Observer.4.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "big_data = pd.read_csv('airbnb_coded.2.0.csv')\n",
    "sc = SC()\n",
    "sc_y = SC()\n",
    "le = LabelEncoder()\n",
    "knn = KNN(n_neighbors=65, weights='distance')\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>id</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>property_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>...</th>\n",
       "      <th>bed_code</th>\n",
       "      <th>room_code</th>\n",
       "      <th>verified_id</th>\n",
       "      <th>profile_pic</th>\n",
       "      <th>superhost</th>\n",
       "      <th>phone_verified</th>\n",
       "      <th>buisiness_travel</th>\n",
       "      <th>instant_book</th>\n",
       "      <th>listing_class</th>\n",
       "      <th>price_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Apartment</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>4534251.0</td>\n",
       "      <td>41610.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22314560.0</td>\n",
       "      <td>43087.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>17838393.0</td>\n",
       "      <td>41565.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>25123411.0</td>\n",
       "      <td>43236.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1          id  host_since  \\\n",
       "0           0             0   8998355.0     42297.0   \n",
       "1           1             1   4534251.0     41610.0   \n",
       "2           2             2  22314560.0     43087.0   \n",
       "3           3             3  17838393.0     41565.0   \n",
       "4           4             4  25123411.0     43236.0   \n",
       "\n",
       "   host_total_listings_count property_type  accommodates  bathrooms  bedrooms  \\\n",
       "0                        2.0     Apartment           2.0        1.0       2.0   \n",
       "1                        1.0     Apartment           4.0        1.0       2.0   \n",
       "2                        1.0     Apartment           4.0        1.0       2.0   \n",
       "3                        1.0     Apartment           6.0        1.5       3.0   \n",
       "4                        1.0     Apartment           5.0        1.0       2.0   \n",
       "\n",
       "   beds  ...  bed_code  room_code  verified_id  profile_pic  superhost  \\\n",
       "0   2.0  ...       2.0        NaN          0.0          1.0        0.0   \n",
       "1   2.0  ...       2.0        NaN          0.0          1.0        0.0   \n",
       "2   2.0  ...       2.0        NaN          0.0          1.0        0.0   \n",
       "3   4.0  ...       2.0        NaN          1.0          1.0        0.0   \n",
       "4   5.0  ...       2.0        NaN          0.0          1.0        0.0   \n",
       "\n",
       "   phone_verified  buisiness_travel  instant_book  listing_class  price_class  \n",
       "0             0.0               0.0           0.0              0            0  \n",
       "1             0.0               0.0           0.0              0            0  \n",
       "2             0.0               0.0           0.0              4            0  \n",
       "3             0.0               0.0           0.0              1            0  \n",
       "4             0.0               0.0           0.0              3            1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_cluster = []\n",
    "for index in big_data['price_x']:\n",
    "    if index>=5000:\n",
    "        price_cluster.append(4)\n",
    "    elif index >=1000:\n",
    "        price_cluster.append(3)\n",
    "    elif index>=500:\n",
    "        price_cluster.append(2)\n",
    "    elif index>=100:\n",
    "        price_cluster.append(1)\n",
    "    else:\n",
    "        price_cluster.append(0)\n",
    "big_data['price_class'] = price_cluster\n",
    "big_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "4\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(), [-2])], 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 = 'price_class'\n",
    "\n",
    "\n",
    "data = big_data[[var18, var2, var3, var22, var10]]\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['property_type'] = data['property_type'].fillna('Apartment')\n",
    "\n",
    "\n",
    "# data[var6][17990] = data[var6].median()\n",
    "\n",
    "X = data.iloc[:, :-2].values\n",
    "y = data.iloc[:, -2].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",
    "# X[:,-8:] = sc.fit_transform(X[:,-8:])\n",
    "# X = sc.fit_transform(X)\n",
    "counter = 0\n",
    "for i in range(len(y)):\n",
    "    if y[i]>=6000:\n",
    "        counter+=1\n",
    "print(counter)\n",
    "print(max(y.T))\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": 74,
   "metadata": {},
   "outputs": [
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     "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": [
    "plt.scatter(data[var22].values, data[var10].values, alpha=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f494d694f10>"
      ]
     },
     "execution_count": 75,
     "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": [
    "plt.scatter(data[var3].values, data['price_class'].values, alpha=0.1, c='red')\n",
    "plt.scatter(data[var10].values, data['price_class'].values, alpha=0.1, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9026765917009819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9009791243303159"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cat = data[[var18, var2, var3]].values\n",
