Predict_House_PricesPart_1.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GAdevopsES/Python100/blob/master/Predict_House_PricesPart_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cxeXz0L6OyKB"
      },
      "outputs": [],
      "source": [
        "# House Sales Data, ETL, Build Predictive Model(linear regression), \n",
        "# Assess Model, Deploy Model   'houseSmallData.csv'\n",
        "# R^2 = 0.75  Percentage of the Price explained by the indep. variables\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjGQiPhcOyKF"
      },
      "source": [
        "### data_description.txt\n",
        "SalePrice — the property’s sale price in dollars. This is the target variable that you’re trying to predict.  \n",
        "MSSubClass — The building class  \n",
        "MSZoning — The general zoning classification \n",
        "MasVnrArea - Masonary Veneer Area in sq ft\n",
        "LotFrontage — Linear feet of street connected to property  \n",
        "LotArea — Lot size in square feet  \n",
        "Street — Type of road access  \n",
        "Alley — Type of alley access  \n",
        "LotShape — General shape of property  \n",
        "LandContour — Flatness of the property  \n",
        "Utilities — Type of utilities available  \n",
        "LotConfig — Lot configuration  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EL4K43PsOyKH"
      },
      "source": [
        "$$Y = X\\beta + e $$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z-zXU8hhOyKI",
        "outputId": "7a0eb821-8a7f-4e42-a31c-dd236b07d33b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(100, 82)"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv('houseSmallData.csv')\n",
        "train = data.iloc[0:100,:]\n",
        "train.shape\n",
        "train\n",
        "train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_tpF2Wv_OyKJ",
        "outputId": "0b92610d-6ea3-4100-89f9-94b4febcfaf5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 2., 17., 35., 17., 11.,  8.,  5.,  3.,  1.,  1.]),\n",
              " array([ 40000.,  79878., 119756., 159634., 199512., 239390., 279268.,\n",
              "        319146., 359024., 398902., 438780.]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "salePrice = train['SalePrice']\n",
        "salePrice.describe()\n",
        "plt.hist(salePrice)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9a3QFJ_5OyKK",
        "outputId": "bec3e22a-de54-46e1-bd28-d0076bdb95ef"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(np.log(salePrice))\n",
        "np.log(salePrice).skew()\n",
        "target = np.log(salePrice)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0bpcIwimOyKL",
        "outputId": "1bf69da4-056f-4109-ada2-e051dc012df0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe53bd755b0>"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD4CAYAAADrRI2NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdOUlEQVR4nO3df4wc533f8feXp5V0VAwdFTKGeZJMOhCo6IdNOgfFgYzEkhuSZh2Lke3KioEqtQPCbV20QcuAggqLcRKYDZE4SGAkZR1BaavIsi3pqlpuKCZSIEANHd/1SIm0SItWbIsr1zyXOsUOz/aR/PaPnSX39mZ2Z+dmdn59XsCBd3P7Y55b6TvPfJ/v8zzm7oiISH2syPsERERkuBT4RURqRoFfRKRmFPhFRGpGgV9EpGYuyfsEwqxevdrXrVuX92mIiJTG9PT099x9TZzHFjLwr1u3jqmpqbxPQ0SkNMzsW3Efq1SPiEjNKPCLiNSMAr+ISM0o8IuI1EyswG9mD5jZKTM70nHst83seTM7ZGZPmdnaiOfeY2YvBV/3pHXiIiKSTNwe/4PA1q5je939re6+EfgS8InuJ5nZVcD9wM8BtwD3m9mqxGcrIlJBkzNNbt3zNOt3Pcmte55mcqaZ6fvFCvzu/ixwuuvYP3T8eAUQtsznFuCAu59299eAAyy9gIiI1NbkTJN7H3uB5tw8DjTn5rn3sRcyDf7LyvGb2e+a2SvAhwnp8QPjwCsdP58MjomICLB3/3HmF84tOja/cI69+49n9p7LCvzufp+7XwM8BHx8Oa9lZjvMbMrMpmZnZ5fzUiIipfHq3PxAx9OQVlXPQ8D7Q443gWs6fr46OLaEu+9z9wl3n1izJtasYxGR0ls7NjrQ8TQkDvxmdl3Hj3cAx0Ieth/YbGargkHdzcExEREBdm7ZwGhjZNGx0cYIO7dsyOw9Y63VY2YPA+8CVpvZSVqVOtvMbANwHvgW8LHgsRPAx9z91939tJn9NvDV4KU+6e6nl7yBiEhNbd/UGvbcu/84r87Ns3ZslJ1bNlw4ngUr4p67ExMTrkXaRETiM7Npd5+I81jN3BURqRkFfhGRmlHgFxGpmUJuxCJSFZMzzaEO2onEocAvkpH2VPz2rMz2VHxAwV9ypVSPSEbymIovEocCv0hG8piKLxKHAr9IRvKYii8ShwK/SEbymIovEocGd0UyksdUfJE4FPhFMrR907gCvRSOUj0iIjWjHr+IpEYT1spBgV9EUqEJa+WhVI+IpEIT1spDgV9EUqEJa+WhwC8iqdCEtfJQ4BeRVPSbsDY50+TWPU+zfteT3LrnaSZnmnmcpqDBXZGBqGolWq8Jaxr4LRYFfpGYFLz6i5qw1mvgN+pvp4tsdpTqEYlJVSvJDTrw277INufmcS5eZJUeSod6/CIxqWolubVjozRD/k7dA7/tXn7YY/vdIUh8CvwiXSZnmux+4ihz8wsArFrZ4P5fvjF28JKldm7ZsChNBktXKu1OpYXRRTYdSvWIdJicabLzC4cvBH2A184ssPOLh7nt+jVaZjmh7ZvG+dSdNzM+NooB42OjfOrOmxf13sNSad10kU2HevwiHfbuP87CeV9yfOGc88yxWT51580acEyo30ql/XrzusimR4FfpEOv4PPq3LyWWc5QVCoNWncIusimR6kekQ69UglKM2QragLYH961ked23a6gnyIFfpEOO7dsoLHClhxvjJjSDBmLMw4g6VCqR6RDO8iEVfUoAGVPqbThUOCXUhnGbE4FH6k6BX4pjThLJmiav0h/yvFLafRbMkHT/EXiUeCX0ui3ZILW0hGJp2/gN7MHzOyUmR3pOLbXzI6Z2fNm9riZjUU895tm9oKZHTKzqRTPW2qo30YfWktHJJ44Pf4Hga1dxw4AN7n7W4GvA/f2eP5t7r7R3SeSnaJIS7+NPrQDlEg8fQO/uz8LnO469pS7nw1+PAhcncG5iSzSr86734VBike7cuUjjaqejwCPRPzOgafMzIH/7O77ol7EzHYAOwCuvfbaFE5LqqhXqWWvHaCkeLSxTX7MfemCVEseZLYO+JK739R1/D5gArjTQ17IzMbdvWlmP0UrPfRvgjuIniYmJnxqSkMCkg6VeBbTrXueDl2bZ3xslOd23Z7DGZWbmU3HTaknruoxs18D3gt8OCzoA7h7M/j3FPA4cEvS9xNJQiWexaXB+PwkSvWY2VbgN4FfdPczEY+5Aljh7t8Pvt8MfDLxmYok0G+vV90NLDbMv4c2tslP38BvZg8D7wJWm9lJ4H5aVTyXAQfMDOCgu3/MzNYCn3X3bcAbgceD318C/IW7/2UmrRDp0Bm8ohKZr87NK8fcZdh/jzi7ckk2YuX4h005fkkqzvZ90MojA8oxd4jKuY+Ycd49kzsA3XGlZ5Acv9bqkUqJs31fu1f5G48cCv19HXLMYQE3qt3ngs5hFncAWhAvH1qyQSqlV9Durv2v64SvqAHvK0cbfZ+rJTCqQT1+qZSoAcOw9E1dc8xRA96XN1Yw2hjpe8dUhzuiqlOPXyplkNm7dd3xKSpwz51ZWPT3GLGlO5FB9e+I6kA9fqmUQWfvppVjLtMgZa8yys6/R9hAeR3uiOpAgV9KYZDAOuwBw7KVhcZNccW5iJbpgicXqZxTCi+q51mUtMymTz7Fa2cWlhwvclloGgG76J9L3aicUyql3+zbPE3ONEODPgxnEDRpAE/jrqjIn4v0psAvhVfkNV16lTZmPQiad4qpyJ+L9KaqHim8Itfb9wpyWQ+C5r3VZJE/F+lNgV8KbzkbrGS90UdUkBsbbWTe6867x62Nb8pLgV8KL2m9/TCWZI4Kfrvfd2Nq7xEl7x53XedBVIGqeqSyohYdW7WywcwnNvd9ftyB07xKGlVVI52GshGLSNFFpTxeO7PQt9dfhg1c1OOWpFTVI0NVhI0+gL4lh3FLFfOurNHqlpKEevwyNMPuRfcaZOw3ABp34DTvyppesh7YlvJS4JehGXaQ3L5pnLGIpYb7DYDGHTjNu7ImShlSVZIfBX4ZmjyC5O733Zio5DCsWseA265fs+hY3pU1UYp8JyL5U+CXockjSCYdAN2+aZz3/+w4nQsTO/DodHNRr7motey9LrJKAYkGd2Vo8tr4JOkA6DPHZpds1t49wDvoMtDDEjWwfeVoo1QriUo2FPhlaIoaJKPETU0VsbIm6iJrhhZWEwV+Ga4iBskovTYsKbqoi2ydN5iXixT4pfDymhm7c8sGdn7hMAvnLyZ8Giss9/x9XGEX2b37j5f2Yibp0eCuJDaMQcLcyxK7t50N34a2NIo6GC3DpR6/xNbZ8x5b2eAHPzx7oTecxiBhWM8+z80+9u4/zsK5xcO7C+e81PnwuOMs2lKx2hT4JZbupQnCdp1aTkCOWvqgO+i3DSMnXdTJWcvVb5wl72UoJHtK9UgsYT3vMEmDYlTPfkVEamVsZfiM3DQVdXJW1jT5q/oU+CWWuAE9aVCMev3zEauGR60mnua4Q9Ts3ebcfKUnPlX1TkcuUqpHYum10mVb9yDhIHniOK/f6fX5pammtFMUnfnw5tw8BhcmdFU5/VHmMlaJRz1+iSWs99sYMcZGG6FLIQxajRNVbRK1yNqVIcezSFFs3zTOc7tuZ3xsNHIWb9Wo8qf61OOXWAaddTtoNU7U6wNLaukB/vHHZ5mcaS56rSxTFHVKf5RthrUMToFfYhm0vC9JoIyqNvmt/3l0SRVRWFllmimK7vaOrWyEVjJVNf1RphnWMri+qR4ze8DMTpnZkY5je83smJk9b2aPm9lYxHO3mtlxMzthZrtSPO/ayXNFxSSTqNKsiJkLCbiw9CKSVooirL0/+OFZGiOLS4yU/pCyipPjfxDY2nXsAHCTu78V+Dpwb/eTzGwE+AzwHuAG4G4zu2FZZ1tTec9eTZI7TzNPHPciktYetGHtXTjvXHHpJdrfViqhb6rH3Z81s3Vdx57q+PEg8IGQp94CnHD3lwHM7HPAHcDXEp9tTaU5ezXJjMykaZupb53m4a+8wjl3Rsx4/88mSx8MspxzGimKqHa9Pr/Aofs3L+u1RYogjRz/R4BHQo6PA690/HwS+LmoFzGzHcAOgGuvvTaF06qOtAYWe5U7QvRgXlTufIUZ63c9GXoBmZxp8uh0k3NBwf05dx6dbjLx5qsGDszDHmxUOaNU3bICv5ndB5wFHlruibj7PmAfwMTERMT0nHpKKxBF3TnsfuIoPzp7PrL+PazHDVwI6mE17WmvsTPMwca8NowRGZbEgd/Mfg14L/Bu99B5lE3gmo6frw6OyYCSBKKwlE7UHcJcyGSoziDd3eNeYXYh6Hc+fvcTRy88JurKPcgkrbyonFGqzsJjdteDWjn+L7n7TcHPW4E/AH7R3WcjnnMJrYHfd9MK+F8FftXdj/Z7v4mJCZ+amorbhloYJDffndKB1oXisktWhAb5KAb8/Z5/uuT4+l1PRgb2OK/56bs2KoiKpMzMpt19Is5j+/b4zexh4F3AajM7CdxPq4rnMuCAmQEcdPePmdla4LPuvs3dz5rZx4H9wAjwQJygL+HCUh1RF4OoNMvljRWMNkaWXBAub6yIVaPefr/l5OEcSr2ssUgVxKnquTvk8J9FPPZVYFvHz18Gvpz47CRSr4HayJTOmQU+fdfG0Nmx/VJJYXcRSVVxtqtImWjmbkn1GjztNRjca5C0Vyqp17LM42OjnPnx2dC7hjCqjhHJlwJ/jrpTNbddv4Znjs3GyuP3KvH89F0bQ3vnzbl51u16krHRBrvfd+Oi1+5XNRP1fgY8t+v20DuCxgoDY9EuVqqOEcmfVufMSdhs3P9+8NuxZ+f2ms3aOYM1zNz8Aju/cHigmb/9Zs+GzZrd+8G3sfcDb7twbNXKBpddsoLfeORQpdezFym6WFU9w1aHqp5b9zwdq7RxfGyU53bdvuR4VOVO9zICvd6n/dpxKobivl+U5T5fRHpLtapHshF3gDPqcXFrzXu9T3Nunv84+QKPTjf7bl6ynNr2yZkm//7zh0Nr/4tc4TM501y0MmhYikykjBT4cxJ3x6leA6FxZrP2e5+HDn47coOR7tdOMnu23dPvDvptRa3wmZxpsvOLhxeNT7RTZFC9XbekXpTjz0nY6pXdRhsj3Hb9mmUtx7xzy4Ylywl3ikr0pRWQ+23SXtQKn737jy8K+m0L51v7AOS5TLbIcqnHn5Pu/VxHgmUQ2v+OB1U+cdIwcd4nbDOTXtIKyL0uIEWu8OmXIktzb98yS7Laq+RPPf4h6+wp7t1/nNuuX8NoY2TRKpbtgPjMsdlU9pDdvmmcmU9sjqzy6b4fSDMgR11ARswKPbDb68I3Ypb63r5llPc+EZKcAv8Qhf2P8tDBb0eumBmVm0+ahonaHOXD77g2sw1Got7z9//Z2wob9CE6RdZYsXSBuraijldkJYvN7WU4lOoZot1PHF3yP0pUjr3XYmpRvdHO2+4rRxuYtZZp6L4FT1qZk+R5YSmtzuBQ1OAfliJrV/W029JtbGVjqOeYtzptQF81CvxDMjnTHGhlzChRaZjuOvnO9+rOQSetzEma024/pmx58V5/q+6KH4Af/PAskzPNwrYnbdqwpryU6hmSXre/0TU3S0VtX9ivemY5t+Bxbun7VblUKS2wfdM4V1y6tM/UrvipizT3VZbhUuBPSb/A1+v2tzvHvqpHyuDR6Wbo4Fmc2+ukm6D0u6WPM8hXtbTA6xF3b2VtTxJpbW4vw6dUTwripEKibotXrWzwO9tv7vl6nbp3umrn2+NMCBuxQe4tLup3Sx9nm8WqpQWq1p6khrklpqRHPf4UxEljRN0W3//LNy55vXZPKsrc/MKS3nW7LLSXqGqUfvrd0sfpzVctLVC19ki9KPCnIE7gG/S2ePum8ci6+27zC+d45thszxU5Cd4ziX7n3m/lzjivUTZVa4/Ui1bnTEHUCphRK2vGNciuV5374w57JUytvCmSv0FW51SPPwVZ3faH9SqjBn6jetfAotr5LGZVqvcrUi7q8adkWGuWDNK7Vk9cpD60Hn8OhlXdMMjs2zjVNt206JZI9Snwl1Dci8ygtfPLnaGbNl2ERLKhHH+Fxam26VSk2bVa+VEkOwr8Gcp7s45BB53jzNAdVnuKdBESqRqlejKSR9qknRrp3Nhl1coGl12ygtfnl67S2a3XbNRht6dqSzyIFIl6/BkZdo+1MzUCF2fpvnZmgR+dPc+n79rIc7tu7xmke90hDLs9g6ap+sn77kukSBT4MzLsHmuv1TnjBuhe9fjDbk+acyM0XiCymFI9GRn2Il79AnDcAB1VMTTs9ixn05huScpaRapMgT8jO7dsCJ08ldUiXv1W51xugB52eyC9uREaLxBZTKmejAx7GYOw1EhbVstHlGUGcNrjBSJlpyUbKiSsqme8K0VSx0lRWrpC6kBLNtRUv9RI0WbmDkua4wUiVdA38JvZA8B7gVPuflNw7IPAbuBngFvcPbR7bmbfBL4PnAPOxr0aSTbSGOQs6x2DdooSuShOjv9BYGvXsSPAncCzMZ5/m7tvVNDP33IHOVUWKVINfQO/uz8LnO469qK7a+58ySx3kFPLKIhUQ9ZVPQ48ZWbTZraj1wPNbIeZTZnZ1OzsbManVU/LnRSlskiRash6cPed7t40s58CDpjZseAOYgl33wfsg1ZVT8bnVSpp5dWXO8g57ElcIpKNTAO/uzeDf0+Z2ePALcQbF5BA2pU4yxnkzGMSl4ikL7NUj5ldYWZvaH8PbKY1KCwDKFJevcyTuETkojjlnA8D7wJWm9lJ4H5ag71/DKwBnjSzQ+6+xczWAp91923AG4HHzaz9Pn/h7n+ZTTOqKyp/3pybZ/2uJ4deUqmySJHy6xv43f3uiF89HvLYV4FtwfcvA29b1tlJzzV4OksqIf9JWGWt8RepG63VU3C91uBpK0JJpWr8RcpDgb/guvPqUfIuqSzSWISI9Ka1ekqgM69+656nC1lSqRp/kfJQj79k0tyZKk1a+likPBT4S6aoJZV5XZC0l67I4JTqKaEillTmsfRxXZeZFlkuBf4hqEuZ47AvSNpLVyQZBf6MlaFXWtYLkwaURZJRjj9jRS9zLHP9vQaURZJR4E8o7qBi0XulRb8w9VLUCieRolOqJ4FB0jdFX8q46BemXrSXrkgyCvwJDDKoWPSljIt+YeqniBVOIkWnVE8Cg/SSi1p336Z0iUj9qMefwKC95CL3SpUuEakfBf4Eip6+GVSRL0wikj4F/gTUSxaRMlPgT0i9ZBEpKw3uiojUjAK/iEjNKPCLiNSMAr+ISM0o8IuI1ExlqnrKurSwiMiwVSLwl2HNexGRoqhEqqfMSwuLiAxbJQJ/mZcWFhEZtkoEfu3EJCISXyUCv5YWFhGJrxKDu1o0TUQkvkoEftCiaSIicVUi1SMiIvEp8IuI1EzfwG9mD5jZKTM70nHsg2Z21MzOm9lEj+duNbPjZnbCzHalddIiIpJcnB7/g8DWrmNHgDuBZ6OeZGYjwGeA9wA3AHeb2Q3JTlNERNLSN/C7+7PA6a5jL7p7v2mxtwAn3P1ld/8x8DngjsRnKiIiqcgyxz8OvNLx88ngmIiI5Kgwg7tmtsPMpsxsanZ2Nu/TERGprCwDfxO4puPnq4Njodx9n7tPuPvEmjVrMjwtEZF6yzLwfxW4zszWm9mlwIeAJzJ8PxERiSFOOefDwN8CG8zspJl91Mx+xcxOAj8PPGlm+4PHrjWzLwO4+1ng48B+4EXg8+5+NKuGiIhIPObueZ/DEhMTEz41NZX3aYiIlIaZTbt75LyqToUZ3BURkeFQ4BcRqRkFfhGRmlHgFxGpGQV+EZGaUeAXEakZBX4RkZpR4BcRqRkFfhGRmlHgFxGpGQV+EZGaUeAXEakZBX4RkZpR4BcRqRkFfhGRmlHgFxGpGQV+EZGaUeAXEakZBX4RkZpR4BcRqRkFfhGRmlHgFxGpGQV+EZGaUeAXEakZBX4RkZq5JO8TkGqanGmyd/9xXp2bZ+3YKDu3bGD7pvG8T0tEUOCXDEzONLn3sReYXzgHQHNunnsfewFAwV+kAJTqkdTt3X/8QtBvm184x979x3M6IxHppMAvqXt1bn6g4yIyXAr8krq1Y6MDHReR4VLgl9Tt3LKB0cbIomOjjRF2btmQ0xmJSCcN7krq2gO4quoRKSYFfsnE9k3jCvQiBdU31WNmD5jZKTM70nHsKjM7YGYvBf+uinjuOTM7FHw9keaJi4hIMnFy/A8CW7uO7QL+2t2vA/46+DnMvLtvDL7el/w0RUQkLX0Dv7s/C5zuOnwH8OfB938ObE/3tEREJCtJq3re6O7fCb7/v8AbIx53uZlNmdlBM9ve6wXNbEfw2KnZ2dmEpyUiIv0su5zT3R3wiF+/2d0ngF8F/tDMfrrH6+xz9wl3n1izZs1yT0tERCIkrer5rpm9yd2/Y2ZvAk6FPcjdm8G/L5vZ3wCbgG/0e/Hp6envmdm3Bjif1cD3Bnh8lajt9VTXtte13dC/7W+O+0JJA/8TwD3AnuDf/9H9gKDS54y7/8jMVgO3Ar8X58XdfaAuv5lNBXcWtaO2q+11Utd2Q7ptj1PO+TDwt8AGMztpZh+lFfB/ycxeAv5J8DNmNmFmnw2e+jPAlJkdBp4B9rj719I4aRERSa5vj9/d74741btDHjsF/Hrw/f8Gbl7W2YmISOqqslbPvrxPIEdqez3Vte11bTek2HZrFeWIiEhdVKXHLyIiMSnwi4jUTGkCv5l908xeCBZ8mwqOhS4WZy1/ZGYnzOx5M3t7vmc/mEEWxuvVVjO7J3j8S2Z2Tx5tGUREu3ebWbNjsb9tHb+7N2j