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vipassanaecon/ISL - simple regression analysis.ipynb

Created January 21, 2022 22:30
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ISL - simple regression analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[230.1]\n",
" [ 44.5]\n",
" [ 17.2]\n",
" [151.5]\n",
" [180.8]\n",
" [ 8.7]\n",
" [ 57.5]\n",
" [120.2]\n",
" [ 8.6]\n",
" [199.8]\n",
" [ 66.1]\n",
" [214.7]\n",
" [ 23.8]\n",
" [ 97.5]\n",
" [204.1]\n",
" [195.4]\n",
" [ 67.8]\n",
" [281.4]\n",
" [ 69.2]\n",
" [147.3]\n",
" [218.4]\n",
" [237.4]\n",
" [ 13.2]\n",
" [228.3]\n",
" [ 62.3]\n",
" [262.9]\n",
" [142.9]\n",
" [240.1]\n",
" [248.8]\n",
" [ 70.6]\n",
" [292.9]\n",
" [112.9]\n",
" [ 97.2]\n",
" [265.6]\n",
" [ 95.7]\n",
" [290.7]\n",
" [266.9]\n",
" [ 74.7]\n",
" [ 43.1]\n",
" [228. ]\n",
" [202.5]\n",
" [177. ]\n",
" [293.6]\n",
" [206.9]\n",
" [ 25.1]\n",
" [175.1]\n",
" [ 89.7]\n",
" [239.9]\n",
" [227.2]\n",
" [ 66.9]\n",
" [199.8]\n",
" [100.4]\n",
" [216.4]\n",
" [182.6]\n",
" [262.7]\n",
" [198.9]\n",
" [ 7.3]\n",
" [136.2]\n",
" [210.8]\n",
" [210.7]\n",
" [ 53.5]\n",
" [261.3]\n",
" [239.3]\n",
" [102.7]\n",
" [131.1]\n",
" [ 69. ]\n",
" [ 31.5]\n",
" [139.3]\n",
" [237.4]\n",
" [216.8]\n",
" [199.1]\n",
" [109.8]\n",
" [ 26.8]\n",
" [129.4]\n",
" [213.4]\n",
" [ 16.9]\n",
" [ 27.5]\n",
" [120.5]\n",
" [ 5.4]\n",
" [116. ]\n",
" [ 76.4]\n",
" [239.8]\n",
" [ 75.3]\n",
" [ 68.4]\n",
" [213.5]\n",
" [193.2]\n",
" [ 76.3]\n",
" [110.7]\n",
" [ 88.3]\n",
" [109.8]\n",
" [134.3]\n",
" [ 28.6]\n",
" [217.7]\n",
" [250.9]\n",
" [107.4]\n",
" [163.3]\n",
" [197.6]\n",
" [184.9]\n",
" [289.7]\n",
" [135.2]\n",
" [222.4]\n",
" [296.4]\n",
" [280.2]\n",
" [187.9]\n",
" [238.2]\n",
" [137.9]\n",
" [ 25. ]\n",
" [ 90.4]\n",
" [ 13.1]\n",
" [255.4]\n",
" [225.8]\n",
" [241.7]\n",
" [175.7]\n",
" [209.6]\n",
" [ 78.2]\n",
" [ 75.1]\n",
" [139.2]\n",
" [ 76.4]\n",
" [125.7]\n",
" [ 19.4]\n",
" [141.3]\n",
" [ 18.8]\n",
" [224. ]\n",
" [123.1]\n",
" [229.5]\n",
" [ 87.2]\n",
" [ 7.8]\n",
" [ 80.2]\n",
" [220.3]\n",
" [ 59.6]\n",
" [ 0.7]\n",
" [265.2]\n",
" [ 8.4]\n",
" [219.8]\n",
" [ 36.9]\n",
" [ 48.3]\n",
" [ 25.6]\n",
" [273.7]\n",
" [ 43. ]\n",
" [184.9]\n",
" [ 73.4]\n",
" [193.7]\n",
" [220.5]\n",
" [104.6]\n",
" [ 96.2]\n",
" [140.3]\n",
" [240.1]\n",
" [243.2]\n",
" [ 38. ]\n",
" [ 44.7]\n",
" [280.7]\n",
" [121. ]\n",
" [197.6]\n",
" [171.3]\n",
" [187.8]\n",
" [ 4.1]\n",
" [ 93.9]\n",
" [149.8]\n",
" [ 11.7]\n",
" [131.7]\n",
" [172.5]\n",
" [ 85.7]\n",
" [188.4]\n",
" [163.5]\n",
" [117.2]\n",
" [234.5]\n",
" [ 17.9]\n",
" [206.8]\n",
" [215.4]\n",
" [284.3]\n",
