Lab4_Task.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecd455a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39e6ef60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1465896</td>\n",
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       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
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       "      <td>288028</td>\n",
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>483084</td>\n",
       "      <td>241542</td>\n",
       "      <td>497</td>\n",
       "      <td>486</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>furnished</td>\n",
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       "    <tr>\n",
       "      <th>544</th>\n",
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       "      <td>406317</td>\n",
       "      <td>527</td>\n",
       "      <td>771</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price    area  length  width  bedrooms  bathrooms  stories mainroad  \\\n",
       "0    1465896  244316     593    412         4          2        3      yes   \n",
       "1    3123904  390488     734    532         4          4        4      yes   \n",
       "2    1519896  379974     498    763         3          2        2      yes   \n",
       "3    1152112  288028     382    754         4          2        2      yes   \n",
       "4     887292  221823     503    441         4          1        2      yes   \n",
       "..       ...     ...     ...    ...       ...        ...      ...      ...   \n",
       "540   377880  188940     470    402         2          1        1      yes   \n",
       "541   483084  241542     497    486         3          1        1       no   \n",
       "542   364276  182138     374    487         2          1        1      yes   \n",
       "543   734160  367080     570    644         3          1        1       no   \n",
       "544  1625268  406317     527    771         3          1        2      yes   \n",
       "\n",
       "    guestroom basement hotwaterheating airconditioning  parking prefarea  \\\n",
       "0          no       no              no             yes        2      yes   \n",
       "1          no       no              no             yes        3       no   \n",
       "2          no      yes              no              no        2      yes   \n",
       "3          no      yes              no             yes        3      yes   \n",
       "4         yes      yes              no             yes        2       no   \n",
       "..        ...      ...             ...             ...      ...      ...   \n",
       "540        no      yes              no              no        2       no   \n",
       "541        no       no              no              no        0       no   \n",
       "542        no       no              no              no        0       no   \n",
       "543        no       no              no              no        0       no   \n",
       "544        no       no              no              no        0       no   \n",
       "\n",
       "    furnishingstatus  \n",
       "0          furnished  \n",
       "1          furnished  \n",
       "2     semi-furnished  \n",
       "3          furnished  \n",
       "4          furnished  \n",
       "..               ...  \n",
       "540      unfurnished  \n",
       "541   semi-furnished  \n",
       "542      unfurnished  \n",
       "543        furnished  \n",
       "544      unfurnished  \n",
       "\n",
       "[545 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Housing.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6f189c",
   "metadata": {},
   "source": [
    "#### Apply univariate analysis on a feature in the given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7d0f6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1058779.8678899081"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = data['price'].mean()\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16e1517d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "907584.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = data['price'].median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "200bc873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "667148.2157118549"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = data['price'].std()\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89f3e48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.610794\n",
       "1      3.098294\n",
       "2      0.691810\n",
       "3      0.140026\n",
       "4     -0.257282\n",
       "         ...   \n",
       "540   -1.021550\n",
       "541   -0.863713\n",
       "542   -1.041960\n",
       "543   -0.487025\n",
       "544    0.849899\n",
       "Name: price, Length: 545, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "stats.zscore(data['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18ec9d9",
   "metadata": {},
   "source": [
    "#### Use any classifier on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56b21d87",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1625268</td>\n",
       "      <td>406317</td>\n",
       "      <td>527</td>\n",
       "      <td>771</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price    area  length  width  bedrooms  bathrooms  stories  mainroad  \\\n",
       "0    1465896  244316     593    412         4          2        3         1   \n",
