From 1cb6f073e5dfb85fcac6d480f9440126974f80de Mon Sep 17 00:00:00 2001
From: codealpha07 <codealpha786@gmail.com>
Date: Fri, 28 Jun 2024 00:46:12 +0530
Subject: [PATCH] Outliers Done ; DataSet refining Done

---
 Model/Prediction_Model.ipynb | 546 ++++++++++++++++++++++++++++++-----
 1 file changed, 478 insertions(+), 68 deletions(-)

diff --git a/Model/Prediction_Model.ipynb b/Model/Prediction_Model.ipynb
index e3d8563..2725ca7 100644
--- a/Model/Prediction_Model.ipynb
+++ b/Model/Prediction_Model.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 300,
+   "execution_count": 524,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 525,
    "metadata": {},
    "outputs": [
     {
@@ -157,7 +157,7 @@
        "4      NaN       1200   2.0      1.0   51.00  "
       ]
      },
-     "execution_count": 301,
+     "execution_count": 525,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": 526,
    "metadata": {},
    "outputs": [
     {
@@ -180,7 +180,7 @@
        "       'Carpet  Area'], dtype=object)"
       ]
      },
-     "execution_count": 302,
+     "execution_count": 526,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": 527,
    "metadata": {},
    "outputs": [
     {
@@ -205,7 +205,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 303,
+     "execution_count": 527,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 304,
+   "execution_count": 528,
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +308,7 @@
        "4                  Kothanur      2 BHK       1200   2.0   51.00"
       ]
      },
-     "execution_count": 304,
+     "execution_count": 528,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 529,
    "metadata": {},
    "outputs": [
     {
@@ -341,7 +341,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 305,
+     "execution_count": 529,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 530,
    "metadata": {},
    "outputs": [
     {
@@ -362,7 +362,7 @@
        "(13246, 5)"
       ]
      },
-     "execution_count": 306,
+     "execution_count": 530,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 307,
+   "execution_count": 531,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +393,7 @@
        "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
       ]
      },
-     "execution_count": 307,
+     "execution_count": 531,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -419,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": 532,
    "metadata": {},
    "outputs": [
     {
@@ -522,7 +522,7 @@
        "4                  Kothanur      2 BHK       1200   2.0   51.00            2"
       ]
      },
-     "execution_count": 308,
+     "execution_count": 532,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -534,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": 533,
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 310,
+   "execution_count": 534,
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +565,7 @@
        "      dtype=object)"
       ]
      },
-     "execution_count": 310,
+     "execution_count": 534,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 311,
+   "execution_count": 535,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 312,
+   "execution_count": 536,
    "metadata": {},
    "outputs": [
     {
@@ -741,7 +741,7 @@
        "[13056 rows x 5 columns]"
       ]