3cTPb0nF8a3DshJlFrSNVKGZ2jZk9Y2ZfM7OjZvZvg+OV/tx7tLvyn7uZXW5mf2dmh4O2/1ZwfL2ZfSVoxyNmdmlw/LLg5xPB79d1vFbo3ySSu5fiC/gmsLrr2O8Bu4LvdwH/Kfh+G/C/AAPeAXwl7/MfsK2/ALwdOJK0rcBVwMvBv6uC71fl3bYE7d4N/IeQx94AHAYuA9bTmhg4Enx9A3gLcGnwmBvybluMtr8JeHvw/RuArwdtrPTn3qPdlf/cg8/uJ4LvG8BXgs/y88CHguN/CvzL4Pt/Bfxp8P2HgEd6/U16vXdpevwRohaLuwP4r95yEBiz1gzjUvDBFsaLausW4IC7n3b314ADLF1ltVAi2h3lDuBz7v4jd/974ARwS/B1wt1fdvcfA58LHlto7v4dd/8/wfffB14Exqn4596j3VEq87kHn90Pgh8bwZcDtwNfDI53f+bt/xa+CLzbzIzov0mkMgV+B54ys2kz2xEci1osbhx4peO5J+n9H1MZDNrWKv0NPh6kMx6wi3s/VLbdwS38Jlo9wNp87l3thhp87mY2YmaHaC17c4BWb33O3c8GD+lsx4U2Br9/HfhJErS9TIH/ne7+duA9wL82s1/o/KW37nlqUZtap7YCfwL8NLAR+A7w+7meTcbM7CeAR4F/5+7/0Pm7Kn/uIe2uxefu7ufcfSNwNa1e+vXDeN/SBH6/uODbKeBxWn+k77ZTOLZ4sbgmcE3H068OjpXZoG2txN/A3b8b/M9xHvgvXLyFrVy7zaxBK/g95O6PBYcr/7mHtbtOnzuAu8/RWtrm52ml7dqrKnS240Ibg99fCfw/ErS9FIHfzK4wsze0vwc2A0e4uFgcLF4s7gngnweVD+8AXu+4XS6rQdu6H9hsZquC2+TNwbFS6Rqb+RVanzu02v2hoNJhPXAd8HfAV4HrgsqIS2kNghV+288gV/tnwIvu/gcdv6r05x7V7jp87ma2xszGgu9HgV+iNcbxDPCB4GHdn3n7v4UPAE8Hd4FRf5NoeY9sx/miNVJ/OPg6CtwXHP9JWls/vgT8FXCVXxwt/wytfNkLwETebRiwvQ/Tur1doJWv+2iStgIfoTXQcwL4F3m3K2G7/1vQrueD/8Df1PH4+4J2Hwfe03F8G63qkG+0/1sp+hfwTlppnOeBQ8HXtqp/7j3aXfnPHXgrMBO08QjwieD4W2gF7hPAF4DLguOXBz+fCH7/ln5/k6gvLdkgIlIzpUj1iIhIehT4RURqRoFfRKRmFPhFRGpGgV9EpGYU+EVEakaBX0SkZv4/GdS4RSSADoYAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(train['GrLivArea'],y=target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_jYi6NCmOyKL",
        "outputId": "2e5c2628-03f9-4f1c-c491-c1a495f5bb78"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe53c00b040>"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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L6bQhX3/QdExT/majIIjUjQZ+qjNI0D7tlDFedPJJfPjGB4NNr6VJWRRdkXLt1w8smRUL7XTatV8fbgUSpWOaK4gevQZ+qrNxzSTXfv0Azxw7cWXEMTOed19yOb6FdEXW9FqalETZE3W63+/tvzLJPY8cSXz/ojsmcX+LXu1pKR3TXEEE+svOXc6X7n8itl2Kd/Vbz4+dcdq5lO/a7XuGTq+lGYspe7wm7v1unm31XMa4yI7Jx6f3D/0avSgd00xBpG6Sapez1jSPsizVS2muk5pHLzZNyqPMiTrTe1t85Cv7Bn6/IlMgux54MvG+iWVavXJUBdGjV44+H8P0hvv19PK4PF7SoG/n37mslTUX9lVSlU2v716RKZBeVT/XXKHVK0dVEIFeOfp8FFm9NMyCYgtBNUnn37mslTXj9lXSNsUpKgWS9PlPMpUaj7IgUjdJuXjl6AdT5JlRmvROkl5BtftgUdbKmr32SZWVKJsuXRnb/u5Lzyp5S6ROgujRK0efj6LPjLL2YnsF1e6DRVkraybtqzGzTNeTzcsnNl4ItHP1x90ZM2PTpSsX22U0BdGjV44+H3Wtk0460MRdu7Wsz5D0Pn/1WxdVniL5xMYLeeyTG/jR9jfz2Cc3KMhLGIE+r2uKhmiQKpph0itFGiR4l/UZ6rqvROKY1/CqQFNTUz4zM5P68d3VInBiHfcoCmm/6GpFIr2Z2ay7T8XdF0SOXjP24oW0BtCoTdTRgU3yFESgl3gau2gmrcYqeQsiR7/wH6N1dA7nhf8YIS6aNQiNXTSTLsMneQuiRx9SiiJPw0xSkrYyUijd75FmBrDIIIII9EpRxBtm7GKUcsRJn7WMFErcexgQVyKhMzHJKohAryUQkmUZxGxqjjjLwanXZy3jTDHuPRxOCPY6E5NhBJGjr+tEn6ZqYo446zhNr89axpli0ms5qEZfchNEj17llflqYiosa++712ct40wx6T06Lz0oMqwgAj2MXp11kZqYCst6cOr1WcsYzNaAuZQhiNSN5KuJqbCspaS9PmsZyxxoKQUpQxBLIEj+slbdVFWtM8xyD6NUYSTh6rUEggK95KbqtXUUsGWUDbXWjZldB7wFOOzuF0Rt7wSuAV4FXOLusVHZzH4E/A9wHHguaSMkDFVPXKvLOI0OOFI3aXL01wPru9oeAq4E7k3x/Mvc/WIF+fA1sVonb1qOQ+qob6B393uBp7vaHnb3+hZVSyW0tk4z5yBI+IquunHgLjObNbPNvR5oZpvNbMbMZo4c0SUAm6iJ1Tp501mN1FHRdfS/6u4tM3sZcLeZPRKdIZzA3XcCO6E9GFvwdkkBQpm4NkyOvYlzECR8hQZ6d29F/x42s1uBS0iX15eGqsuAaFbDrvOjCVBSR4WlbszsNDN7ycLvwBtpD+KK1NawOXZNgJI6SlNeuQt4PXCGmR0CrqY9OPt3wHLgDjN70N3XmdkK4PPuvgF4OXCrmS28zz+7+zeK+RhSR00sM8wjx970sxoJT99A7+6bEu66NeaxTwEbot8fBy4aauuksZq61HEVOfYmHhClWbTWjRSiqWWGZVcOqe5eyqBAL4Voaplh2Tn2ph4QpVmCWaZY6qXJZYZl5tibekCUZlGPXgqhyVPpaDaxlCGYQD+9t8Xa7Xs4e+sdrN2+RznOiqnMMB0dEKUMQaRumlrhETqVGfYXymxiqbcgAn3Vy+NK+UIqSdQBUYoWRKDXgNZo0RmcyGCCyNFPnDo+ULs0m0oSRQYTRI8+6WqINbxKYi00Pe2hMziRwQTRo392bn6g9lEWwkxMlSSKDCaIQK//+OmFkPZQSaLIYIII9PqPn14IaQ/V6IsMJogcvWqR02vy0gSdVJIokl4QgR70Hz8tXQFJZPQEE+glHZ39iIweBfoRpLMfkdESxGCsiIgkU6AXEQmcAr2ISOAU6EVEAqdALyISOFXdyBJNX/BMRE6kQC+LtM67SJiUupFFISx4JiInUqCXRSEseCYiJ1Kgl0Va7lkkTAr0skjLPYuESYOxskgLnomESYFeltCCZyLhUepGRCRwCvQiIoHrG+jN7DozO2xmD3W0vdPMDpjZ82Y21eO5683soJk9amZb89poERFJL02P/npgfVfbQ8CVwL1JTzKzMeAzwJuA84BNZnZets0UEZGs+gZ6d78XeLqr7WF37zdd8hLgUXd/3N1/BnwZeFvmLRURkUyKzNFPAk923D4UtYmISIlqMxhrZpvNbMbMZo4cOVL15oiIBKPIQN8CVnbcPjNqi+XuO919yt2nli9fXuBmiYiMliID/XeAc8zsbDM7BXgXcFuB7yciIjHSlFfuAr4FrDazQ2Z2lZn9ppkdAl4H3GFmu6PHrjCzOwHc/TngQ8Bu4GHgK+5+oKgPIiIi8czdq96GE0xNTfnMzEzVmyEi0hhmNuvusfOaajMYKyIixVCgFxEJnAK9iEjgFOhFRAKnQC8iEjgFehGRwCnQi4gEToFeRCRwCvQiIoFToBcRCZwCvYhI4BToRUQCp0AvIhI4BXoRkcAp0IuIBE6BXkQkcAr0IiKBU6AXEQmcAr2ISOAU6EVEAqdALyISOAV6EZHAKdCLiAROgV5EJHAK9CIigTu56g2Q+pre22LH7oM8dXSOFRPL2LJuNRvXTFa9WSIyIAV6iTW9t8W2W/YzN38cgNbRObbdsh9AwV6kYZS6kVg7dh9cDPIL5uaPs2P3wYq2SESyUqCXWE8dnRuoXUTqS4FeYq2YWDZQu4jUlwK9xNqybjXLxseWtC0bH2PLutUVbZGIZKXBWIm1MOCqqhuR5lOgl0Qb10wqsIsEoG/qxsyuM7PDZvZQR9tLzexuM/tB9O/pCc89bmYPRj+35bnhIiKSTpoc/fXA+q62rcA33f0c4JvR7Thz7n5x9HNF9s0UEZGs+gZ6d78XeLqr+W3AF6PfvwhszHezREQkL1mrbl7u7j+Jfv8P4OUJj3uxmc2Y2f1mtrHXC5rZ5uixM0eOHMm4WSIi0m3o8kp3d8AT7v5Fd58C3g18ysx+qcfr7HT3KXefWr58+bCbJSIikaxVN/9pZq9w95+Y2SuAw3EPcvdW9O/jZvavwBrgsX4vPjs7+1Mz+3HGbTsD+GnG54ZI+2Mp7Y+ltD+WavL++MWkO7IG+tuA9wLbo3+/1v2AqBLnmLv/n5mdAawF/iLNi7t75i69mc1EZxGC9kc37Y+ltD+WCnV/pCmv3AV8C1htZofM7CraAf43zOwHwK9HtzGzKTP7fPTUVwEzZrYPuAfY7u7fK+JDiIhIsr49enfflHDXG2IeOwN8IPr934ELh9o6EREZWohr3eysegNqRvtjKe2PpbQ/lgpyf1i7aEZEREIVYo9eREQ6KNCLiAQumEBvZuvN7KCZPWpmSWvvBMXMVprZPWb2PTM7YGZ/GLXHLjpnbX8b7aPvmtlrqv0ExTCzMTPba2a3R7fPNrMHos99o5mdErW/KLr9aHT/qko3vABmNmFmN5nZI2b2sJm9bpS/H2b24ej/ykNmtsvMXjwK348gAr2ZjQGfAd4EnAdsMrPzqt2qUjwHfMTdzwNeC/xe9LmTFp17E3BO9LMZ+IfyN7kUfwg83HH7z4G/cfdfBp4BrorarwKeidr/JnpcaD4NfMPdzwUuor1fRvL7YWaTwB8AU+5+ATAGvItR+H64e+N/gNcBuztubwO2Vb1dFeyHrwG/ARwEXhG1vQI4GP3+OWBTx+MXHxfKD3Am7eB1OXA7YLRnOp7c/V0BdgOvi34/OXqcVf0ZctwXPw/8sPszjer3A5gEngReGv29bwfWjcL3I4gePS/8ARccitpGRnRauQZ4gORF50ZhP30K+CjwfHT7F4Cj7v5cdLvzMy/uj+j+Z6PHh+Js4Ajwj1Eq6/Nmdhoj+v3w9pIsfwk8AfyE9t97lhH4foQS6Eeamf0ccDPwR+7+3533ebs7MhI1tGb2FuCwu89WvS01cTLwGuAf3H0N8L90XTtixL4fp9NeYv1sYAVwGideayNIoQT6FrCy4/aZUVvwzGycdpC/wd1viZr/M1psjq5F50LfT2uBK8zsR8CXaadvPg1MmNnCLPDOz7y4P6L7fx74rzI3uGCHgEPu/kB0+ybagX9Uvx+/DvzQ3Y+4+zxwC+3vTPDfj1AC/XeAc6LR81NoD7AEf+lCMzPgC8DD7v7XHXctLDoHSxeduw34nai64rXAsx2n8I3n7tvc/Ux3X0X7O7DH3d9De62ld0QP694fC/vpHdHjg+nduvt/AE+a2eqo6Q3A9xjR7wftlM1rzezU6P/Owv4I//tR9SBBXj/ABuD7tJdB/ljV21PSZ/5V2qfd3wUejH420M4jfhP4AfAvwEujxxvt6qTHgP20qw8q/xwF7ZvXA7dHv78S+DbwKPBV4EVR+4uj249G97+y6u0uYD9cDMxE35Fp4PRR/n4A1wKPAA8B/wS8aBS+H1oCQUQkcKGkbkREJIECvYhI4BToRUQCp0AvIhI4BXoRkcAp0IuIBE6BXkQkcP8PQ0O+aMa06U8AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(train['GarageArea'],y=target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pWyh-ilTOyKM",
        "outputId": "708d8e61-ab39-456d-f359-71eab5fe6ee2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(100, 39)"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nulls = train.isnull().sum().sort_values(ascending=False)[0:20]\n",
        "type(nulls)\n",
        "data = train.select_dtypes(include=[np.number]).interpolate().dropna(axis=1)\n",
        "sum(data.isnull().sum() != 0)\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AkPqFtzzOyKN",
        "outputId": "ae738f59-20f4-486c-c9c8-96904fafcba0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SalePrice      1.000000\n",
              "OverallQual    0.855061\n",
              "GrLivArea      0.735129\n",
              "GarageArea     0.688249\n",
              "GarageCars     0.663441\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "numeric = data.select_dtypes(include=[np.number])\n",
        "numeric.head()\n",
        "corr = numeric.corr()\n",
        "cols = corr['SalePrice'].sort_values(ascending=False)[:5].index\n",
        "cols\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FaPEDCEKOyKO",
        "outputId": "410d1814-a898-419b-a136-b93313a68595"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>YearBuilt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>1710</td>\n",
              "      <td>548</td>\n",
