{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## কুমড়ার মূল্য নির্ধারণের জন্য লিনিয়ার এবং পলিনোমিয়াল রিগ্রেশন - পাঠ ৩\n", "\n", "প্রয়োজনীয় লাইব্রেরি এবং ডেটাসেট লোড করুন। ডেটাকে একটি ডেটাফ্রেমে রূপান্তর করুন যা ডেটার একটি অংশ ধারণ করে:\n", "\n", "- শুধুমাত্র সেই কুমড়াগুলো নিন যেগুলো বাসেল অনুযায়ী মূল্য নির্ধারণ করা হয়েছে\n", "- তারিখকে মাসে রূপান্তর করুন\n", "- উচ্চ এবং নিম্ন মূল্যের গড় হিসাব করে মূল্য নির্ধারণ করুন\n", "- মূল্যকে বাসেল পরিমাণ অনুযায়ী রূপান্তর করুন\n" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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City NameTypePackageVarietySub VarietyGradeDateLow PriceHigh PriceMostly Low...Unit of SaleQualityConditionAppearanceStorageCropRepackTrans ModeUnnamed: 24Unnamed: 25
0BALTIMORENaN24 inch binsNaNNaNNaN4/29/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
1BALTIMORENaN24 inch binsNaNNaNNaN5/6/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
2BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
3BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
4BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN11/5/1690.0100.090.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
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5 rows × 26 columns

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" ], "text/plain": [ " City Name Type Package Variety Sub Variety Grade Date \\\n", "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", "\n", " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", "0 270.0 280.0 270.0 ... NaN NaN NaN \n", "1 270.0 280.0 270.0 ... NaN NaN NaN \n", "2 160.0 160.0 160.0 ... NaN NaN NaN \n", "3 160.0 160.0 160.0 ... NaN NaN NaN \n", "4 90.0 100.0 90.0 ... NaN NaN NaN \n", "\n", " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n", "0 NaN NaN NaN E NaN NaN NaN \n", "1 NaN NaN NaN E NaN NaN NaN \n", "2 NaN NaN NaN N NaN NaN NaN \n", "3 NaN NaN NaN N NaN NaN NaN \n", "4 NaN NaN NaN N NaN NaN NaN \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from datetime import datetime\n", "\n", "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n", "pumpkins.head()" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthDayOfYearVarietyCityPackageLow PriceHigh PricePrice
709267PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
719267PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7210274PIE TYPEBALTIMORE1 1/9 bushel cartons18.018.016.363636
7310274PIE TYPEBALTIMORE1 1/9 bushel cartons17.017.015.454545
7410281PIE TYPEBALTIMORE1 1/9 bushel cartons15.015.013.636364
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" ], "text/plain": [ " Month DayOfYear Variety City Package Low Price \\\n", "70 9 267 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 \n", "71 9 267 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 \n", "72 10 274 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 \n", "73 10 274 PIE TYPE BALTIMORE 1 1/9 bushel cartons 17.0 \n", "74 10 281 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 \n", "\n", " High Price Price \n", "70 15.0 13.636364 \n", "71 18.0 16.363636 \n", "72 18.0 16.363636 \n", "73 17.0 15.454545 \n", "74 15.0 13.636364 " ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", "\n", "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n", "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", "\n", "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n", "\n", "month = pd.DatetimeIndex(pumpkins['Date']).month\n", "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n", "\n", "new_pumpkins = pd.DataFrame(\n", " {'Month': month, \n", " 'DayOfYear' : day_of_year, \n", " 'Variety': pumpkins['Variety'], \n", " 'City': pumpkins['City Name'], \n", " 'Package': pumpkins['Package'], \n", " 'Low Price': pumpkins['Low Price'],\n", " 'High Price': pumpkins['High Price'], \n", " 'Price': price})\n", "\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n", "\n", "new_pumpkins.