       "1    3123904  390488     734    532         4          4        4         1   \n",
       "2    1519896  379974     498    763         3          2        2         1   \n",
       "3    1152112  288028     382    754         4          2        2         1   \n",
       "4     887292  221823     503    441         4          1        2         1   \n",
       "..       ...     ...     ...    ...       ...        ...      ...       ...   \n",
       "540   377880  188940     470    402         2          1        1         1   \n",
       "541   483084  241542     497    486         3          1        1         0   \n",
       "542   364276  182138     374    487         2          1        1         1   \n",
       "543   734160  367080     570    644         3          1        1         0   \n",
       "544  1625268  406317     527    771         3          1        2         1   \n",
       "\n",
       "     guestroom  basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0            0         0                0                1        2         1   \n",
       "1            0         0                0                1        3         0   \n",
       "2            0         1                0                0        2         1   \n",
       "3            0         1                0                1        3         1   \n",
       "4            1         1                0                1        2         0   \n",
       "..         ...       ...              ...              ...      ...       ...   \n",
       "540          0         1                0                0        2         0   \n",
       "541          0         0                0                0        0         0   \n",
       "542          0         0                0                0        0         0   \n",
       "543          0         0                0                0        0         0   \n",
       "544          0         0                0                0        0         0   \n",
       "\n",
       "     furnishingstatus  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   1  \n",
       "3                   0  \n",
       "4                   0  \n",
       "..                ...  \n",
       "540                 2  \n",
       "541                 1  \n",
       "542                 2  \n",
       "543                 0  \n",
       "544                 2  \n",
       "\n",
       "[545 rows x 15 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoding_columns = ['mainroad', 'guestroom', 'basement', 'hotwaterheating','airconditioning', 'prefarea', 'furnishingstatus']\n",
    "\n",
    "data[encoding_columns] = data[encoding_columns].apply(LabelEncoder().fit_transform)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0681a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = data.drop('furnishingstatus',axis=1)\n",
    "y = data['furnishingstatus']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a43b2b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.69724770642202"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "predictions = gnb.predict(X_test)\n",
    "\n",
    "accuracy_score(y_test, predictions) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5552b78",
   "metadata": {},
   "source": [
    "#### Normalize the data using techniques learned in the class and feed the data to any classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2abdeea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaling_columns = data[['price', 'area', 'length','width']]\n",
    "\n",
    "scaled = scaler.fit_transform(scaling_columns)\n",
    "df = pd.DataFrame(data = scaled, columns=[\"price\",\"area\",\"length\",'width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "40de3634",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.columns:\n",
    "     data[i] = df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "121d0e49",
   "metadata": {},
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.287069</td>\n",
       "      <td>0.288093</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.224</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.663195</td>\n",
       "      <td>0.569795</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.464</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.299319</td>\n",
       "      <td>0.549533</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.926</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.215885</td>\n",
       "      <td>0.372335</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.908</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.155810</td>\n",
       "      <td>0.244745</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.282</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>0.040247</td>\n",
       "      <td>0.181372</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.204</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>0.064113</td>\n",
       "      <td>0.282747</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.372</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>0.037161</td>\n",
       "      <td>0.168264</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.374</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>0.121071</td>\n",
       "      <td>0.524684</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.688</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>0.323223</td>\n",