      },
-     "execution_count": 312,
+     "execution_count": 536,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -752,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 313,
+   "execution_count": 537,
    "metadata": {},
    "outputs": [
     {
@@ -927,7 +927,7 @@
        "886             Chandapura       520 - 645   1.0   15.135            1"
       ]
      },
-     "execution_count": 313,
+     "execution_count": 537,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -945,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": 538,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -963,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
+   "execution_count": 539,
    "metadata": {},
    "outputs": [
     {
@@ -1145,7 +1145,7 @@
        "14                  Sarjapur      2250.0   3.0  148.00            3"
       ]
      },
-     "execution_count": 315,
+     "execution_count": 539,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 316,
+   "execution_count": 540,
    "metadata": {},
    "outputs": [
     {
@@ -1275,7 +1275,7 @@
        "4     4250.000000  "
       ]
      },
-     "execution_count": 316,
+     "execution_count": 540,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1289,7 +1289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 541,
    "metadata": {},
    "outputs": [
     {
@@ -1306,7 +1306,7 @@
        "Name: Price_per_sqft, dtype: float64"
       ]
      },
-     "execution_count": 317,
+     "execution_count": 541,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 318,
+   "execution_count": 542,
    "metadata": {},
    "outputs": [
     {
@@ -1328,7 +1328,7 @@
        "1298"
       ]
      },
-     "execution_count": 318,
+     "execution_count": 542,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1353,7 +1353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 319,
+   "execution_count": 543,
    "metadata": {},
    "outputs": [
     {
@@ -1374,7 +1374,7 @@
        "Name: count, Length: 1298, dtype: int64"
       ]
      },
-     "execution_count": 319,
+     "execution_count": 543,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1394,7 +1394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 544,
    "metadata": {},
    "outputs": [
     {
@@ -1428,7 +1428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 321,
+   "execution_count": 545,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1437,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 322,
+   "execution_count": 546,
    "metadata": {},
    "outputs": [
     {
@@ -1535,7 +1535,7 @@
        "4     4250.000000  "
       ]
      },
-     "execution_count": 322,
+     "execution_count": 546,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1547,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 547,
    "metadata": {},
    "outputs": [
     {
@@ -1556,7 +1556,7 @@
        "241"
       ]
      },
-     "execution_count": 323,
+     "execution_count": 547,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1588,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 324,
+   "execution_count": 548,
    "metadata": {},
    "outputs": [
     {
@@ -1742,7 +1742,7 @@
        "[744 rows x 6 columns]"
       ]
      },
-     "execution_count": 324,
+     "execution_count": 548,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1753,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": 549,