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "একটি স্ক্যাটারপ্লট আমাদের মনে করিয়ে দেয় যে আমাদের কাছে শুধুমাত্র আগস্ট থেকে ডিসেম্বর পর্যন্ত মাসের ডেটা রয়েছে। সম্ভবত আমাদের আরও ডেটা প্রয়োজন যাতে আমরা একটি সরলরৈখিক পদ্ধতিতে সিদ্ধান্ত নিতে পারি।\n" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "new_pumpkins.plot.scatter('Month','Price')" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "new_pumpkins.plot.scatter('DayOfYear','Price')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "চলুন দেখি কোনো সম্পর্ক আছে কিনা:\n" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.14878293554077535\n", "-0.16673322492745407\n" ] } ], "source": [ "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n", "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "মনে হচ্ছে সম্পর্কটি বেশ ছোট, তবে আরও গুরুত্বপূর্ণ কিছু সম্পর্ক রয়েছে - কারণ উপরের প্লটে মূল্য পয়েন্টগুলি কয়েকটি স্বতন্ত্র ক্লাস্টারে বিভক্ত। চলুন একটি প্লট তৈরি করি যা বিভিন্ন কুমড়ার প্রজাতি দেখাবে:\n" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "image/png": 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ORc3w4XDeec5C7uedB27KmphZtQo+/xw2bXL2q1YlTk9EQvLp7N+/n/z8/JD0y6XuYgg//OEP+dWvfkVtLXz8Mdxyyx1MmXI9tbVO1s36dMuFhYWcdNJJDdfn5+dz3HHHcfTRR/Otb32Lv/3tbyHlq9f78Y+nc/PN9zSkZQYoKChg06ZNQPMU0nfeeScAp59+OvXDrgsKChg6dDhDh47gtNO+yeuvV7WY5nnr1q1cccUVDBw4kIEDB3LFFVeEpKJetWoVZ555JscccwxHH300v/rVr6gfij5r1ixEhDfffLPh/Oeffx4R4dlnnw25z6xZsyguLg6xbdq0ifz8fPbs2QPApEmTOPHEE0POCU5DPXTo0JCkeVdddVXDfU4//XQGDx7c8Le56KKLuOOOOxqOg/92Dz74INOnT+eee+5p0An32W3cuJEJEyYwcuRIhg4dyrnnxr+EiQ3nNBJOTQ2UlEBdnbOBczx2rHfr7778Mqxskvzj448du+s7o2LLlsay1lNX59i7dfNer1OnTqxcuZK6ujoCgQBvvPEGRx55ZFitGTNmUFhYyMiRU1AVXnjh98yZ8yFVVXDwINx9990N6ZaDqU/tALBo0SIuuOACFi1axJAhQ9i3j4brVZ2tPi1z03w9kVJIN+WhhxbRtWsvHn30dh5/fAa9ez8eMc1zSUkJw4YN44knngDg9ttv55prruGZZ56hrq6OiRMn8rvf/Y7x48eza9cuLrzwQh555JGGFBbDhw+nvLycs846C3AmwI0cObLZfS644AJuuukmdu3aRceOHQF49tlnmThxIocccghbtmzhgw8+oHPnzvzzn/9kwIABDdfWp6H+7LPPGDVqFBdddBG5YZIZzZ07l6Ki0KHzP//5zwHnRy/4bzd9+vSQ88J9dtOmTWPcuHFMnToVcNJ5xIu1+I2EU1nZPMtjbq5j94pI2QVizToQKRTTaK8Blrh7L/TgnHPO4U9/+hPQmJkzHF26dOG22+5g5sxSZs68nu9975ccemg3RBzH3RbOOOMMrr32Wh577DHASb8sEnpOPGmZVRv1hg8/kZqaLyPqff755yxbtozbbrutwTZt2jSWLl3KF198wbx58zj55JMZP348AB07duThhx9ueNIAOPXUU1m8eDH79u1jx44dfP755xQWFja7V5cuXTjttNNYsGBBg+2pp55q+Fs/99xznHfeeVxyySURZ0kfffTRdOzYkc2bN0f7Z4mJDRs20KdPn4bjEfE+ymKO30gCBQXN/+H37XPsXjF5cnT21ojUqnfs5UB/YJy7bz1u1bKeQ72z2b17NytWrOCEE06IqHfZZcVs27aZnTu3ce65TohI1Vl0/eabb24IF0yZMiWixvHHH88nn3wCOD/MwZP4y8vv55JLCjnpJEdn/fr1De8Fp5AuLCxk/vz5zbRFGvXee+9VvvnNyRHTPK9evbohDFJPfUhk1apVrFq1ilGjRoVcM3DgQHbs2MG2bdvc+wljx47ltdde48UXX2TixIkR611cXNzg1NevX8+aNWsa0krX/+AWFxdHXAPhgw8+4Oijj+awww4L+/6UKVMa/jY333xzxHKEI9xnd/3111NSUsIZZ5zBHXfcEfJZxIqFeoyEk5/vxN1LSpyW/r59zrGXyyxMmODE+INWG2T48NjCPOA45EAgNDwTCEC3bjVACVDnbrjHY4HIFYqs13g8YsQIKisrKS8vbzWOu3HjOrZu/TcHDgh79uwgEOhM//6O448U6mlKcLqW3Fyn76WqynHal156I7fddhM9ezrvFwT9Src11HPDDWfw739vpEePw7j++hkR0zyrKtL0cSPIHul9IMR+ySWX8OCDD7J161buvfdefv3rX4e9ZsKECVx33XVs27aNp59+mosuuoicnBw2btzI559/zimnnIKI0L59e1auXMmwYcMAJzfR448/zj/+8Q9effXViPUOF+ppK+E+u29961sN93zllVc47rjjWLlyJfGsU2ItfiMpFBc7TmXhQmcfIYoRFytWwIIFzg/MggXOcTwceywMGuQsjj5okHMMlUDTZmuua49FL5SJEydy0003RQzz1DN16lR++cvpFBdfzDPP/BfDh9PgpNvKhx9+GJLbpWdPGnR6945eryl//esiKiurGDnyWJ59dlpEvWOPPZYPP/yQg0FxqoMHD/LRRx8xZMgQjj32WJrm6vrHP/5B586dOfTQxrTRY8aMYeXKlWzatIljjjkmYrkCgQBnn302zz//fEiYZ/78+WzevJkBAwZQUFBAZWVlSLjnxhtv5NNPP2X+/PlcccUV7N69O5Y/S0z06NGDSy+9lCeffJLRo0fz17/+NS49c/xG0sjPh9GjvW3pN2XCBPj972Nv6TelWzcnJNXYMi8Amgaq97n2WPRCufrqq5k2bRrDhw+PqPHKK69QXV3NFVdcwfTpt/Hyy8/z2Wer23T/ev7yl7/w2GOP8R//8R8h9txcJxwTFHWJiy5dAjz88APMmfMEX331VdhzBg0axHHHHceMGTMabDNmzOD4449n0KBBTJkyhXfeeYeFCxcCTqjphz/8Ibfcckszrf/+7/+O2NIPpri4mPvuu4+NGzfyjW98A3DCPK+++mpDKuxly5aFjfNfcMEFFBUVMXv27Db9DeLl//7v/9i1axcA27dv54svvqBfv35xaZrjN4yoyAfKgADQxd2X0VKYJxr69OnTMHojHLt37+ZHP/oRjzzyCCJCp06dmDlzZsNQTwiNExcWFrLX7WCZP38+hYWFHHPMMfz617/mueeeizorKTSP8d96660tnt+7d2+Ki4v57W9/C4RP81xWVsaaNWsYNGgQAwcOZM2aNZSVlQFOC/3FF19kxowZDB48mOHDhzN69OiQOtdzzjnntGkZyPHjx7N+/Xq++93vIiLuOgRrG34EAAYMGECXLl14//33m10/bdo07rvvvpCnlHqCY/xjx45ttSzBhPvsli1bRlFRESNGjODEE0/kmmuuYfTo0VHpNiXhaZlFJAdYCnypqhNEpAcwH6eJVAlcrKotdo9bWmZ/UVPjjMgpKIi/9T53Ljz9NFx8MbTQD5nQssWSltkZzVOJ8zVOXZbOHTtg61bo2hXCDI9PuV5dnTOBq1Mnp0/DSBzRpGVORot/KlARdHwr8KaqHg286R4baYKXE7H69oXLLoOXXnL2cT69JmWSWCP5wGhS6fTXrIFPPoENG5z9mjX+0lu71pmkVlnp7NeujU/P8I6EOn4R6QN8G/h9kHkSUB8cmw1MTmQZDO8Inoi1dauzLymJLQXD3Lmwbl2o7V//cuypLls6sGMHuCMZG9i2zbH7Qa+uDqqrQ23V1c0nsRmpIdEt/geAW4DgQNjh9cstuvuwg2FF5FoRWSoiS2sy9b83zfByItbTT0dnb414y5YOK9EFE5TNoE32ZOvt3Bmd3YiPaL+/CXP8IjIBqFbVZbFcr6qPqWqRqhbFM17V8A4vJ2JdfHF09taIp2x5eXnU1tamlfPv2jU6e7L1OnWKzm7EjqpSW1tLXl5em69J5ASuk4GJInIukAd0EZE5wEYR6a2qG0SkN1DdoorhG7yciDVlCvz0p054p56+fWPv4I2nbH369GHdunWk25Pljh0QPJQ8Ly/075lqvT17IHjN90MP9TZNh9FIXl5eSFqH1kjKYusicjpwkzuq526gVlXvFJFbgR6q2nxAbhA2qsdfZNqonnTm3Xfh9ddh/Hg4+WT/6VVUwOLFMGYMxDBy1IiTSKN6UuH4ewJPA/2AtcB3VDX8zA4Xc/yGYRjRE8nxJyVXj6q+Bbzlvq4FzkrGfQ3DMIzm2MxdwzCMLCOjHX8ylvqLB6/LV1EBs2c7+2zQSwZef0Z+/04aWYKq+n4bNWqURsu8eaqBgGrXrs5+3ryoJRKK1+UrLa1fN8nZSkszWy8ZeP0Z+f07aWQewFIN41OT0rkbL9F27tbUOFP2m+Y+r6ryx0gPr8tXUQFDhza3r14d20gKv+slA68/I79/J43MJJW5epJOMpb6iwevy7d4cXT2dNdLBl5/Rn7/ThrZRUY6/mQs9RcPXpdvzJjo7Omulwy8/oz8/p00souMdPz1szgDAejSxdl7vdRfPHhdviFDoGlq8tLS2MMoftdLBl5/Rn7/ThrZRUbG+Ovx+yxOr8vn9SxJv+slA68/I79/J43MIqUzd+PFZu4abcUcq2E0klWdu0Z2ktyFWAwjfTHHb2QE2bYQi2HEgzn+FOL1LM6XX4ZrrnH2idSL9T7hrps7FyZNin3lrXoSNVzy3Xfh9tudvRd4ref1Z24zi7OEcLO6/LbFMnPX73g9i3PYsNCZscOHJ0Yv1vuEu65Pn1Bb376xl7e6WlUkVE/EscfKuHGheuPHx66VCD2vP3ObWZx5EGHmbsqdelu2THP81dXOP1bwP20gELuTWrAgVKt+W7DAW73bbovtPpH0wm1z5nhb5lj/Bu+8E17vnXf8oed1fb3+Thr+IJLjt1BPCvA6LPHCC9HZY9WbPz+2+0RTjljX3PX6b/D669HZk63ndX1tZnF2kcg1d/NEZLGIfCQiq0Tkv1z7dBH5UkSWu9u5iSqDX/F6FufkydHZY9X77ndju0805Yh1zV2v/wbjx0dnT7ae1/W1mcVZRrjHAC82QIDO7utc4H3gG8B0nNW4sjbUo9oYT+3SxZt46vDhoY/p8cZ7I+nFep9w1/XtG2qLJ8YfT9kiMX58qF68MXmv9byur9ffSVUnVLR4sXchI6/1Mh2SHepx77vDPcx1N//PFksSxcVOZsaFC519cXF8eitWwIIFzhDGBQuc40ToHXFE6Hm9e8eupx5/G7zWW78+9HjDhvj0XnsN3nkHpk1z9q+9Fp+e15+5199Jr+dV2DwNDwn3a+DVBuQAy4EdwF2ubTpQCawA/gB0b00nE1v86YiXHZRz5oTX8kvnrtd62YbXncXW+RwbpKJzV1UPqGoh0AcYIyLDgN8BA4FCYANwb7hrReRaEVkqIktrbFCxL/CygzJSJ65fOne91ss2LK21v0nKqB5V3YKz2PrZqrrR/UE4CDwOhE3Oq6qPqWqRqhbl+yTpit8n83hN08k88XZQButF6sT1S+eu13r1/O53cNppzt4LvNbzakKdpbX2OeEeA7zYgHygm/s6ALwNTAB6B51zI/BUa1p+CPX4fTKP10SazNO9e2i5e/SIXa9Hj9i0IuF1Z7HXerH+7ZKl5+WEOlXvO5+9nrCWDZDsCVzACOBDnFj+SmCaa38S+Ni1vxT8QxBpS7Xj9/tkHq+JFE+NFPdurdyR9PLymtv8EgP2Wu+RR8L/7R55xB96Xve5ZNv/jF+J5PgTOapnhaoep6ojVHWYqv7StV+uqsNd+0RVjXOsROLx+2Qer4kUT40U326t3OH02rWDnJzm9/BLDNhrvUgjUGIdmeK1ntd9Ltn2P5Nu2MzdNuD3yTxeEymeGim+3Vq5w+kdPAgHDjS/h19iwF7rRRoaGeuQSa/1vO5zybb/mbQj3GOA37ZUh3pU/T+Zx2siTeaJtdzz5jmhnU6dnP28ec7Wvr1qTo6zj3fCUL1eu3be6eXkOMnecnLi1/O6T