       "      <td>0.600301</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.942</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price      area  length  width  bedrooms  bathrooms  stories  \\\n",
       "0    0.287069  0.288093   0.586  0.224         4          2        3   \n",
       "1    0.663195  0.569795   0.868  0.464         4          4        4   \n",
       "2    0.299319  0.549533   0.396  0.926         3          2        2   \n",
       "3    0.215885  0.372335   0.164  0.908         4          2        2   \n",
       "4    0.155810  0.244745   0.406  0.282         4          1        2   \n",
       "..        ...       ...     ...    ...       ...        ...      ...   \n",
       "540  0.040247  0.181372   0.340  0.204         2          1        1   \n",
       "541  0.064113  0.282747   0.394  0.372         3          1        1   \n",
       "542  0.037161  0.168264   0.148  0.374         2          1        1   \n",
       "543  0.121071  0.524684   0.540  0.688         3          1        1   \n",
       "544  0.323223  0.600301   0.454  0.942         3          1        2   \n",
       "\n",
       "     mainroad  guestroom  basement  hotwaterheating  airconditioning  parking  \\\n",
       "0           1          0         0                0                1        2   \n",
       "1           1          0         0                0                1        3   \n",
       "2           1          0         1                0                0        2   \n",
       "3           1          0         1                0                1        3   \n",
       "4           1          1         1                0                1        2   \n",
       "..        ...        ...       ...              ...              ...      ...   \n",
       "540         1          0         1                0                0        2   \n",
       "541         0          0         0                0                0        0   \n",
       "542         1          0         0                0                0        0   \n",
       "543         0          0         0                0                0        0   \n",
       "544         1          0         0                0                0        0   \n",
       "\n",
       "     prefarea  furnishingstatus  \n",
       "0           1                 0  \n",
       "1           0                 0  \n",
       "2           1                 1  \n",
       "3           1                 0  \n",
       "4           0                 0  \n",
       "..        ...               ...  \n",
       "540         0                 2  \n",
       "541         0                 1  \n",
       "542         0                 2  \n",
       "543         0                 0  \n",
       "544         0                 2  \n",
       "\n",
       "[545 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2e8e9a",
   "metadata": {},
   "source": [
    "#### Evaluate your results, was normalization helpful? If yes then which one outperformed rest and why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ef4ef99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_scale = data.drop('furnishingstatus',axis=1)\n",
    "y_scale = data['furnishingstatus']\n",
    "\n",
    "X_train_scale, X_test_scale, y_train_scale, y_test_scale = train_test_split(X_scale, y_scale, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6362885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.03669724770643"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train_scale, y_train_scale)\n",
    "predictions_scale = gnb.predict(X_test_scale)\n",
    "\n",
    "accuracy_score(y_test_scale, predictions_scale) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1adfe16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization was extremely helpful and it outperformed the old results because it reduced data points to range 0-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7f649e",
   "metadata": {},
   "source": [
    "#### Plot the area w.r.t to its independent variables in 2D and 3D "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6313ec47",
   "metadata": {},
   "outputs": [
    {
     "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",
    "\n",
    "x_axis = data['price']\n",
    "y_axis = data['area']\n",
    "\n",
    "plt.scatter(x_axis, y_axis ,c=\"g\")\n",
    "plt.title('Area - Price Ratio')\n",
    "\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Area')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7bc830e9",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.scatter(data[\"price\"], data[\"width\"], data[\"length\"])\n",
    "\n",
    "ax.set_xlabel('Price')\n",
    "ax.set_ylabel('Width')\n",
    "ax.set_zlabel('Length')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e414c451",
   "metadata": {},
   "source": [
    "#### Find the covariance &amp; correlation matrix of the normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e89081c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>price</th>\n",
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       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.022906</td>\n",
       "      <td>0.020038</td>\n",
       "      <td>0.019557</td>\n",
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       "      <th>area</th>\n",
       "      <td>0.020038</td>\n",