" [ 50. ]\n",
" [164.5]\n",
" [ 19.6]\n",
" [168.4]\n",
" [222.4]\n",
" [276.9]\n",
" [248.4]\n",
" [170.2]\n",
" [276.7]\n",
" [165.6]\n",
" [156.6]\n",
" [218.5]\n",
" [ 56.2]\n",
" [287.6]\n",
" [253.8]\n",
" [205. ]\n",
" [139.5]\n",
" [191.1]\n",
" [286. ]\n",
" [ 18.7]\n",
" [ 39.5]\n",
" [ 75.5]\n",
" [ 17.2]\n",
" [166.8]\n",
" [149.7]\n",
" [ 38.2]\n",
" [ 94.2]\n",
" [177. ]\n",
" [283.6]\n",
" [232.1]]\n",
"[22.1 10.4 9.3 18.5 12.9 7.2 11.8 13.2 4.8 10.6 8.6 17.4 9.2 9.7\n",
" 19. 22.4 12.5 24.4 11.3 14.6 18. 12.5 5.6 15.5 9.7 12. 15. 15.9\n",
" 18.9 10.5 21.4 11.9 9.6 17.4 9.5 12.8 25.4 14.7 10.1 21.5 16.6 17.1\n",
" 20.7 12.9 8.5 14.9 10.6 23.2 14.8 9.7 11.4 10.7 22.6 21.2 20.2 23.7\n",
" 5.5 13.2 23.8 18.4 8.1 24.2 15.7 14. 18. 9.3 9.5 13.4 18.9 22.3\n",
" 18.3 12.4 8.8 11. 17. 8.7 6.9 14.2 5.3 11. 11.8 12.3 11.3 13.6\n",
" 21.7 15.2 12. 16. 12.9 16.7 11.2 7.3 19.4 22.2 11.5 16.9 11.7 15.5\n",
" 25.4 17.2 11.7 23.8 14.8 14.7 20.7 19.2 7.2 8.7 5.3 19.8 13.4 21.8\n",
" 14.1 15.9 14.6 12.6 12.2 9.4 15.9 6.6 15.5 7. 11.6 15.2 19.7 10.6\n",
" 6.6 8.8 24.7 9.7 1.6 12.7 5.7 19.6 10.8 11.6 9.5 20.8 9.6 20.7\n",
" 10.9 19.2 20.1 10.4 11.4 10.3 13.2 25.4 10.9 10.1 16.1 11.6 16.6 19.\n",
" 15.6 3.2 15.3 10.1 7.3 12.9 14.4 13.3 14.9 18. 11.9 11.9 8. 12.2\n",
" 17.1 15. 8.4 14.5 7.6 11.7 11.5 27. 20.2 11.7 11.8 12.6 10.5 12.2\n",
" 8.7 26.2 17.6 22.6 10.3 17.3 15.9 6.7 10.8 9.9 5.9 19.6 17.3 7.6\n",
" 9.7 12.8 25.5 13.4]\n"
]
}
],
"source": [
"tv = np.array([230.1, 44.5,\t17.2,\t151.5,\t180.8,\t8.7,\t57.5,\t120.2,\t8.6,\t199.8,\t66.1,\t214.7,\t23.8,\t97.5,\t204.1,\t195.4,\t67.8,\t281.4,\t69.2,\t147.3,\t218.4,\t237.4,\t13.2,\t228.3,\t62.3,\t262.9,\t142.9,\t240.1,\t248.8,\t70.6,\t292.9,\t112.9,\t97.2,\t265.6,\t95.7,\t290.7,\t266.9,\t74.7,\t43.1,\t228,\t202.5,\t177,\t293.6,\t206.9,\t25.1,\t175.1,\t89.7,\t239.9,\t227.2,\t66.9,\t199.8,\t100.4,\t216.4,\t182.6,\t262.7,\t198.9,\t7.3,\t136.2,\t210.8,\t210.7,\t53.5,\t261.3,\t239.3,\t102.7,\t131.1,\t69,\t31.5,\t139.3,\t237.4,\t216.8,\t199.1,\t109.8,\t26.8,\t129.4,\t213.4,\t16.9,\t27.5,\t120.5,\t5.4,\t116,\t76.4,\t239.8,\t75.3,\t68.4,\t213.5,\t193.2,\t76.3,\t110.7,\t88.3,\t109.8,\t134.3,\t28.6,\t217.7,\t250.9,\t107.4,\t163.3,\t197.6,\t184.9,\t289.7,\t135.2,\t222.4,\t296.4,\t280.2,\t187.9,\t238.2,\t137.9,\t25,\t90.4,\t13.1,\t255.4,\t225.8,\t241.7,\t175.7,\t209.6,\t78.2,\t75.1,\t139.2,\t76.4,\t125.7,\t19.4,\t141.3,\t18.8,\t224,\t123.1,\t229.5,\t87.2,\t7.8,\t80.2,\t220.3,\t59.6,\t0.7,\t265.2,\t8.4,\t219.8,\t36.9,\t48.3,\t25.6,\t273.7,\t43,\t184.9,\t73.4,\t193.7,\t220.5,\t104.6,\t96.2,\t140.3,\t240.1,\t243.2,\t38,\t44.7,\t280.7,\t121,\t197.6,\t171.3,\t187.8,\t4.1,\t93.9,\t149.8,\t11.7,\t131.7,\t172.5,\t85.7,\t188.4,\t163.5,\t117.2,\t234.5,\t17.9,\t206.8,\t215.4,\t284.3,\t50,\t164.5,\t19.6,\t168.4,\t222.4,\t276.9,\t248.4,\t170.2,\t276.7,\t165.6,\t156.6,\t218.5,\t56.2,\t287.6,\t253.8,\t205,\t139.5,\t191.1,\t286,\t18.7,\t39.5,\t75.5,\t17.2,\t166.8,\t149.7,\t38.2,\t94.2,\t177,\t283.6,\t232.1]).reshape((-1, 1))\n",
