Correlator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn.cluster as cluster\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler as sc\n",
    "from sklearn.neighbors import KNeighborsRegressor as KNN\n",
    "\n",
    "big_data = pd.read_csv('airbnb_coded.2.0.csv')\n",
    "sc = sc()\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0 0.0 0.0 ... nan 10.0 0]\n",
      " [1.0 0.0 0.0 ... 9.0 9.0 0]\n",
      " [1.0 0.0 0.0 ... 7.0 9.0 83]\n",
      " ...\n",
      " [1.0 0.0 0.0 ... 9.0 10.0 6]\n",
      " [1.0 0.0 0.0 ... 9.0 9.0 0]\n",
      " [1.0 0.0 0.0 ... 10.0 10.0 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-1])], remainder='passthrough')\n",
    "var = 'bathrooms'\n",
    "var2 = 'accommodates'\n",
    "var3 = 'cleaning_fee'\n",
    "var4 = 'bedrooms'\n",
    "var5 = 'beds'\n",
    "var6 = 'minimum_nights_x'\n",
    "var7 = 'maximum_nights'\n",
    "var8 = 'number_of_reviews_y'\n",
    "var9 = 'listing_class'\n",
    "var10 = 'price_x'\n",
    "var11 = 'bed_code'\n",
    "var12 = 'cancellation_policy_mapped'\n",
    "var13 = 'room_code'\n",
    "var14 = 'superhost'\n",
    "var15 = 'instant_book'\n",
    "var16 = 'review_scores_location'\n",
    "var17 = 'review_scores_rating'\n",
    "var18 = 'host_total_listings_count'\n",
    "var19 = 'availability_90'\n",
    "var20 = 'room_code'\n",
    "var21 = 'extra_people'\n",
    "var22 = 'property_type'\n",
    "var23 = 'review_scores_value'\n",
    "var24 = 'review_scores_cleanliness'\n",
    "var25 = 'review_scores_checkin'\n",
    "var26 = 'review_scores_communication'\n",
    "var27 = 'review_scores_value'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "data = big_data[[var, var2, var3, var6, var8, var18, var16, var21, var17, var24, var23, var19, var9, var12, var22, var11, var13, var10]]\n",
    "\n",
    "\n",
    "\n",
    "data[var] = data[var].fillna(data[var].median())\n",
    "data[var2] = data[var2].fillna(data[var2].median())\n",
    "data[var3] = data[var3].fillna(data[var3].median())\n",
    "# data[var4] = data[var4].fillna(data[var4].median())\n",
    "# data[var5] = data[var5].fillna(data[var5].median())\n",
    "\n",
    "data[var6][17990] = data[var6].median()\n",
    "data[var6][6215] = data[var6].median()\n",
    "data[var6][11299] = data[var6].median()\n",
    "data[var6][12737] = data[var6].median()\n",
    "data[var6][14598] = data[var6].median()\n",
    "# data[var7][872] = 8000\n",
    "# data[var7][2372] = 8000\n",
    "# data[var7][4391] = 8000\n",
    "# data[var7][17952] = 8000\n",
    "# data[var7][17990] = data[var7].median()\n",
    "# data[var7][6351] = data[var7].median()\n",
    "# data[var7][8341] = data[var7].median()\n",
    "\n",
    "\n",
    "data[var6] = data[var6].fillna(data[var6].median())\n",
    "# data[var7] = data[var7].fillna(data[var7].median())\n",
    "data[var8] = data[var8].fillna(data[var8].median())\n",
    "data[var10] = data[var10].fillna(data[var10].median())\n",
    "data[var11] = data[var11].fillna(data[var11].median())\n",
    "data[var12] = data[var12].fillna(data[var12].median())\n",
    "data[var13] = data[var13].fillna(data[var13].median())\n",
    "# data[var14] = data[var14].fillna(data[var14].median())\n",
    "# data[var15] = data[var15].fillna(data[var15].median())\n",
    "# data[var16] = data[var16].fillna(data[var16].median())\n",
    "data[var17] = data[var17].fillna(data[var17].median())\n",
    "data[var18] = data[var18].fillna(data[var18].median())\n",
    "data[var19] = data[var19].fillna(data[var19].median())\n",
    "data[var20] = data[var20].fillna(data[var20].median())\n",
    "data[var21] = data[var21].fillna(data[var21].median())\n",
    "data[var22] = data[var22].fillna('Apartment')\n",
    "data[var23] = data[var23].fillna(data[var23].median())\n",
    "\n",
    "\n",
    "\n",
    "# data[var6][17990] = data[var6].median()\n",
    "\n",
    "# data = data.drop(data.index[17990])\n",
    "data = data.drop(data.index[14598])\n",
    "\n",
    "\n",
    "\n",
    "X = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, -1].values\n",
    "X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "X = np.array(X)\n",
    "X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "X = np.array(X)\n",
    "X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "X = np.array(X)\n",
    "X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "X = np.array(X)\n",
    "# ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-4])], remainder='passthrough')\n",
    "X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "X = np.array(X)\n",
    "# ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-5])], remainder='passthrough')\n",
    "# X = ct.fit_transform(X)\n",
    "# X = X.todense()\n",
    "# X = np.array(X)\n",
    "# ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [-6])], remainder='passthrough')\n",
    "# X = ct.fit_transform(X)\n",
    "# # X = X.todense()\n",
    "# X = np.array(X)\n",
    "\n",
    "# plt.scatter(data[var6].values, data[var7].values, s=100, c='red', alpha=0.5)\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "\n",
    "print(X)\n",
    "# X[:,-8:] = sc.fit_transform(X[:,-8:])\n",
    "# X = sc.fit_transform(X)\n",
    "# print(X[0])\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1)\n",
    "\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[        nan -0.48993592 -0.48993592 ...  0.69738701 -0.48993592\n",
      "  0.69738701]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f6679408430>"
      ]
     },
     "execution_count": 185,
     "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": [
    "xx = sc.fit_transform(big_data[[var27, var10]].values)\n",
    "print(xx.T[0])\n",
    "# plt.scatter(xx.T[0], xx.T[1], alpha=0.2)\n",
    "plt.scatter(big_data[var27].values, big_data[var10].values, alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}