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       "      <th>length</th>\n",
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       "      <td>0.083608</td>\n",
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       "      <td>0.001580</td>\n",
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       "      <td>-0.007210</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>-0.012196</td>\n",
       "      <td>-0.033321</td>\n",
       "      <td>-0.009220</td>\n",
       "      <td>-0.007908</td>\n",
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       "      <th>bedrooms</th>\n",
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       "      <td>0.022760</td>\n",
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       "      <td>0.088562</td>\n",
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       "      <th>bathrooms</th>\n",
       "      <td>0.021006</td>\n",
       "      <td>0.004682</td>\n",
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       "      <td>0.004671</td>\n",
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       "      <td>0.024501</td>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.043689</td>\n",
       "      <td>0.076842</td>\n",
       "      <td>0.013532</td>\n",
       "      <td>-0.054921</td>\n",
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       "      <th>stories</th>\n",
       "      <td>0.097208</td>\n",
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       "      <th>mainroad</th>\n",
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       "      <th>basement</th>\n",
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       "      <td>-0.007210</td>\n",
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       "      <td>0.068025</td>\n",
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       "      <td>0.046216</td>\n",
       "      <td>-0.041025</td>\n",
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       "      <th>hotwaterheating</th>\n",
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       "      <td>0.742330</td>\n",
       "      <td>0.033496</td>\n",
       "      <td>-0.116463</td>\n",
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       "      <td>0.000530</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>-0.009220</td>\n",
       "      <td>0.024747</td>\n",
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       "      <td>0.180032</td>\n",
       "      <td>-0.034788</td>\n",
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       "      <th>furnishingstatus</th>\n",
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       "      <td>-0.000004</td>\n",
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      ],
      "text/plain": [
       "                     price      area    length     width  bedrooms  bathrooms  \\\n",
       "price             0.022906  0.020038  0.019557  0.017820  0.037506   0.021006   \n",
       "area              0.020038  0.047448  0.044980  0.042667  0.005443   0.004682   \n",
       "length            0.019557  0.044980  0.088036 -0.001806  0.015686   0.006043   \n",
       "width             0.017820  0.042667 -0.001806  0.083608 -0.000140   0.004671   \n",
       "bedrooms          0.037506  0.005443  0.015686 -0.000140  0.544738   0.138674   \n",
       "bathrooms         0.021006  0.004682  0.006043  0.004671  0.138674   0.252476   \n",
       "stories           0.097208 -0.000091  0.002222  0.001580  0.261589   0.142171   \n",
       "mainroad          0.003696 -0.004424 -0.005035 -0.002583 -0.003096   0.007427   \n",
       "guestroom         0.002196 -0.000327  0.003135 -0.004354  0.022760   0.024329   \n",
       "basement         -0.012477 -0.005379 -0.002691 -0.007210  0.034299   0.024501   \n",
       "hotwaterheating   0.000444  0.000157 -0.003226  0.003076  0.007117   0.007066   \n",
       "airconditioning   0.015832 -0.001547  0.012609 -0.012196  0.055140   0.043689   \n",
       "parking          -0.007205 -0.023454 -0.007975 -0.033321  0.088562   0.076842   \n",
       "prefarea          0.000530 -0.004157  0.000598 -0.009220  0.024747   0.013532   \n",
       "furnishingstatus -0.011191 -0.004181 -0.000004 -0.007908 -0.069256  -0.054921   \n",
       "\n",
       "                   stories  mainroad  guestroom  basement  hotwaterheating  \\\n",
       "price             0.097208  0.003696   0.002196 -0.012477         0.000444   \n",
       "area             -0.000091 -0.004424  -0.000327 -0.005379         0.000157   \n",
       "length            0.002222 -0.005035   0.003135 -0.002691        -0.003226   \n",
       "width             0.001580 -0.002583  -0.004354 -0.007210         0.003076   \n",
       "bedrooms          0.261589 -0.003096   0.022760  0.034299         0.007117   \n",
       "bathrooms         0.142171  0.007427   0.024329  0.024501         0.007066   \n",
       "stories           0.752543  0.036809   0.014460 -0.071418         0.003424   \n",
       "mainroad          0.036809  0.121546   0.012325  0.007326        -0.000860   \n",
       "guestroom         0.014460  0.012325   0.146573  0.068025        -0.000826   \n",
       "basement         -0.071418  0.007326   0.068025  0.228056         0.000438   \n",
       "hotwaterheating   0.003424 -0.000860  -0.000826  0.000438         0.043848   \n",
       "airconditioning   0.118480  0.017097   0.024609  0.010517        -0.012665   \n",