    "metadata": {},
    "outputs": [
     {
@@ -1762,7 +1762,7 @@
        "(12456, 6)"
       ]
      },
-     "execution_count": 325,
+     "execution_count": 549,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1776,12 +1776,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<b><h4 style=\"color:green;\">Outlier Removal Using Standard Deviation and Mean</h4></b>"
+    "<b><h4 style=\"color:green;\">Price Per Sqft Outlier Removal : Using Standard Deviation and Mean</h4></b>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": 550,
    "metadata": {},
    "outputs": [
     {
@@ -1798,7 +1798,7 @@
        "Name: Price_per_sqft, dtype: float64"
       ]
      },
-     "execution_count": 326,
+     "execution_count": 550,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1825,7 +1825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": 551,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1843,7 +1843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": 552,
    "metadata": {},
    "outputs": [
     {
@@ -1852,7 +1852,7 @@
        "(10245, 6)"
       ]
      },
-     "execution_count": 328,
+     "execution_count": 552,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1871,14 +1871,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 329,
+   "execution_count": 553,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_scatter_pps(df, given_location):\n",
     "    bhk_2 = df[(df.location == given_location) & (df.Size_in_BHK==2)]\n",
     "    bhk_3 = df[(df.location == given_location) & (df.Size_in_BHK==3)]\n",
-    "    matplotlib.rcParams['figure.figsize'] = (12,8)\n",
+    "    matplotlib.rcParams['figure.figsize'] = (12,7)\n",
     "    plt.scatter(bhk_2.total_sqft, bhk_2.price, color=\"blue\", label = \"2 BHK\", s=50)\n",
     "    plt.scatter(bhk_3.total_sqft,bhk_3.price,marker='+', color='green',label='3 BHK', s=50)\n",
     "    plt.xlabel(\"Total Square Feet Area\", fontsize=13, color=\"darkslategray\", fontweight=1000, loc=\"center\", labelpad=20)\n",
@@ -1889,14 +1889,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 330,
+   "execution_count": 554,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "<Figure size 1200x700 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1909,14 +1909,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 331,
+   "execution_count": 555,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "<Figure size 1200x700 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1945,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": 556,
    "metadata": {},
    "outputs": [
     {
@@ -1954,7 +1954,7 @@
        "(7331, 6)"
       ]
      },
-     "execution_count": 332,
+     "execution_count": 556,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1984,14 +1984,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
+   "execution_count": 557,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "<Figure size 1200x700 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2004,14 +2004,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 334,