    "y_cat = data[[var22]].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_cat, y_cat, test_size = 0.3)\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(max_depth=2)\n",
    "classifier = classifier.fit(X_train, y_train)\n",
    "print(classifier.score(X_train, y_train))\n",
    "classifier.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.89584488 0.90687361 0.89855876 0.90521064 0.90077605 0.90022173\n",
      " 0.904102   0.90521064 0.90354767 0.90077605]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9021122037208787"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(classifier, X_cat, \n",
    "                          y_cat, scoring='accuracy', cv=10)\n",
    "print(scores)\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = sc.fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)\n",
    "# X_train = sc.fit_transform(X_train)\n",
    "# X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5413"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(max_depth=2)\n",
    "classifier = classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "# y_test[:,-1] = y_pred\n",
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f494ccd98b0>"
      ]
     },
     "execution_count": 87,
     "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": [
    "yy = np.random.rand(5413,1)\n",
    "plt.scatter(y_pred, yy, alpha=0.1, c='red')\n",
    "plt.scatter(np.array(y_test), yy, alpha=0.1, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_data.to_csv('airbnb_coded.3.0.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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.3.0.csv')\n",
    "sc = SC()\n",
    "sc_y = SC()\n",
    "knn = KNN(n_neighbors=65, weights='distance')\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.   0.   0. ...  97.   0.   0.]\n",
      " [  1.   0.   0. ...  75.   0.   0.]\n",
      " [  0.   0.   0. ...  80.  10.  83.]\n",
      " ...\n",
      " [  0.   0.   0. ...  99.  19.   6.]\n",
      " [  0.   0.   0. ...  90.  10.   0.]\n",
      " [  1.   0.   0. ... 100.   0.   0.]]\n",
      "21\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 = 'price_class'\n",
    "\n",
    "\n",
    "data = big_data[[var18, var2, var3, var, var6, var8,  var17, var21, var19, var9, var12, var20, var14, var10, var22]]\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",
    "\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[:, :].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",
    "print(X)\n",
    "# X[:,-8:] = sc.fit_transform(X[:,-8:])\n",
    "# X = sc.fit_transform(X)\n",
    "print(len(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": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = sc.fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)\n",
    "# X_train = sc.fit_transform(X_train)\n",
    "# X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0. 0. ... 1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(max_depth=2)\n",
    "print(y_test[:,-1])\n",
    "classifier = classifier.fit(X_train[:,:3], y_train[:,-1])\n",
    "y_pred = classifier.predict(X_test[:,:3])\n",
    "# y_test[:,-1] = y_pred\n",
    "# np.array(y_test[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f494d91e9d0>"
      ]
     },
     "execution_count": 58,
     "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": [
    "plt.scatter(y_pred, np.array(y_test[:,-2]), alpha=0.1, c='red')\n",
    "plt.scatter(np.array(y_test[:,-1]), np.array(y_test[:,-2]), alpha=0.1, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9643113004434647"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor as RFR\n",
    "rf = RFR(n_estimators=500, min_samples_leaf=1)\n",
    "rf = rf.fit(X_train, y_train[:,-2])\n",
    "rf.score(X_train, y_train[:,-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.score(X_test, y_test[:,-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf = RFR(n_estimators=500, min_samples_leaf=10)\n",
    "# rf = rf.fit(X_train, y_train)\n",
    "# rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.96399424 0.35206444 0.87540843 0.76764399 0.95998444 0.60615038\n",
      " 0.84268381 0.78519998 0.71470277 0.94645633]\n",
      "0.781428880757405\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.18021197709847947"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(rf, X, \n",
    "                          y, scoring='r2', cv=10)\n",
    "print(scores)\n",
    "print(np.mean(scores))\n",
    "np.std(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(rf, X, \n",
    "                          y, scoring='r2', cv=15)\n",
    "print(scores)\n",
    "print(np.mean(scores))\n",
    "np.std(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = data[var10].isna()\n",
    "array = array.values\n",
    "for i in range(len(array)):\n",
    "    if array[i]:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn.fit(X_train, y_train)\n",
    "knn.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = -cross_val_score(knn, X, \n",
    "                          y, scoring='neg_mean_absolute_error', cv=5)\n",
    "print(scores)\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor as DTR\n",
    "dt = DTR(min_samples_leaf=10)\n",
    "dt = dt.fit(X_train, y_train)\n",
    "dt.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = -cross_val_score(dt, X, \n",
    "                          y, scoring='neg_mean_absolute_error', cv=5)\n",
    "print(scores)\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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