       "parking           0.034043  0.061407   0.012358  0.021189         0.012244   \n",
       "prefarea          0.016352  0.029567   0.026137  0.046216        -0.005279   \n",
       "furnishingstatus -0.069135 -0.041601  -0.034491 -0.041025        -0.005042   \n",
       "\n",
       "                  airconditioning   parking  prefarea  furnishingstatus  \n",
       "price                    0.015832 -0.007205  0.000530         -0.011191  \n",
       "area                    -0.001547 -0.023454 -0.004157         -0.004181  \n",
       "length                   0.012609 -0.007975  0.000598         -0.000004  \n",
       "width                   -0.012196 -0.033321 -0.009220         -0.007908  \n",
       "bedrooms                 0.055140  0.088562  0.024747         -0.069256  \n",
       "bathrooms                0.043689  0.076842  0.013532         -0.054921  \n",
       "stories                  0.118480  0.034043  0.016352         -0.069135  \n",
       "mainroad                 0.017097  0.061407  0.029567         -0.041601  \n",
       "guestroom                0.024609  0.012358  0.026137         -0.034491  \n",
       "basement                 0.010517  0.021189  0.046216         -0.041025  \n",
       "hotwaterheating         -0.012665  0.012244 -0.005279         -0.005042  \n",
       "airconditioning          0.216392  0.063795  0.023169         -0.053295  \n",
       "parking                  0.063795  0.742330  0.033496         -0.116463  \n",
       "prefarea                 0.023169  0.033496  0.180032         -0.034788  \n",
       "furnishingstatus        -0.053295 -0.116463 -0.034788          0.579688  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd3bd270",
   "metadata": {},
   "outputs": [
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       "      <td>-0.031198</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>-0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>0.407210</td>\n",
       "      <td>0.677425</td>\n",
       "      <td>-0.021047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000656</td>\n",
       "      <td>0.032149</td>\n",
       "      <td>0.006299</td>\n",
       "      <td>-0.025620</td>\n",
       "      <td>-0.039328</td>\n",
       "      <td>-0.052216</td>\n",
       "      <td>0.050802</td>\n",
       "      <td>-0.090670</td>\n",
       "      <td>-0.133752</td>\n",
       "      <td>-0.075153</td>\n",
       "      <td>-0.035919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.335762</td>\n",
       "      <td>0.033856</td>\n",
       "      <td>0.071629</td>\n",
       "      <td>-0.000656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373930</td>\n",
       "      <td>0.408564</td>\n",
       "      <td>-0.012033</td>\n",
       "      <td>0.080549</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.160603</td>\n",
       "      <td>0.139270</td>\n",
       "      <td>0.079023</td>\n",
       "      <td>-0.123244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.276222</td>\n",
       "      <td>0.042773</td>\n",
       "      <td>0.040536</td>\n",
       "      <td>0.032149</td>\n",
       "      <td>0.373930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326165</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.126469</td>\n",
       "      <td>0.102106</td>\n",
       "      <td>0.067159</td>\n",
       "      <td>0.186915</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>-0.143559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stories</th>\n",
       "      <td>0.740398</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.008633</td>\n",
       "      <td>0.006299</td>\n",
       "      <td>0.408564</td>\n",
       "      <td>0.326165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121706</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>-0.172394</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>0.293602</td>\n",
       "      <td>0.045547</td>\n",
       "      <td>0.044425</td>\n",
       "      <td>-0.104672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainroad</th>\n",
       "      <td>0.070040</td>\n",
       "      <td>-0.058259</td>\n",
       "      <td>-0.048675</td>\n",
       "      <td>-0.025620</td>\n",
       "      <td>-0.012033</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.121706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092337</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>0.105423</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.199876</td>\n",
       "      <td>-0.156726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guestroom</th>\n",
       "      <td>0.037897</td>\n",
       "      <td>-0.003926</td>\n",
       "      <td>0.027602</td>\n",
       "      <td>-0.039328</td>\n",
       "      <td>0.080549</td>\n",
       "      <td>0.126469</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>0.092337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.372066</td>\n",
       "      <td>-0.010308</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>0.160897</td>\n",
       "      <td>-0.118328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basement</th>\n",
       "      <td>-0.172628</td>\n",
       "      <td>-0.051712</td>\n",
       "      <td>-0.018991</td>\n",
       "      <td>-0.052216</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.102106</td>\n",