8PvfSTZ9j/jR4gQ6rEVuKLg3XedR8vx4+Hkk/2n5zXhVrPq2xfWrWs8p29fWLu2da3ycmeiUbt2Tmu/rAxuuSU2rUj06AGbN4ce19bGrtepE+zaFXq8Y0fk85Ndvlg/i2TpQfb9z/iNSCtwpbw135bNDy1+I/YOwHAdpR06xKYVCa87O++6K7zeXXf5o3xed8Z6rWf4A+Lp3BWRY0TkTRFZ6R6PEJFfePvbZPidWDsAw3WUHjwYm1Yk/N556vfOWK/1DH/T1s7dx4GfAvvAGbEDXJKoQhn+JNYOwHAdpe0ifPNi7Uz0e+ep3ztjvdYzfE64x4CmG7DE3X8YZFvelmu92DI11JOOmQtj7QAM11nsdWei152dnTqF6nXq5K/y+X3CmpF6iHMc/yYRGQhOdk0RuQgnz44RI+mauXDtWpgzByZOdPZt7fwLl/kxVq1I1NbCI4/Aqac6+3g6TsHpyL3rLigsdPbxdOwmonx33QXt2zvzIdq3d47jwevPw/AvbRrVIyJHAY8BJwGbgX8Cl6lqZUJL5+KXUT1eUVPjOOe6ukZbIOA4xFjSEnmtZ/gf+8yNthBpVE+bWvyq+g9VHYuTf+frqnpKspx+JuL3Waax0DShm5FY/PCZG+lLW0f1/FpEuqnqTlXdLiLdRWRGoguXqfh9lmm02AIZySfVn7mR3rQ1xn+OOqmVAVDVzUDWrZXrFfn5zgSmQAC6dHH2ZWWxP6J7rVdPpFZ8zc4alny5hJqdNdTUOBOz6upg61ZnX1LS9pZ/sJYXZIte/Wd+SPca8gYu4ZDuNZ585hVra5j9xhIq1tqjWybTvo3n5YjIIaq6B0BEAsAhiStW5lNcDGPHNp8Z6xe9+pm2HTo4LcuyMuce5R+XU/JSCR1yOrD3wF5+NqyMDh2KQ2LNOTnw5z/DuW7TIFKZmmqVTSqjeFjs6/1lm97ftpWz57oSONABcvbyt21lFBO73g2PlvPwv0rgYAf4y15K+5bx0PfiXH/R8CVt7dy9BZgI/C/OyJ6rgZdUdWZii+eQaZ27fidSx+GyT2oY9WR/6vY3vhFoH0Dvq2L3V6Fe/dBDYfduEHGuDf7xAKfl2/+B5lpVP6oiv1P0v1rZplextoahj/WH3KAPaV+A1ddWMaRf6vUMfxBv5+5M4A5gCHAs8KtkOX0j+UTqOFz8aSUdckLfyM3J5ef3VBIIQOfOjfbt252Y89694UNAlVvCa1VuqYytzFmmt/jTSqdlHszBXMfuAz3D37Q5LbOqvqKqN6nqf6rqa4kslJFaInUcjhlcwN4DoW/sO7CP711cQFUVPPyw09KPRPCok4Ju4bUKuhXEVuYs0xszuADaNZ0Ovc+x+0DP8DctOn4RecfdbxeRbUHbdhHZ1sq1eSKyWEQ+EpFVIvJfrr2HiLwhIp+5++7eVSe78WoN34aOw0Mat7IyGNIvn7JJZeTlBAhIF/JyApRNKiO/Uz6bNsG2bc4PRCTq6mDaNGc9101r87myWxkdJMAh2oVD2jVqxVTmTk7ZOkiA3INd6CDe6B3SztvyeaU3pF8+pX3LYF8AdneBfQFK+5bFHJYJ0dsTv57hc8JN5/ViAwTo7L7OBd4HvgHMBG517bcCd7WmlakpG7wkWWsCl5aq0rFa+dpipWO1lpa6tjCZHdu0BWnFW+Zhw0L14l2Tddw4b8vntV64zyJeVldV66zXF+vqqgTm/TCSBrGuuYvzVLCytfNa0egIfACcAHyKu84u0Bv4tLXrzfG3TLLWN42UutfLLdYyR1oPeMECb/8GflkzdvXq8HqrV8emZ2QmkRx/qzF+VT0IfCQi/aJ9mhCRHBFZDlQDb6jq+8Dh6q6z6+4Pi3DttSKyVESW1th00BZJ1vqmyUjRG2uZI60HHMkeazn8smbs4sXR2Q0jmLZ27vYGVrk5+V+q31q7SFUPqGoh0AcYIyLD2lowVX1MVYtUtSjfko+0SLLWN01Git5YyxxpPeBI9ljL4Zc1Y8eMic5uGCGEewxougHfDLe15dogjduBm7BQT0JI1vqmTeP5ccf4PSzz8OGhevHG+P2+Zmy4z8IwgiGWNXdFJA/4PjAI+BgoU9X9bflBEZF8YJ+qbnFn+r4O3OX+aNSq6p0icivQQ1VvaUnLJnC1jWStb1pR4YQUxoyBIUOa2yD867fecmYEFxfDiBGO9hFHwL//7V2ZX37ZCe9MngwTJsSv5/c1Y8N9FoZRT6QJXK05/vk4q269DZwDVKnq1DbecAQwG8jBCSk9raq/FJGewNNAP2At8B1V/aolLXP86U9wCohduyLP6DUMwztidfwfq+pw93V7YLGqHp+4YobHHH96Ey4FRDCWR94wEkOsKRsapuO0NcRjGE0JlwIiGMsjbxjJpTXHPzJ4ti4woq0zd43MpaICZs929m0hXAqIYHbvbvn9aIi2bKZnZCXhenz9ttmoHv8Q60iS4MXWc3NVO3RwNi9HpXg9yiXb9IzMg1hG9fgFi/H7g4oKGDq0uX316tARJTU1jTn4IfzrNWvglFNa1/K6bKZnZBNxpWU2DGjbbNHgZRiPPBL69GlcknHhQhg92unE/fzz6O7hRdlMzzAczPEbbaa12aJNl2FsKR+/1zNPTS8+PSO7MMdvtJkhQ6C0NNRWWtoYWohm9E5rWl6XzfQMoxGL8RtRE2m2aCzj9b2eeWp6htFITBO4/II5/vShfoZubq7zAyACeXlO2Mdm6BpGconk+NunojBG5lJcDGPHhh/JYzNzDcMfmOM3PCc/P9TJm8M3DH9hnbuGYRhZhrX4jaQRaWKXPREYRnIxx28kBUvLbBj+wUI9RsKJZmKXYRiJJ2GOX0T6isgiEakQkVUiMtW1TxeRL0Vkubudm6gyGMmjpgaWLAnvwC0ts2H4i0S2+PcD/6mqQ4BvANeLSH1aqftVtdDd/pzAMhhJIDg/T//+znEwraVl3revMe5vGEbiSZjjV9UNqvqB+3o7UAEcmaj7GamhaRgnXOgmP9+J4wcC0KWL08Lv0MF5HQg471kHr2Ekj6TE+EWkADgOeN81lYrIChH5g4h0j3DNtSKyVESW1lgA2LeEC+OEC90UFzvpGhYuhC+/hHXrnNdVVdaxaxjJJuEpG0SkM/AX4A5V/aOIHA5sAhT4FdBbVa9uScNSNviXcPl5bA1dw/AHKcnHLyK5wHPAXFX9I4CqblTVA6p6EHgcsESyaUzTME5LoZvgZQJtyUDDSB0JG8cvIgKUARWqel+QvbeqbnAPzwdWJqoMRnJomp8nnNO/4QZ4+OHw15eWwkMPJbKEhmEEk7BQj4icArwNfAwcdM0/A4qBQpxQTyXwvaAfgrBYqCe9ibRMYDC2ZKBheE/Ss3Oq6juAhHnLhm9mGW1ZDnDxYnP8hpEsbOaukXDashygLRloGMnDHL+RcMItExiMLRloGMnFkrQZSeGhh+C66xqXCQRbMtAwUoU5fiNpDBkS6uTN4RtGarBQj2EYRpZhjt8wDCPLMMdvRKalXMt+0DMMIybM8RvhaS3Xcqr1DMOImYQnafMCm7mbZLzOvGaZ3AwjJaQkSZuRprQ113Kq9AzDiAtz/EZzwi2ZFc8yWV7rGYYRF+b4jeZEk2s5FXqGYcSFxfiNyNTUtJxrOdV6hmG0SNKzcxoZQH6+tw7aa71w+P3HyvRMzw+oqu+3UaNGqWG0yrx5qoGAateuzn7ePNMzvfTV8wBgqYbxqQlz1kBfYBFQAawCprr2HsAbwGfuvntrWub4jVaprnb+2aBxCwQcu+mZXrrpeUQkx5/Izt39wH+q6hDgG8D1IjIUuBV4U1WPBt50j7MTv8+MTaeZtn4fgmp6pucjEub4VXWDqn7gvt6O0/I/EpgEzHZPmw1MTlQZfI3fZ8am20xbvw9BNT3T8xPhHgO83oACYC3QBdjS5L3NrV2fcaEevz9m+vSxtVXqY6xdungbszU900uFngcQIdST8OGcItIZ+Atwh6r+UUS2qGq3oPc3q2r3MNddC1wL0K9fv1FVVVUJLWdSWbLEaUlv3dpo69IFFi6E0aMzTy+Z+H2UhumZXhKJNJwzoY5fRHKBl4HXVPU+1/YpcLqqbhCR3sBbqjq4JZ2MG8fv91w4llvHMDKCpOfqEREByoCKeqfv8hJwpfv6SuDFRJXBtwTPZO3UyduZsV7q5eU5enl5oXqxdvqGu87vHdKmZ2QgiRzVczJwOXCmiCx3t3OBO4FxIvIZMM49zk7qn7a8euryWk8kdA+xd/qGu87vHdKmF5+e4V/CBf79tlnnrk/0Vq+O7T6R9PLy0u9vYHpGGkEKxvGnHr8+BidqDHEvoAhnnwi9xYtjK3e4+rZrBzk50WtFcw/TS52e4Wsy1/H7+TG4oCC04xRg9+74xhBP2A5VOHOhq4AJO7zXGzMmtrHK4eq7dy8cOBC9Vkv38PO47GzTM/xNuMcAv21Rh3r8/hhcXa2amxuql5sbu17NatWdTf5sO3HsXuvFMlY5Un3/53/8PY7a9OLTM1IOEUI9mZmds/6xNbiVWf/YGstIl0TodewYOk4+EIhdr3qxMyc6mH2uvdcQb/WKr4SxY6Mbqxypvscf7wwR9Wrcc3Fx9GUzvcTpGb4lMx2/3x+DvdY7bAzkNrHluvZE6EWbXrml+vo99bPpGRlIZsb4WxuHHqueX1ek6jUEPiyFXcBWnP2HpbG19hOh5/U8A8Mw4iIzHX894cahx0pxsROWWLjQ2RcX+0tv7Ukw+BA4L8/Zrz3JX3rg/TyDZODXkWHZqmd4Q7jAv9+2lHfu+p106MxOx8/D7wt1ZJueETUkeyEWL7eoHf/ixc6XLdjRdOni2DMRr+vrd71k4Pcfv2zTM2IikuPPzFBPosYk+/UxONmdz9GWuyU9j/+mL8/dzDWTqnl57ub4hNyRXBUMZjZXUMFgf02QyjY9w1vC/Rr4bYspZUOixjj79TF42LDQ1tXw4fHpjRsXqjd+fHzlLi0N1Sst9fxvMKxPrcLBhm1439rYxaqrtbTdb0P0Sts97J8WcLbpGTFBVoV66qmudsIJ8X7Z/P5P8c47oVr12zvvxKa3enVkPZ/m6lkw5yvXQQdLHtQFc76KSc/5EzTXWx3jnDhVDf/jFw9+n8BlE8JSTiTHn5mhnnry852FQ+IdNuj3x+DXX4/O3hqLF0fW82munhfm7IjK3hqLF26Lyt4qNTXOENZgysriC3H5faSZ13qGZ2S24/cKv0/gGj8+OntrjIkw8Wv8eNi1K9RWV9e2XD1791JDL5ZQRA294OBB2Lkz9Lxt22L+G0w+aWNU9tYYc3j4Fd8i2VslUTFvrxo36aJneII5/rbg9wlcJ5/c3MmPH+/YY6FXL2jfZFJ3+/bQo0fzORFtmSORn095yRv0p4pxLKQ/VZSf+0T4c9esianIE477N8NZDmjDNpzlTDju3zHpDelYRSkPhuiV8iBDOsbo+BM14GDuXJg0ydl7wcsvwzXXOHsvePdduP12Z+9HvYoKmD3b2WeDXj3h4j9+23yTj9+rPoNE6b3zjuq0abHH9uuJNPxy1izVnJxQe05Oq8Myw4b42+/Rano170eYNi22Mk+bpgq6gLO1hEd1AWd7oreawTqLK3Q1g+PTU1Xt3j20rj16xK6lqtqnT6he377x6SVrgIBf9Lzuc/GhHsnu3AX+AFQDK4Ns04EvgeXudm5btHzj+LOFSJ2xc+Y0d9SgumBBi3Jhf0cCe3QxRc215syJrcyRymZ6bWPBgpg+24h4PeAgWQMYYu2996leJMefyFDPLODsMPb7VbXQ3f6cwPsbsRIpFBUpDLNkSYtyYaMcB9pRQGXzk/fvj6nIEa/zi97TT0dnT7beCy9EZ28NrwccJGsAQyR7uus1IWGOX1X/CnyVKH0jwRQXwxtvwI9+5OyLi2PuRG74Hck7SJfAXgJ5Bym749/ks6n5yZE6llsj0nV+0bv44ujsrXF2uDZVC/bWmDw5OntreD3gIFkDGPzyffFarynhHgO82oACmod6KoEVOKGg7i1cey2wFFjar1+/6B6TjPiJFF8cPz7U3tY4a2mpVtNLF1PkxPZLS30ZE02oXt++oXrxxOQXLw4fCognDcbw4aFa8cb4Y/2uJEvP79+XdIzxa3jHfziQg/OkcQfwh7boWIw/ybQWX5wzR3XixLbHk1vSW7BAtaQk9lhyU/yud9ddqoWFzj4eEjUz9pFHVE891dl7QbTflWTr+f37EueADV84/ra+13Qzx59kZs0K76hnzYqtFRJJ76yzfNdCSis9G+WS2XoepDTxheMHege9vhF4qi065viTTKQWeqSRH62NNIik58NREKZner7Q8+iJLpLjT1jnroiUA+8Bg0VknYiUADNF5GMRWQGc4Tp/w28MGQKlpaG20lKorQ1/fmsjDcLpnXVWbFqR8PuoCtMzvWhIcHbThK25q6rhEnOUhbEZfuShh+C665wv7pgxjvOONHuwLSMNTjoJfv97Z6avqjP65M03Y9OKpgymZ3rpqJeomd71hHsM8NtmoR6fUF2t2q5d6ONnu3axZ+csKQm1xRMTjbVspteI30fh+F3Ph9lXycrsnIa3VFbCoYeG2jp3ji07Z24ufO97sHo1zJrl7B96KPllMz2Hmhp4++1Q29tvx549NNv0wPn+evV9hoRmN01YqMfIQGJ9/Gzpuvx8J4yUqrKZnkP9j3NdXaOtPqYcS/LAbNOrZ8gQb77P9eTnJySzqbX4jbYTa1ZRr7ORJuMe2abn9x8mv+ulG+HiP37bLMbvM2LNKup1NtJk3COb9Py+Apff9XwIEWL84rznb4qKinTp0qWpLoZhZD41NU64oz4MZ3ppjYgsU9WipnaL8RtGS2S4Y2iG1zHlbNNLEyzGbxiRKC+H/v1h3DhnX16e6hIZhieY4zeMcNTUQEmJM+pj61ZnX1IS33A/w/AJ5vgNIxwJnjJvGKnEHL9hhCPbh/sZGY05fsMIR6LmHlRUwOzZkfMepVqvpsZZStNCWhmNOX7DiITXU+ZvuAGGDoWrrnL2N9zgLz3rzM4abBy/YSSDigrHOTdl9erYpvh7rVdT4zj74BQGgYDzg5