzOnW2mR0GYmqU5eZ04NmSxKkKyVgOkrEcJGM51C3jmLvHRurMaaXQLWY2mZRTvClIxnKQjOUgGcuhyTLKfCSEEGIKKQUhhBBTDLpS2F63ADmQjOUgGctBMpZDY2UcaJ+CEEKImQz6TEEIIUSEgVQKcQWAmoaZnWVmd5vZw2a2x8yuqVumdszsZDO718weCGX8dN0yxWFmw2b2XTP7Wt2yJGFm+83se2Z2v5lN1i1PHGZ2ipn9mZl9P/x/+Za6ZYpiZueFv19r+5GZfaxuudoxs98I/14eMrMvmdnJdcsUZSDNR2b2c8AR4Avu/rq65YkjTBi41N3vM7NXALuBd7n73ppFm8LMDFjo7kfMbAT4JnCNu/9dRteeYma/CYwDr3T3d9QtTxxmth8Yd/fGxteb2Q7g/7j758xsPjDq7j+sW644wiJdTwJvcvdu1jKVipmdSfB3ssrdXwzT+9zm7jfVK9k0AzlTSCgA1Cjc/ZC73xfuPw88TMNqTHjAkfBwJNwa9ZZhZsuBtwOfq1uWuYyZvRL4OeDzAO5+tKkKIWQN8PdNUggR5gELzGweMEpCRui6GEilMNcwsxXAG4Bv1yvJbELTzP0EadDvdPemyfgZ4BPAiboFycCBr5vZ7rBmSNP4CeAwcGNoivucmS2sW6gU1gJfqluIdtz9SeD3CBKCHgL+0d2/Xq9UM5FSaDhmtgj4MvAxd/9R3fK04+7H3f0CghoYF5lZY8xxZvYO4Bl33123LDlY7e5vBH4RuDo0cTaJecAbgevd/Q3AjwlrrDeN0LT1TuB/1S1LO2Z2KkH54XOAZcBCM3t/vVLNREqhwYR2+i8DE+7+lbrlSSM0JdwDXF6zKFFWA+8M7fU3A281s531ihSPuz8Vfj4D/DnNq1F+EDgYmQn+GYGSaCK/CNzn7k/XLUgMlwCPufthd38Z+ArwL2qWaQZSCg0ldOJ+HnjY3f+gbnniMLMlZnZKuL+A4D/89+uVahp33+zuy919BYE54Rvu3qi3MgAzWxgGExCaZN4GNCoyzt3/H/CEmZ0XNq0hqH/SRN5HA01HIY8Dbzaz0fBvfA2Bv7AxDKRSCAsA/S1wnpkdDIv+NI3VwAcI3m5bIXZX1C1UG0uBu83sQeA7BD6FxoZ9NpgzgG+a2QMEFQf/wt1vr1mmOH4NmAj/vS8AfrtmeWZhZqPApQRv4I0jnGn9GXAf8D2CMbhRq5sHMiRVCCFEPAM5UxBCCBGPlIIQQogppBSEEEJMIaUghBBiCikFIYQQU0gpiIHBzI63ZdHseEWumf1NmbK13XvczP6oqvsLkYZCUsXAYGZH3H1R3XII0WQ0UxADT1jL4NNmdl9Y0+C1YfsSM7szbP8TMztgZqeH546Enxeb2T2ROgMT4UpVzOxCM/urMMndHWE69PZnvyfMq/+Amf115J5fC/dvi8xs/tHM1odJCH/XzL5jZg+a2VW9+q1E/yOlIAaJBW3mo38ZOfdsmJDueuDjYdsnCVJjvJEgH9HZCfd9A/AxYBVBNtHVYd6q/wr8srtfCNwAbI3p+1vAZe7+0wRJ3Gbg7leECQd/BTgA/O9w/x/d/WeAnwF+1czOyf8zCJHMvLoFEKKHvBgOsHG00iLsBn4p3P9Z4N0A7n67mf1DQt973f0gQJhGfAXwQ+B1wJ3hxGGYIFVyO98CbgqLrcSmZghnJ18E3uvu/2hmbwNeb2a/HF7yKmAl8FiCfELkRkpBiICXws/jTP9dWMG+0f4G7HH31JKV7v4RM3sTQSGg+81shtIKK4jdDPwHd28lyTPg19z9jpzyCZEbmY+ESOabwHsBwrfzUwv0fQRYYmEdYzMbMbPz2y8ys3Pd/dvu/lvAs8BZbZdcBzzo7jdH2u4ANoYmKszsNQ0veCPmEJopiEFiQWjeaXG7u6eFpX4a+FLoe/grAvPP83ke5O5HQ/POH5nZqwj+1j4D7Gm79HfNbCXB2/8u4AHg5yPnPw7sicj9WwSlRVcA94VO7cPAu/LIJUQWCkkVIgEzOwk47u7Hwjf+61N8EkL0BZopCJHM2cAtZjYEHAV+tWZ5hKgczRSEEEJMIUezEEKIKaQUhBBCTCGlIIQQYgopBSGEEFNIKQghhJhCSkEIIcQU/x8d8E4srBS3/wAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# GRÁFICO Y CALCULO VARIABLES DEPENDIENTES E INDEPENDIENTES\n", | |
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n", | |
"plt.xlabel(\"Engine size\")\n", | |
"plt.ylabel(\"Emission\")\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Coefficients: [[39.01738722]]\n", | |
