3rd-python-sunny_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "path1=os.getcwd()\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27230, 18)\n",
      "(27230, 18)\n",
      "object\n"
     ]
    }
   ],
   "source": [
    "path=path1+'/Mod_Data.csv'\n",
    "data=pd.read_csv(path)\n",
    "data.dropna(inplace=True)\n",
    "print(data.shape)\n",
    "#print(np.count_nonzero(data['price'], axis=0))\n",
    "#print(data['price'].shape)\n",
    "#data.dropna(inplace=True)\n",
    "print(data.shape)\n",
    "print(data['last_review'].dtype)\n",
    "\n",
    "data['last_review']=data['last_review'].astype('datetime64[ns]')\n",
    "data['month_of_review']=pd.DatetimeIndex(data['last_review']).month\n",
    "data['year_of_review']=pd.DatetimeIndex(data['last_review']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_enc_rt = pd.get_dummies(data.room_type, prefix='Roomtype')\n",
    "X_enc_nh = pd.get_dummies(data.neighbourhood_group, prefix='NBG')\n",
    "X_enc_ng = pd.get_dummies(data.neighbourhood, prefix='NB')\n",
    "X_enc_hname=pd.get_dummies(data.host_name,prefix='HName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27230, 245)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Var = pd.concat([data, X_enc_rt,X_enc_nh,X_enc_ng], axis=1)\n",
    "#Var = data\n",
    "#print('Var columns ',Var.shape)\n",
    "#print('Data columns ',data.shape)\n",
    "Var.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-f9b8bb865974>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  D1['price new']=n1\n",
      "<ipython-input-5-f9b8bb865974>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  D1['Distance new']=n2\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>...</th>\n",
       "      <th>NB_Westerleigh</th>\n",
       "      <th>NB_Whitestone</th>\n",
       "      <th>NB_Williamsbridge</th>\n",
       "      <th>NB_Williamsburg</th>\n",
       "      <th>NB_Windsor Terrace</th>\n",
       "      <th>NB_Woodhaven</th>\n",
       "      <th>NB_Woodlawn</th>\n",
       "      <th>NB_Woodside</th>\n",
       "      <th>price new</th>\n",
       "      <th>Distance new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5728806</td>\n",
       "      <td>Large private room in Nolita</td>\n",
       "      <td>4271676</td>\n",
       "      <td>Nat</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Nolita</td>\n",
       "      <td>40.72217</td>\n",
       "      <td>-73.99481</td>\n",
       "      <td>Private room</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>8.169504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>35515415</td>\n",
       "      <td>COZY APT CLOSE TO LGA &amp; JFK AND TRANSPORTATION</td>\n",
       "      <td>267193767</td>\n",
       "      <td>Jenny</td>\n",
       "      <td>Queens</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>40.75558</td>\n",
       "      <td>-73.89316</td>\n",
       "      <td>Entire home/apt</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.298317</td>\n",
       "      <td>8.195852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3780951</td>\n",
       "      <td>Charming Harlem apartment</td>\n",
       "      <td>16065171</td>\n",
       "      <td>Gina</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.81022</td>\n",
       "      <td>-73.94266</td>\n",
       "      <td>Shared room</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.744932</td>\n",
       "      <td>7.564448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>9944426</td>\n",
       "      <td>Lower Manhattan Luxury</td>\n",
       "      <td>51105827</td>\n",
       "      <td>Dax</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Battery Park City</td>\n",
       "      <td>40.71192</td>\n",
       "      <td>-74.01572</td>\n",
       "      <td>Entire home/apt</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.416100</td>\n",
       "      <td>8.384940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>19786915</td>\n",
       "      <td>Spacious Clean Studio  Apartment</td>\n",
       "      <td>86892032</td>\n",
       "      <td>Marian</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>40.65766</td>\n",
       "      <td>-73.72838</td>\n",
       "      <td>Entire home/apt</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.094345</td>\n",
       "      <td>9.424361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 247 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        id                                            name  \\\n",
       "0           0   5728806                    Large private room in Nolita   \n",
       "2           2  35515415  COZY APT CLOSE TO LGA & JFK AND TRANSPORTATION   \n",
       "4           4   3780951                       Charming Harlem apartment   \n",