eFwx0zhUjj+K3FbxjJIMvzvxv+IpELsfxBRKpFZGWQrYeIvCEin7n77om6v2H4imzP/274ikS2+GcBZzex3Qq8qapHA2+6x4aR+URa1SzWTI5e6yUjkZ7hGxIa4xeRAuBlVR3mHn8KnK6qG0SkN/CWqg5uTcdi/EbGUFERuqqZ3/SyLUVFhhMpxp9sx79FVbsFvb9ZVcOGe0TkWuBagH79+o2qqqpKWDkNwzAykbTr3FXVx1S1SFWL8q3lYRiG4RnJdvwb3RAP7r46yfc3DMPIepLt+F8CrnRfXwm8mOT7G4ZhZD2JHM5ZDrwHDBaRdSJSAtwJjBORz4Bx7rFhGIaRRBK2EIuqRprfflai7mkYhmG0TlqkbBCRGiCRw3p6AZsSqO8HMr2OmV4/sDpmCsmsY39VbTY6Ji0cf6IRkaXhhjxlEplex0yvH1gdMwU/1NG3wzkNwzCMxGCO3zAMI8swx+/wWKoLkAQyvY6ZXj+wOmYKKa+jxfgNwzCyDGvxG4ZhZBnm+A3DMLKMjHf8ItJXRBaJSIWIrBKRqU3ev0lEVER6Bdl+KiKfi8inIvKt5Jc6Olqqo4jc4NZjlYjMDLJnRB1FpFBE/i4iy0VkqYiMCbom3eqYJyKLReQjt47/5dojLmCUTnVsoX53i8gnIrJCRJ4XkW5B16RN/SByHYPe94e/UdWM3oDewPHu60OBNcBQ97gv8BrO5LBerm0o8BFwCDAA+ALISXU9YqkjcAawEDjEfe+wDKzj68A5rv1cnDUe0rWOAnR2X+cC7wPfAGYCt7r2W4G70rGOLdRvPNDetd+VrvVrqY7usW/8Tca3+FV1g6p+4L7eDlQAR7pv3w/cAgT3cE8CnlLVPar6T+BzIMb17JJDC3X8AXCnqu5x36vPhppJdVSgi3taV2C9+zod66iqusM9zHU3xanLbNc+G5jsvk6rOkaqn6q+rqr7XfvfgT7u67SqH7T4GYKP/E3GO/5g3IVhjgPeF5GJwJeq+lGT044E/hV0vI7GHwrfE1xH4BjgVBF5X0T+IiKj3dMyqY4/Au4WkX8B9wA/dU9LyzqKSI6ILMdJWf6Gqr4PHK6qG8D5AQQOc09PuzpGqF8wVwOvuK/Trn4Qvo5+8zdZ4/hFpDPwHI6j2A/8HJgW7tQwtrQY8xpcR1XdhpOErzvO4/TNwNMiImRWHX8A3KiqfYEbgbL6U8Nc7vs6quoBVS3EafWOEZFhLZyednVsqX4i8nOc/8259aZwEgkvZJyEqeMIfOZvssLxi0gujrOYq6p/BAbixNM+EpFKnA/oAxE5AucXt2/Q5X1oDB/4ljB1BKcuf3QfPxcDB3ESRGVSHa8E6l8/Q+NjclrWsR5V3QK8BZxN5AWM0raOTeqHiFwJTACmqBv8Jo3rByF1nITf/E0qO0KSseH8oj4BPNDCOZU0drYcS2hnyz9Ijw6lZnUEvg/80n19DM4jpWRYHSuA093XZwHL0vhzzAe6ua8DwNs4zvBuQjt3Z6ZjHVuo39nAaiC/yflpVb+W6tjknJT7m4Tl4/cRJwOXAx+7cTeAn6nqn8OdrKqrRORpnC/ifuB6VT2QlJLGTtg6An8A/iAiK4G9wJXqfNsyqY7/AfxGRNoDu4FrIW0/x97AbBHJwXkaf1pVXxaR93DCdCXAWuA7kJZ1jFS/z3Ec3xtOJJK/q+r307B+EKGOkU5OVR0tZYNhGEaWkRUxfsMwDKMRc/yGYRhZhjl+wzCMLMMcv2EYRpZhjt8wDCPLMMdvZCwicsDN2rnKzZb4YxGJ+TsvIqe4mRc/cbdrg97Ld1NjfChOFtEfBL13gpt5MhuGTxtpgH0RjUymTp2p84jIYcA8nERut0cr5M6ynAdMVtUP3LS6r4nIl6r6J5zJY5+o6pUicjjwnog8C9QCDwPXaWMismjvLThDrw/Gcr1hNMXG8RsZi4jsUNXOQcdHAUtw0lb0B54EOrlvl6rq30TkSeBZVX3RvWYuMB8YjZN8cVqQ3lnAdOAG4CWcmZpfAicC/8+9ZgkwCmdi2Z3A6TiTlX6rqo+6uYdexMmplAv8QlVfdBPRvQIscvUmq2qVl38fI3sxx29kLE0dv2vbDHwd2A4cVNXdInI0UK6qRSLyTZykb5NFpCuwHDgaeBqYXf+D4Gp1Bf6pqj1E5CqgSFVL3ffaAe/hZNIsAi7EWQ9hhogcAryLMwP3X0BHVd3mPkX83b1ff5zp+yep6t8T8gcyshYL9RjZRn02xFzgYREpBA7g5DJCVf8iIr91Q0MXAM+p6n433BKulRS25aSqB0XkUZwfg1oRGQ+MEJGL3FO64jj4dcCvReQ0nCR6RwKHu+dUmdM3EoE5fiNrcEM9B3CyW94ObARG4gxy2B106pPAFOASnPzwAKtwWu4vBZ03CifHSiQOuhs4Pzg3qOprTcp0FU5ir1Gqus/N3pjnvr2z7bUzjLZjo3qMrEBE8oH/AR52E9V1BTa4HaaXAzlBp8/CWbcBVV3l2n4LXOU+ISAiPXGWCZxJ23gN+IGbWhoROUZEOrnlqHad/hk4IR7DSCjW4jcymYCbyTMXJ/Phk8B97nuPAM+JyHdwOlAbWtequlFEKoAXgmwbROQy4HERORSnBf+Aqi5oY1l+DxTg5GEXoAZnCcW5wAIRWYrTn/BJLBU1jGiwzl3DaIKIdAQ+xlncfWuqy2MYXmOhHsMIQkTG4rS6HzKnb2Qq1uI3DMPIMqzFbxiGkWWY4zcMw8gyzPEbhmFkGeb4DcMwsgxz/IZhGFnG/wfSVo6szTyIxgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax=None\n", "colors = ['red','blue','green','yellow']\n", "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n", " ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.2669192282197318\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n", "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n", "pie_pumpkins.plot.scatter('DayOfYear','Price')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### লিনিয়ার রিগ্রেশন\n", "\n", "আমরা স্কিকিট লার্ন ব্যবহার করে লিনিয়ার রিগ্রেশন মডেল প্রশিক্ষণ করব:\n" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean error: 2.77 (17.2%)\n" ] } ], "source": [ "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n", "y = pie_pumpkins['Price']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", "lin_reg = LinearRegression()\n", "lin_reg.fit(X_train,y_train)\n", "\n", "pred = lin_reg.predict(X_test)\n", "\n", "mse = np.sqrt(mean_squared_error(y_test,pred))\n", "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n" ] }, { "cell_type": "code", "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X_test,y_test)\n", "plt.plot(X_test,pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "রেখার ঢাল লিনিয়ার রিগ্রেশন সহগ থেকে নির্ধারণ করা যেতে পারে:\n" ] }, { "cell_type": "code", "execution_count": 178, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([-0.01751876]), 21.133734359909326)" ] }, "execution_count": 178, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lin_reg.coef_, lin_reg.intercept_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "আমরা প্রশিক্ষিত মডেলটি ব্যবহার করে মূল্য পূর্বাভাস দিতে পারি:\n" ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([16.64893156])" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pumpkin price on programmer's day\n", "\n", "lin_reg.predict([[256]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### পলিনোমিয়াল রিগ্রেশন\n", "\n", "কখনও কখনও ফিচার এবং ফলাফলের মধ্যে সম্পর্ক স্বাভাবিকভাবেই অ-রৈখিক হয়। উদাহরণস্বরূপ, কুমড়ার দাম শীতকালে (মাস=১,২) বেশি হতে পারে, তারপর গ্রীষ্মে (মাস=৫-৭) কমে যেতে পারে, এবং পরে আবার বাড়তে পারে। লিনিয়ার রিগ্রেশন এই সম্পর্কটি সঠিকভাবে খুঁজে পেতে অক্ষম।\n", "\n", "এই ক্ষেত্রে, আমরা অতিরিক্ত ফিচার যোগ করার কথা বিবেচনা করতে পারি। সহজ উপায় হল ইনপুট ফিচার থেকে পলিনোমিয়াল ব্যবহার করা, যা **পলিনোমিয়াল রিগ্রেশন** তৈরি করবে। সাইকিট লার্ন-এ, আমরা পাইপলাইনের মাধ্যমে স্বয়ংক্রিয়ভাবে পলিনোমিয়াল ফিচার প্রি-কম্পিউট করতে পারি:\n" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean error: 2.73 (17.0%)\n", "Model determination: 0.07639977655280217\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 180, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.pipeline import make_pipeline\n", "\n", "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n", "\n", "pipeline.fit(X_train,y_train)\n", "\n", "pred = pipeline.predict(X_test)\n", "\n", "mse = np.sqrt(mean_squared_error(y_test,pred))\n", "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n", "\n", "score = pipeline.score(X_train,y_train)\n", "print('Model determination: ', score)\n", "\n", "plt.scatter(X_test,y_test)\n", "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### এনকোডিং প্রকারভেদ\n", "\n", "আদর্শ পরিস্থিতিতে, আমরা চাই একই মডেল ব্যবহার করে বিভিন্ন কুমড়ার প্রকারভেদের দাম পূর্বাভাস দিতে সক্ষম হতে। প্রকারভেদকে বিবেচনায় আনতে, প্রথমে আমাদের এটিকে সংখ্যায় রূপান্তর করতে হবে, বা **এনকোড** করতে হবে। এটি করার কয়েকটি পদ্ধতি রয়েছে:\n", "\n", "* সাধারণ সংখ্যাগত এনকোডিং, যা বিভিন্ন প্রকারভেদের একটি টেবিল তৈরি করবে এবং তারপর সেই টেবিলের সূচকের মাধ্যমে প্রকারভেদের নাম প্রতিস্থাপন করবে। এটি লিনিয়ার রিগ্রেশনের জন্য সেরা পদ্ধতি নয়, কারণ লিনিয়ার রিগ্রেশন সূচকের সংখ্যাগত মানকে বিবেচনায় নেয়, এবং সংখ্যাগত মান সম্ভবত দামের সাথে সংখ্যাগতভাবে সম্পর্কিত নয়।\n", "* ওয়ান-হট এনকোডিং, যা `Variety` কলামকে ৪টি ভিন্ন কলামে প্রতিস্থাপন করবে, প্রতিটি প্রকারভেদের জন্য একটি কলাম থাকবে, যেখানে সংশ্লিষ্ট সারি যদি নির্দিষ্ট প্রকারভেদের হয়, তাহলে ১ থাকবে, অন্যথায় ০ থাকবে।\n", "\n", "নিচের কোডটি দেখায় কীভাবে আমরা একটি প্রকারভেদকে ওয়ান-হট এনকোড করতে পারি:\n" ] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " FAIRYTALE MINIATURE MIXED HEIRLOOM VARIETIES PIE TYPE\n", "70 0 0 0 1\n", "71 0 0 0 1\n", "72 0 0 0 1\n", "73 0 0 0 1\n", "74 0 0 0 1\n", "... ... ... ... ...