"Intercept: [126.05998845]\n" | |
] | |
} | |
], | |
"source": [ | |
"# CALCULO DE COEFICIENTE E INTERCEPCIÓN. Dependiente e Independiente\n", | |
"from sklearn import linear_model\n", | |
"regr = linear_model.LinearRegression()\n", | |
"train_x = np.asanyarray(train[['ENGINESIZE']])\n", | |
"train_y = np.asanyarray(train[['CO2EMISSIONS']])\n", | |
"regr.fit (train_x, train_y)\n", | |
"# The coefficients\n", | |
"print ('Coefficients: ', regr.coef_)\n", | |
"print ('Intercept: ',regr.intercept_)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 27, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"Text(0, 0.5, 'Emission')" | |
] | |
}, | |
"execution_count": 27, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# TRASAR LÍNEA DE AJUSTE\n", | |
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n", | |
"plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n", | |
"plt.xlabel(\"Engine size\")\n", | |
"plt.ylabel(\"Emission\")\n", | |
"plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n", | |
"plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n", | |
"plt.xlabel(\"Engine size\")\n", | |
"plt.ylabel(\"Emission\")\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Mean absolute error: 99.20\n", | |
"Residual sum of squares (MSE): 11199.45\n", | |
"R2-score: -1.19\n" | |
] | |
} | |
], | |
"source": [ | |
"# CALCULO DE ERROR PORCENTUAL\n", | |
"from sklearn.metrics import r2_score\n", | |
"\n", | |
"test_x = np.asanyarray(test[['CYLINDERS']])\n", | |
"test_y = np.asanyarray(test[['CO2EMISSIONS']])\n", | |
"test_y_ = regr.predict(test_x)\n", | |
"\n", | |
"print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n", | |
"print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n", | |
"print(\"R2-score: %.2f\" % r2_score(test_y_ , test_y) )" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 29, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Predicción: : 262.62\n" | |
] | |
} | |
], | |
"source": [ | |
"# vamos a predicir y = regresion_lineal(5)\n", | |
"nuevo_x = np.array([3.5]) \n", | |
"prediccion = regr.predict( nuevo_x.reshape(-1,1))\n", | |
"\n", | |
"print(\"Predicción: : %.2f\" % prediccion )\n", | |
"# resultado: [1.7449]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python", | |
"language": "python", | |
"name": "conda-env-python-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.7" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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