              "      <td>2</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>1262</td>\n",
              "      <td>460</td>\n",
              "      <td>2</td>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>1786</td>\n",
              "      <td>608</td>\n",
              "      <td>2</td>\n",
              "      <td>2001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>1717</td>\n",
              "      <td>642</td>\n",
              "      <td>3</td>\n",
              "      <td>1915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>2198</td>\n",
              "      <td>836</td>\n",
              "      <td>3</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>6</td>\n",
              "      <td>1470</td>\n",
              "      <td>420</td>\n",
              "      <td>2</td>\n",
              "      <td>1993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>7</td>\n",
              "      <td>1588</td>\n",
              "      <td>472</td>\n",
              "      <td>2</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>4</td>\n",
              "      <td>960</td>\n",
              "      <td>432</td>\n",
              "      <td>1</td>\n",
              "      <td>1965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5</td>\n",
              "      <td>835</td>\n",
              "      <td>366</td>\n",
              "      <td>1</td>\n",
              "      <td>1920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>4</td>\n",
              "      <td>1225</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1959</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    OverallQual  GrLivArea  GarageArea  GarageCars  YearBuilt\n",
              "0             7       1710         548           2       2003\n",
              "1             6       1262         460           2       1976\n",
              "2             7       1786         608           2       2001\n",
              "3             7       1717         642           3       1915\n",
              "4             8       2198         836           3       2000\n",
              "..          ...        ...         ...         ...        ...\n",
              "95            6       1470         420           2       1993\n",
              "96            7       1588         472           2       1999\n",
              "97            4        960         432           1       1965\n",
              "98            5        835         366           1       1920\n",
              "99            4       1225           0           0       1959\n",
              "\n",
              "[100 rows x 5 columns]"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = data[cols]\n",
        "Y = X['SalePrice']\n",
        "X = X.drop(['SalePrice'], axis = 1)\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7E8SpcPTOyKO",
        "outputId": "7e17c1bf-76f7-4458-b0be-d6c10e708c48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R^2 is: 0.8309859964337734\n"
          ]
        }
      ],
      "source": [
        "from sklearn import linear_model\n",
        "lr = linear_model.LinearRegression()\n",
        "model = lr.fit(X, Y)\n",
        "predictions = model.predict(X)\n",
        "print(f\"R^2 is: {model.score(X,Y)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2MZUfjA3OyKP",
        "outputId": "7b2718e0-65bd-46ff-a6da-702682816c18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0     208500\n",
              "1     181500\n",
              "2     223500\n",
              "3     140000\n",
              "4     250000\n",
              "       ...  \n",
              "95    185000\n",
              "96    214000\n",
              "97     94750\n",
              "98     83000\n",
              "99    128950\n",
              "Name: SalePrice, Length: 100, dtype: int64"
            ]
          },