       "5           5   9944426                          Lower Manhattan Luxury   \n",
       "6           6  19786915                Spacious Clean Studio  Apartment   \n",
       "\n",
       "     host_id host_name neighbourhood_group      neighbourhood  latitude  \\\n",
       "0    4271676       Nat           Manhattan             Nolita  40.72217   \n",
       "2  267193767     Jenny              Queens      East Elmhurst  40.75558   \n",
       "4   16065171      Gina           Manhattan             Harlem  40.81022   \n",
       "5   51105827       Dax           Manhattan  Battery Park City  40.71192   \n",
       "6   86892032    Marian              Queens           Rosedale  40.65766   \n",
       "\n",
       "   longitude        room_type  ...  NB_Westerleigh  NB_Whitestone  \\\n",
       "0  -73.99481     Private room  ...               0              0   \n",
       "2  -73.89316  Entire home/apt  ...               0              0   \n",
       "4  -73.94266      Shared room  ...               0              0   \n",
       "5  -74.01572  Entire home/apt  ...               0              0   \n",
       "6  -73.72838  Entire home/apt  ...               0              0   \n",
       "\n",
       "   NB_Williamsbridge NB_Williamsburg  NB_Windsor Terrace  NB_Woodhaven  \\\n",
       "0                  0               0                   0             0   \n",
       "2                  0               0                   0             0   \n",
       "4                  0               0                   0             0   \n",
       "5                  0               0                   0             0   \n",
       "6                  0               0                   0             0   \n",
       "\n",
       "   NB_Woodlawn  NB_Woodside  price new  Distance new  \n",
       "0            0            0   4.787492      8.169504  \n",
       "2            0            0   5.298317      8.195852  \n",
       "4            0            0   4.744932      7.564448  \n",
       "5            0            0   5.416100      8.384940  \n",
       "6            0            0   4.094345      9.424361  \n",
       "\n",
       "[5 rows x 247 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D1=Var[(Var['price'] > 0)]\n",
    "#D2=D1.iloc[:,7:26]\n",
    "n1=np.log(D1['price'])\n",
    "n2=np.log(D1['Distance in m'])\n",
    "D1['price new']=n1\n",
    "D1['Distance new']=n2\n",
    "D1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure =plt.figure(figsize=(15,8))\n",
    "sns.distplot(D1['price new'],bins=20)\n",
    "plt.title('Distribution of Properties as a function of distance from City Center', fontsize=30)\n",
    "plt.xlabel('Distance in (m)',fontsize=25)\n",
    "plt.ylabel('Distribution',fontsize=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-4e3578fcf858>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  D2.dropna(inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25827, 247)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D2=D1[(D1['price new'] >=2. )&(D1['price new'] <= 5.8)]\n",
    "D2.dropna(inplace=True)\n",
    "D2.shape\n",
    "#D2=D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#Y1=D2[['Distance in m']].values.astype(float)\n",
    "#min_max_scaler = preprocessing.MinMaxScaler()\n",
    "#D_scaled=min_max_scaler.fit_transform(Y1)\n",
    "#D_normalized=pd.DataFrame(D_scaled)\n",
    "#D_normalized\n",
    "#D2['D_norm']=D_normalized\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X=D2.drop(['price new','name','host_name','neighbourhood_group','neighbourhood','room_type','price','minimum_nights','last_review','reviews_per_month','calculated_host_listings_count','Distance new','availability_365'],axis=1)\n",
    "#X=D2.drop(['Distance in m','price new','name','host_name','neighbourhood_group','neighbourhood','room_type','price','last_review','reviews_per_month','number_of_reviews'],axis=1)\n",
    "\n",
    "#Y1=D2[['price new']].values.astype(float)\n",
    "#min_max_scaler=preprocessing.MinMaxScaler()\n",
    "#Y_scaled=min_max_scaler.fit_transform(Y1)\n",
    "#Y=pd.DataFrame(Y_scaled)\n",
    "#D2_normalized\n",
    "Y=D2['price new']\n",
    "#X.isnull().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6007868230504613"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.3, random_state=60)\n",
    "sklr = LinearRegression()\n",
    "sklr.fit(X_train,y_train)\n",
    "sklr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35995419967893133\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "y_pred=sklr.predict(X_test)\n",
    "reg_model_mse=mean_squared_error(y_pred,y_test)\n",
    "print(math.sqrt(reg_model_mse))"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}