\n", "1738 0 1 0 0\n", "1739 0 1 0 0\n", "1740 0 1 0 0\n", "1741 0 1 0 0\n", "1742 0 1 0 0\n", "\n", "[415 rows x 4 columns]" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.get_dummies(new_pumpkins['Variety'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### বিভিন্নতার উপর লিনিয়ার রিগ্রেশন\n", "\n", "এখন আমরা উপরের একই কোড ব্যবহার করব, তবে `DayOfYear` এর পরিবর্তে ইনপুট হিসেবে আমাদের এক-হট-এনকোড করা বিভিন্নতা ব্যবহার করব:\n" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "X = pd.get_dummies(new_pumpkins['Variety'])\n", "y = new_pumpkins['Price']" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean error: 5.24 (19.7%)\n", "Model determination: 0.774085281105197\n" ] } ], "source": [ "def run_linear_regression(X,y):\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", " lin_reg = LinearRegression()\n", " lin_reg.fit(X_train,y_train)\n", "\n", " pred = lin_reg.predict(X_test)\n", "\n", " mse = np.sqrt(mean_squared_error(y_test,pred))\n", " print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n", "\n", " score = lin_reg.score(X_train,y_train)\n", " print('Model determination: ', score)\n", "\n", "run_linear_regression(X,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "আমরা একই পদ্ধতিতে অন্যান্য বৈশিষ্ট্য ব্যবহার করার চেষ্টা করতে পারি এবং সেগুলিকে সংখ্যাগত বৈশিষ্ট্যের সাথে যেমন `Month` বা `DayOfYear` এর সাথে সংযুক্ত করতে পারি:\n" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean error: 2.84 (10.5%)\n", "Model determination: 0.9401096672643048\n" ] } ], "source": [ "X = pd.get_dummies(new_pumpkins['Variety']) \\\n", " .join(new_pumpkins['Month']) \\\n", " .join(pd.get_dummies(new_pumpkins['City'])) \\\n", " .join(pd.get_dummies(new_pumpkins['Package']))\n", "y = new_pumpkins['Price']\n", "\n", "run_linear_regression(X,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### পলিনোমিয়াল রিগ্রেশন\n", "\n", "পলিনোমিয়াল রিগ্রেশন এক-হট-এনকোড করা ক্যাটেগরিকাল বৈশিষ্ট্যগুলোর সাথেও ব্যবহার করা যেতে পারে। পলিনোমিয়াল রিগ্রেশন প্রশিক্ষণের কোড মূলত উপরে দেখা কোডের মতোই হবে।\n" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean error: 2.23 (8.25%)\n", "Model determination: 0.9652870784724543\n" ] } ], "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.pipeline import make_pipeline\n", "\n", "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", "\n", "pipeline.fit(X_train,y_train)\n", "\n", "pred = pipeline.predict(X_test)\n", "\n", "mse = np.sqrt(mean_squared_error(y_test,pred))\n", "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n", "\n", "score = pipeline.score(X_train,y_train)\n", "print('Model determination: ', score)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**অস্বীকৃতি**: \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসম্ভব সঠিকতার জন্য চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। মূল ভাষায় থাকা নথিটিকে প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ সুপারিশ করা হয়। এই অনুবাদ ব্যবহারের ফলে কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যা হলে আমরা দায়বদ্ধ থাকব না।\n" ] } ], "metadata": { "interpreter": { "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" }, "kernelspec": { "display_name": "Python 3.7.0 64-bit ('3.7')", "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": "3.9.5" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } }, "orig_nbformat": 2, "coopTranslator": { "original_hash": "d77bd89ae7e79780c68c58bab91f13f8", "translation_date": "2025-08-29T22:46:07+00:00", "source_file": "2-Regression/3-Linear/solution/notebook.ipynb", "language_code": "bn" } }, "nbformat": 4, "nbformat_minor": 2 }