          "execution_count": 129,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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rRz8kab+k45K+LWlVabdq/8nECUkvSprsss6/KfMfLp09Xhl3V3n/l1X5dV61X6l/WMbdVWlfq9pGOKvaV8gLSvvPl9ezZfzaLuqcVe3420x51M9qWXZ92sFnatiPQ9g2r1Htq+zhSn9uHsa20GGda1U7C+P7ZR3f1e36U+13k9ny+N1W20iX/XqhanvKHxjm31ebtT2q2iGc/1PtGPWtw+jHZsvo5sGl9ACQFFdiAkBSBDgAJEWAA0BSBDgAJEWAA0BSBDgAJEWAA0BS/w/CUCmL7C38YAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(Y - predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dtvnWStAOyKP",
        "outputId": "e6369570-afaa-49d4-9627-18bbaddb72ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fe53c360790>"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(predictions, Y, color = 'r')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wNvWwR8DOyKQ",
        "outputId": "7b4d2b91-59bd-4125-cefe-d0ab63b3f1c9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SalePrice</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>MasVnrArea</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>208500</td>\n",
              "      <td>7</td>\n",
              "      <td>196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>181500</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>223500</td>\n",
              "      <td>7</td>\n",
              "      <td>162.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>140000</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>250000</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>143000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>307000</td>\n",
              "      <td>8</td>\n",
              "      <td>186.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>200000</td>\n",
              "      <td>7</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>129900</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>118000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>129500</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>345000</td>\n",
              "      <td>9</td>\n",
              "      <td>286.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>144000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>279500</td>\n",
              "      <td>7</td>\n",
              "      <td>306.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>157000</td>\n",
              "      <td>6</td>\n",
              "      <td>212.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>132000</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>149000</td>\n",
              "      <td>6</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>90000</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>159000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>139000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    SalePrice  OverallQual  MasVnrArea\n",
              "0      208500            7       196.0\n",
              "1      181500            6         0.0\n",
              "2      223500            7       162.0\n",
              "3      140000            7         0.0\n",
              "4      250000            8       350.0\n",
              "5      143000            5         0.0\n",
              "6      307000            8       186.0\n",
              "7      200000            7       240.0\n",
              "8      129900            7         0.0\n",
              "9      118000            5         0.0\n",
              "10     129500            5         0.0\n",
              "11     345000            9       286.0\n",
              "12     144000            5         0.0\n",
              "13     279500            7       306.0\n",
              "14     157000            6       212.0\n",
              "15     132000            7         0.0\n",
              "16     149000            6       180.0\n",
              "17      90000            4         0.0\n",