       "      <td>-0.172394</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>0.372066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.047341</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.228083</td>\n",
       "      <td>-0.112831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hotwaterheating</th>\n",
       "      <td>0.014013</td>\n",
       "      <td>0.003432</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>0.050802</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.067159</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>-0.010308</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130023</td>\n",
       "      <td>0.067864</td>\n",
       "      <td>-0.059411</td>\n",
       "      <td>-0.031628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airconditioning</th>\n",
       "      <td>0.224880</td>\n",
       "      <td>-0.015263</td>\n",
       "      <td>0.091354</td>\n",
       "      <td>-0.090670</td>\n",
       "      <td>0.160603</td>\n",
       "      <td>0.186915</td>\n",
       "      <td>0.293602</td>\n",
       "      <td>0.105423</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.047341</td>\n",
       "      <td>-0.130023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>0.117382</td>\n",
       "      <td>-0.150477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parking</th>\n",
       "      <td>-0.055252</td>\n",
       "      <td>-0.124971</td>\n",
       "      <td>-0.031198</td>\n",
       "      <td>-0.133752</td>\n",
       "      <td>0.139270</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.045547</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.067864</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091627</td>\n",
       "      <td>-0.177539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prefarea</th>\n",
       "      <td>0.008255</td>\n",
       "      <td>-0.044978</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>-0.075153</td>\n",
       "      <td>0.079023</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>0.044425</td>\n",
       "      <td>0.199876</td>\n",
       "      <td>0.160897</td>\n",
       "      <td>0.228083</td>\n",
       "      <td>-0.059411</td>\n",
       "      <td>0.117382</td>\n",
       "      <td>0.091627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.107686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furnishingstatus</th>\n",
       "      <td>-0.097114</td>\n",
       "      <td>-0.025209</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.035919</td>\n",
       "      <td>-0.123244</td>\n",
       "      <td>-0.143559</td>\n",
       "      <td>-0.104672</td>\n",
       "      <td>-0.156726</td>\n",
       "      <td>-0.118328</td>\n",
       "      <td>-0.112831</td>\n",
       "      <td>-0.031628</td>\n",
       "      <td>-0.150477</td>\n",
       "      <td>-0.177539</td>\n",
       "      <td>-0.107686</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price      area    length     width  bedrooms  bathrooms  \\\n",
       "price             1.000000  0.607827  0.435523  0.407210  0.335762   0.276222   \n",
       "area              0.607827  1.000000  0.695952  0.677425  0.033856   0.042773   \n",
       "length            0.435523  0.695952  1.000000 -0.021047  0.071629   0.040536   \n",
       "width             0.407210  0.677425 -0.021047  1.000000 -0.000656   0.032149   \n",
       "bedrooms          0.335762  0.033856  0.071629 -0.000656  1.000000   0.373930   \n",
       "bathrooms         0.276222  0.042773  0.040536  0.032149  0.373930   1.000000   \n",
       "stories           0.740398 -0.000482  0.008633  0.006299  0.408564   0.326165   \n",
       "mainroad          0.070040 -0.058259 -0.048675 -0.025620 -0.012033   0.042398   \n",
       "guestroom         0.037897 -0.003926  0.027602 -0.039328  0.080549   0.126469   \n",
       "basement         -0.172628 -0.051712 -0.018991 -0.052216  0.097312   0.102106   \n",
       "hotwaterheating   0.014013  0.003432 -0.051918  0.050802  0.046049   0.067159   \n",
       "airconditioning   0.224880 -0.015263  0.091354 -0.090670  0.160603   0.186915   \n",
       "parking          -0.055252 -0.124971 -0.031198 -0.133752  0.139270   0.177496   \n",
       "prefarea          0.008255 -0.044978  0.004749 -0.075153  0.079023   0.063472   \n",
       "furnishingstatus -0.097114 -0.025209 -0.000019 -0.035919 -0.123244  -0.143559   \n",
       "\n",
       "                   stories  mainroad  guestroom  basement  hotwaterheating  \\\n",
       "price             0.740398  0.070040   0.037897 -0.172628         0.014013   \n",
       "area             -0.000482 -0.058259  -0.003926 -0.051712         0.003432   \n",
       "length            0.008633 -0.048675   0.027602 -0.018991        -0.051918   \n",
       "width             0.006299 -0.025620  -0.039328 -0.052216         0.050802   \n",
       "bedrooms          0.408564 -0.012033   0.080549  0.097312         0.046049   \n",
       "bathrooms         0.326165  0.042398   0.126469  0.102106         0.067159   \n",
       "stories           1.000000  0.121706   0.043538 -0.172394         0.018847   \n",
       "mainroad          0.121706  1.000000   0.092337  0.044002        -0.011781   \n",
       "guestroom         0.043538  0.092337   1.000000  0.372066        -0.010308   \n",
       "basement         -0.172394  0.044002   0.372066  1.000000         0.004385   \n",