"\n",
"sales = np.array([22.1,\t10.4,\t9.3,\t18.5,\t12.9,\t7.2,\t11.8,\t13.2,\t4.8,\t10.6,\t8.6,\t17.4,\t9.2,\t9.7,\t19,\t22.4,\t12.5,\t24.4,\t11.3,\t14.6,\t18,\t12.5,\t5.6,\t15.5,\t9.7,\t12,\t15,\t15.9,\t18.9,\t10.5,\t21.4,\t11.9,\t9.6,\t17.4,\t9.5,\t12.8,\t25.4,\t14.7,\t10.1,\t21.5,\t16.6,\t17.1,\t20.7,\t12.9,\t8.5,\t14.9,\t10.6,\t23.2,\t14.8,\t9.7,\t11.4,\t10.7,\t22.6,\t21.2,\t20.2,\t23.7,\t5.5,\t13.2,\t23.8,\t18.4,\t8.1,\t24.2,\t15.7,\t14,\t18,\t9.3,\t9.5,\t13.4,\t18.9,\t22.3,\t18.3,\t12.4,\t8.8,\t11,\t17,\t8.7,\t6.9,\t14.2,\t5.3,\t11,\t11.8,\t12.3,\t11.3,\t13.6,\t21.7,\t15.2,\t12,\t16,\t12.9,\t16.7,\t11.2,\t7.3,\t19.4,\t22.2,\t11.5,\t16.9,\t11.7,\t15.5,\t25.4,\t17.2,\t11.7,\t23.8,\t14.8,\t14.7,\t20.7,\t19.2,\t7.2,\t8.7,\t5.3,\t19.8,\t13.4,\t21.8,\t14.1,\t15.9,\t14.6,\t12.6,\t12.2,\t9.4,\t15.9,\t6.6,\t15.5,\t7,\t11.6,\t15.2,\t19.7,\t10.6,\t6.6,\t8.8,\t24.7,\t9.7,\t1.6,\t12.7,\t5.7,\t19.6,\t10.8,\t11.6,\t9.5,\t20.8,\t9.6,\t20.7,\t10.9,\t19.2,\t20.1,\t10.4,\t11.4,\t10.3,\t13.2,\t25.4,\t10.9,\t10.1,\t16.1,\t11.6,\t16.6,\t19,\t15.6,\t3.2,\t15.3,\t10.1,\t7.3,\t12.9,\t14.4,\t13.3,\t14.9,\t18,\t11.9,\t11.9,\t8,\t12.2,\t17.1,\t15,\t8.4,\t14.5,\t7.6,\t11.7,\t11.5,\t27,\t20.2,\t11.7,\t11.8,\t12.6,\t10.5,\t12.2,\t8.7,\t26.2,\t17.6,\t22.6,\t10.3,\t17.3,\t15.9,\t6.7,\t10.8,\t9.9,\t5.9,\t19.6,\t17.3,\t7.6,\t9.7,\t12.8,\t25.5,\t13.4\n",
"])\n",
"\n",
"print(tv)\n",
"print(sales)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"model = LinearRegression().fit(tv, sales)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"coefficient of determination: 0.611875050850071\n",
"intercept: 7.032593549127693\n",
"slope: [0.04753664]\n"
]
}
],
"source": [
"r_squared = model.score(tv, sales)\n",
"print('coefficient of determination:', r_squared)\n",
"\n",
"print('intercept:', model.intercept_) \n",
"print('slope:', model.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"predicted response:\n",
"[17.97077451 9.14797405 7.85022376 14.23439457 15.62721814 7.44616232\n",
" 9.76595037 12.74649773 7.44140866 16.53041431 10.17476548 17.23871025\n",
" 8.16396559 11.66741599 16.73482186 16.32125309 10.25557777 20.40940417\n",
" 10.32212907 14.03474068 17.41459582 18.31779199 7.6600772 17.88520856\n",
" 9.99412625 19.52997632 13.82557947 18.44614092 18.85970969 10.38868036\n",