              "18     159000            5         0.0\n",
              "19     139000            5         0.0"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train[['SalePrice', 'OverallQual', 'MasVnrArea']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLhamdEMOyKQ"
      },
      "source": [
        "### Interpolate Missing Data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tLaTZisJOyKQ",
        "outputId": "d0234c6b-8c22-4a3e-d493-381956a902b5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>...</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>196.0</td>\n",
              "      <td>...</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>162.0</td>\n",
              "      <td>...</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>350.0</td>\n",
              "      <td>...</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 40 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
              "0           0   1          60         65.0     8450            7            5   \n",
              "1           1   2          20         80.0     9600            6            8   \n",
              "2           2   3          60         68.0    11250            7            5   \n",
              "3           3   4          70         60.0     9550            7            5   \n",
              "4           4   5          60         84.0    14260            8            5   \n",
              "\n",
              "   YearBuilt  YearRemodAdd  MasVnrArea  ...  OpenPorchSF  EnclosedPorch  \\\n",
              "0       2003          2003       196.0  ...           61              0   \n",
              "1       1976          1976         0.0  ...            0              0   \n",
              "2       2001          2002       162.0  ...           42              0   \n",
              "3       1915          1970         0.0  ...           35            272   \n",
              "4       2000          2000       350.0  ...           84              0   \n",
              "\n",
              "   3SsnPorch  ScreenPorch  PoolArea  PoolQC  MiscVal  MoSold  YrSold  \\\n",
              "0          0            0         0     NaN        0       2    2008   \n",
              "1          0            0         0     NaN        0       5    2007   \n",
              "2          0            0         0     NaN        0       9    2008   \n",
              "3          0            0         0     NaN        0       2    2006   \n",
              "4          0            0         0     NaN        0      12    2008   \n",
              "\n",
              "   SalePrice  \n",
              "0     208500  \n",
              "1     181500  \n",
              "2     223500  \n",
              "3     140000  \n",
              "4     250000  \n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "file = 'https://raw.githubusercontent.com/GAdevopsES/Python100/master/houseSmallData.csv'\n",
        "data = pd.read_csv(file)\n",
        "train = data.iloc[0:100,:]\n",
        "numeric = train.select_dtypes(include=[np.number])\n",
        "numeric.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8VwlBdJmOyKR",
        "outputId": "4b638bed-a898-47a0-87f7-a10c775a036b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SalePrice      1.000000\n",
              "OverallQual    0.855061\n",
              "GrLivArea      0.735129\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "corr = numeric.corr()\n",
        "cols = corr['SalePrice'].sort_values(ascending=False)[:3]\n",
        "cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ew40NoaVOyKR"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "name": "Predict House PricesPart 1.ipynb",
      "provenance": []
    },
    "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": "3.9.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}