predict_prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4aec5e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "(1460, 35)\n"
     ]
    }
   ],
   "source": [
    "#import House sales data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('train.csv')\n",
    "df = pd.DataFrame(data)\n",
    "print(df.shape)\n",
    "\n",
    "#remove non-numeric column data\n",
    "num = df.select_dtypes(include=[np.number]).dropna(axis=1) \n",
    "print(num.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "074a6b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    }
   ],
   "source": [
    "#build model\n",
    "i_corr = num.corr()['SalePrice'].sort_values(ascending=False)[1:num.size]\n",
    "print(i_corr.size)\n",
    "#check if absolute value changes order of most correlated\n",
    "abs_corr = abs(num.corr()['SalePrice']).sort_values(ascending=False)[1:40]\n",
    "#print(abs_corr)\n",
    "\n",
    "# get IDs of most correlated\n",
    "# cols = i_corr.index[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c039a53d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1460\n",
      "1435\n",
      "R^2 is: 0.8088471172804269\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#assess model, deploy model\n",
    "\n",
    "#for i in range(1,37): #check variables to see if they improve - kept improving\n",
    "cols = i_corr.index[0:i_corr.size]\n",
    "print(len(num))\n",
    "\n",
    "#create data set for model training\n",
    "X = num[cols]\n",
    "X = X[0:len(num)-25]\n",
    "Y = num['SalePrice'][0:len(num)-25]\n",
    "plt.hist(Y)\n",
    "print(len(Y))\n",
    "\n",
    "#create model\n",
    "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)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "241f198e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25, 34)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22b4ae83430>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#plt.scatter(predictions, Y)\n",
    "\n",
    "#check on remaining 24 rows\n",
    "tester = num[cols][len(num)-25:len(num)]\n",
    "print(tester.shape)\n",
    "\n",
    "tester_y = num['SalePrice'][len(num)-25:len(num)]\n",
    "test_train = model.predict(tester)\n",
    "plt.scatter(test_train,tester_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abdaf5d5",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'SalePrice'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'SalePrice'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\LAURAC~1\\AppData\\Local\\Temp/ipykernel_34544/1807178242.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpsetData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'test.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mpset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpsetData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#remove non-numeric columns and log transform\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'SalePrice'"
     ]
    }
   ],
   "source": [
    "psetData = pd.read_csv('test.csv')\n",
    "pset = pd.DataFrame(psetData)\n",
    "\n",
    "#remove non-numeric columns and log transform\n",
    "pset2 = pset.select_dtypes(include=[np.number])[cols].dropna(axis=1)\n",
    "print(pset2.shape)\n",
    "tryit = model.predict(pset2)\n",
    "\n",
    "#create output dataframe\n",
    "out = pd.DataFrame(pset['Id'])\n",
    "out = out.assign(SalePrice = tryit)\n",
    "\n",
    "out.to_csv(\"C:/Users/Laura C Chen/Desktop/1.00/PS2HouseDataExercise/predictions.csv\",index=False)\n",
    "                \n",
    "#check histogram\n",
    "plt.hist(tryit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8149f63a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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