" 20.95607553 12.39948025 11.653155 19.65832525 11.58185004 20.85149492\n",
" 19.72012288 10.58358059 9.08142275 17.87094757 16.65876324 15.44657891\n",
" 20.98935118 16.86792445 8.22576322 15.35625929 11.2966302 18.43663359\n",
" 17.83291826 10.21279479 16.53041431 11.80527225 17.31952254 15.71278409\n",
" 19.52046899 16.48763133 7.37961102 13.50708398 17.05331735 17.04856369\n",
" 9.57580381 19.45391769 18.4081116 11.91460652 13.26464711 10.31262174\n",
" 8.52999772 13.65444756 18.31779199 17.3385372 16.49713866 12.25211667\n",
" 8.30657551 13.18383482 17.17691262 7.83596277 8.33985116 12.76075872\n",
" 7.28929141 12.54684384 10.66439288 18.43187992 10.61210257 10.28409975\n",
" 17.18166628 16.21667248 10.65963921 12.29489965 11.2300789 12.25211667\n",
" 13.41676436 8.39214147 17.38132017 18.95953663 12.13802873 14.79532693\n",
" 16.4258337 15.82211837 20.80395828 13.45954734 17.60474238 21.12245377\n",
" 20.3523602 15.96472829 18.3558213 13.58789626 8.22100956 11.32990584\n",
" 7.65532354 19.17345152 17.76636696 18.52219954 15.38478127 16.99627338\n",
" 10.74995883 10.60259525 13.6496939 10.66439288 13.00794925 7.95480437\n",
" 13.74952084 7.92628239 17.68080101 12.88435399 17.94225253 11.17778859\n",
" 7.40337934 10.84503211 17.50491544 9.86577732 7.0658692 19.63931059\n",
" 7.43190133 17.48114712 8.78669558 9.32861328 8.24953154 20.04337204\n",
" 9.07666909 15.82211837 10.52178296 16.2404408 17.51442276 12.00492614\n",
" 11.60561836 13.7019842 18.44614092 18.5935045 8.83898589 9.15748138\n",
" 20.37612852 12.78452704 16.4258337 15.17562006 15.95997462 7.22749377\n",
" 11.49628409 14.15358229 7.58877224 13.29316909 15.23266402 11.10648363\n",
" 15.98849661 14.80483426 12.60388781 18.17993573 7.88349941 16.86317079\n",
" 17.2719859 20.54726042 9.40942557 14.8523709 7.9643117 15.0377638\n",
" 17.60474238 20.19548929 18.84069503 15.12332975 20.18598196 14.9046612\n",
" 14.47683144 17.41934948 9.70415274 20.70413134 19.09739289 16.77760484\n",
" 13.66395489 16.11684554 20.62807271 7.92152873 8.91029085 10.6216099\n",
" 7.85022376 14.96170517 14.14882862 8.84849321 11.51054508 15.44657891\n",
" 20.51398478 18.06584779]\n"
]
}
],
"source": [
"sales_pred = model.predict(tv)\n",
"print('predicted response:', sales_pred, sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(9,7))\n",
"plt.scatter(tv, sales, color = \"blue\")\n",
"plt.plot(tv, model.predict(tv), color = \"black\")\n",
"plt.title(\"example\")\n",
"plt.xlabel(\"tv\")\n",
"plt.ylabel(\"sales\")\n",
"plt.axis([0, 300, 0, 30])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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