       "hotwaterheating   0.018847 -0.011781  -0.010308  0.004385         1.000000   \n",
       "airconditioning   0.293602  0.105423   0.138179  0.047341        -0.130023   \n",
       "parking           0.045547  0.204433   0.037466  0.051497         0.067864   \n",
       "prefarea          0.044425  0.199876   0.160897  0.228083        -0.059411   \n",
       "furnishingstatus -0.104672 -0.156726  -0.118328 -0.112831        -0.031628   \n",
       "\n",
       "                  airconditioning   parking  prefarea  furnishingstatus  \n",
       "price                    0.224880 -0.055252  0.008255         -0.097114  \n",
       "area                    -0.015263 -0.124971 -0.044978         -0.025209  \n",
       "length                   0.091354 -0.031198  0.004749         -0.000019  \n",
       "width                   -0.090670 -0.133752 -0.075153         -0.035919  \n",
       "bedrooms                 0.160603  0.139270  0.079023         -0.123244  \n",
       "bathrooms                0.186915  0.177496  0.063472         -0.143559  \n",
       "stories                  0.293602  0.045547  0.044425         -0.104672  \n",
       "mainroad                 0.105423  0.204433  0.199876         -0.156726  \n",
       "guestroom                0.138179  0.037466  0.160897         -0.118328  \n",
       "basement                 0.047341  0.051497  0.228083         -0.112831  \n",
       "hotwaterheating         -0.130023  0.067864 -0.059411         -0.031628  \n",
       "airconditioning          1.000000  0.159173  0.117382         -0.150477  \n",
       "parking                  0.159173  1.000000  0.091627         -0.177539  \n",
       "prefarea                 0.117382  0.091627  1.000000         -0.107686  \n",
       "furnishingstatus        -0.150477 -0.177539 -0.107686          1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a929ded2",
   "metadata": {},
   "source": [
    "#### Find the correlation of length and area and plot it,."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c3bf7c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = data[['length' , 'area']]\n",
    "\n",
    "correlation = correlation.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "271ae2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(correlation , annot=True)\n",
    "\n",
    "plt.yticks(rotation=0)  \n",
    "plt.xticks(rotation=90)  \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c041271c",
   "metadata": {},
   "source": [
    "#### Assuming 3 friends have 5 cities (A,B,C,D,E) to go to, what is the possible state space of this problem? Display the possible state space in a proper way. I&#39;m interested in the random variable where 2 person go to the same city."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23078513",
   "metadata": {},
   "source": [
    "#### Find the probability of the occurrence of that event "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "721dcb04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "friends = []\n",
    "alphabets = ['A','B','C','D','E']\n",
    "\n",
    "for i in alphabets:\n",
    "    for j in alphabets:\n",
    "        for k in alphabets:\n",
    "            \n",
    "            combination = i + j + k\n",
    "            friends.append(combination)\n",
    "            \n",
    "len(friends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e765e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "same_city = 0\n",
    "all_different_city = 0\n",
    "\n",
    "for i in friends:\n",
    "    \n",
    "    if (i[0] == i[1]) or (i[0] == i[2]) or (i[1] == i[2]):\n",
    "            same_city += 1\n",
    "    if (i[0] != i[1]) and (i[0] != i[2]) and (i[1] != i[2]):\n",
    "        all_different_city += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f71a6d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_same_city = same_city/len(friends)\n",
    "probability_same_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2e86723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability_different_city = all_different_city/len(friends)\n",
    "probability_different_city"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a2468a",
   "metadata": {},
   "source": [
    "#### Plot the probability distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "010688f3",
   "metadata": {},
   "outputs": [
    {
     "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",
    "   \n",
    "city = ['Same City','Different City']\n",
    "\n",
    "probability = []\n",
    "probability.append(probability_same_city)\n",
    "probability.append(probability_different_city)\n",
    "\n",
    "plt.bar(city, probability)\n",
    "\n",
    "plt.title('Probability Distribution')\n",
    "plt.xlabel('Combination')\n",
    "plt.ylabel('Probabilities')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128a3add",
   "metadata": {},
   "source": [
    "#### Is it a discrete distribution or a continuous one?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5bd5edf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is a descrete distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c385094b",
   "metadata": {},
   "source": [
    "#### Is it univariate or a multivariate problem?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2af54202",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is a multivariate problem"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}