+   "execution_count": 558,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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JvV2BoLxRAXPnSuPHO4OQvDz/vCcAAACAsjGCAKhGbDbPwgHJ2c5m8997p6V51z4vzzkNYtw4z3/V92RUQHKyMxwwTfdwwNf3BAAAAOB/BARAJbNYnCMDPBEU5GwfaJ5Od/C08GIgp1gAAAAA8AwBAVDJIiKk4cOdQ+nLEhIi3Xqrf4v2xcb6dlxenpSU5Jw2UBpPRwWsWiUtWlS8jS/vCQAAAKDyEBAAVWDSJOc8+7Lk50uPPurf942I8GwFg5KUN93hr38tf2REcLBz9ECgplgAAAAA8BwBAVAFevVyFuEzjOIjCUJCnNvnzPH/EoeSNHOmb8eVNt0hOdk5IuLzz8sPPfLypC+/dF5fRd4TAAAAQOUjIACqyNix0rp1zpvrwl/eg4Kcr9etq7wK/r16OWsFeKO06Q6FNQeWLPH8XAUF0o03BmaKBQAAAADPscxhFWKZw4opbxm9msQf1+LtOdavdxYB/Oyz8tsahjO0KDqiITnZGQ54+40RFCQtWyYNGlT2sSW9JwAAAICKYZlD1CqeLKNX00REOAsI+hIO+Pr36NlT+vRTKStLev5576c7eLISwcUKRwUMGBC4KRYAAAAAPENAgGrN02X06oqK/D0cDiktzfn88ce9m+7gcHi2EsHFihZeDNQUCwAAAACeISBAteXpMno1eSSBN3z9e5Q24mD7dme4UDjs3zRLX23AZvN8JQLJOdKgpFEBhaMYMjOl1FTnv59+ysgBAAAAoDogIEC15cmQ9uBg57z6usCXv0dpIw4WLXKGDYsWuQcEpY1EsFjKX9KwqGHDyh4VUJEpFgAAAAAqB0UKqxBFCj3ncDh/6fbkV+ugIOcv0bX5ZtOXv8eWLb4VFZRKLhiYmOgMEMqaZhAc7AwHkpK8f08AAAAA/keRQtR43gxpLyhwtq/NfPl7+FJUsFBJIzMmTXLWFCjvvadM8e09AQAAAAQWAQGqJW+GtAcFOdvXZt7+PUJDnb/ie1tUsFBenvN4h+PCtl69WIkAAAAAqM0ICFAtRUQ4q9tffCN6seBg5zJ6tXl6geT536NwWcEHHqj4e5Y0MoOVCAAAAIDai4AA1ZYnQ9rz86WTJ+vGSgae/j1atZIWLqz4+5U2MoOVCAAAAIDaiYAA1VZZQ9qL2rCh5Mr7tY2nQ/znz6/4exWORChrZAYrEQAAAAC1CwHBedOnT5dhGG6PuLg4137TNDV9+nTFx8crIiJCN9xwg3bv3h3AHtcNhUPay/p1Oi/PWal/3LjKHUngcEhpae7z8qtaeUP8R46UUlIq/j75+dKjj1b8PAAAAABqDgKCItq1a6eUlBTXY+fOna59L7zwgmbPnq3XXntNmzZtUlxcnAYNGqSMjIwA9rhu6NlTatTIs3oEF1fe94fkZOcSf1FRUlyc89/ExMBNayhriP/x496dyzDcX1NsEAAAAKi7CAiKCAkJUVxcnOvRtGlTSc7RAy+//LKeeuopJSYmKiEhQe+//76ysrI0b968APe69nM4pEWLyq/IX1Ll/YqaO1fq00davPjCMoMFBc7XgZ7WUNIQ//h4785x880UGwQAAADgREBQxI8//qj4+Hi1adNGd955p37++WdJ0oEDB5SamqrBgwe72oaHh6tv377asGFDqefLzs6WzWZze8B7NtuFm/PylFR531fJydL48c7pCxeHE1U1rcFbjRpJzZp51jY+Xvr8c4oNAgAAAHAiIDiva9eu+te//qWvvvpKb731llJTU9WjRw+dPn1aqampkqTY2Fi3Y2JjY137SjJr1ixZrVbXo2XLlpV6DbWVxXLhV+7ylFZ53xezZzunLZSlsqY1VMT/+3/etaPYIAAAAACJgMBl6NChuu2229S+fXsNHDhQS5YskSS9//77rjbGRRO2TdMstq2oqVOnKj093fU4cuRI5XS+louIcA59L68GgSeV9z0VyGkNFTVunHTXXWW3uesu6eGHq6Y/AAAAAGoGAoJSREZGqn379vrxxx9dqxlcPFrgxIkTxUYVFBUeHi6LxeL2gG8mTXJW1i+LPyvvB2pag7/Mm+csNHhxTYL4eOd2SmcAAAAAuBgBQSmys7P1ww8/qFmzZmrTpo3i4uK0fPly1/6cnBytWbNGPXr0CGAv645evZw3toZRfCRBZVTeD9S0Bn96+GHp2DHp9Glp507nv8eOMXIAAAAAQMkICM6bMmWK1qxZowMHDujbb7/V7bffLpvNptGjR8swDE2cOFEzZ85UUlKSdu3apTFjxqh+/fq6++67A931OmPsWGeF/eHDK7/yfiCmNVSWRo2khATnvwAAAABQmnJuf+qOo0eP6q677tKpU6fUtGlTdevWTRs3blTr1q0lSY8//rgcDofGjRunM2fOqGvXrlq2bJmio6MD3PO6pWdP58PhcA7rt1gq7+Z80iRp4cKy2/hzWgMAAAAABJJhmqYZ6E7UFTabTVarVenp6dQjqCFef91Z9C842L1gYUiIMxyYM8e/IxcAAAAAwJ+8uQ9ligGKcTiktLTqVZk/UKpyWgMAAAAABBIBAVySk6XERCkqSoqLc/6bmCitXx/ongVWz57Sp59KmZlSaqrz308/9V9BRAAAAACoDggIIEmaO1fq00davPjC8n4FBc7XvXs7h9rXdRERUmxs9S5ICAAAAAC+IiCAkpOl8eMl03SfZy85X5umcx5+XR9JAAAAAAC1GQEBNHu2swhfWYKDpZdeqpr+AAAAAACqHgFBHedwSIsWFR85cLG8PCkpicKFAAAAAFBbERDUcTbbhZoD5SkocLYHAAAAANQ+BAR1nMVyYfm+8gQFOdsDAAAAAGofAoI6LiJCGj5cCgkpu11IiHTrrVTwBwAAAIDaioAAmjRJys8vu01+vvToo1XTHwAAAABA1SMggHr1kubMkQyj+EiCkBDn9jlzpJ49A9M/AAAAAEDlIyCAJGnsWGndOud0g8KaBEFBztfr1jn3AwAAAABqr3JmnqMu6dnT+XA4nKsVWCzUHAAAAACAuoKAAMVERBAMAAAAAEBdwxQDAAAAAABAQAAAAAAAAAgIAAAAAACACAgAAAAAAIAICAAAAAAAgAgIAAAAAACACAgAAAAAAIAICAAAAAAAgAgIAAAAAACACAgAAAAAAIAICAAAAAAAgAgIAAAAAACACAgAAAAAAIAICAAAAAAAgAgIAAAAAACACAgAAAAAAICkkEB3oC4xTVOSZLPZAtwTAAAAAEBdUHj/WXg/WhYCgiqUkZEhSWrZsmWAewIAAAAAqEsyMjJktVrLbGOYnsQI8IuCggIdP35c0dHRMgwj0N1BNWOz2dSyZUsdOXJEFosl0N1BLcBnCv7GZwr+xmcKlYHPFfytpn+mTNNURkaG4uPjFRRUdpUBRhBUoaCgILVo0SLQ3UA1Z7FYauQXD6ovPlPwNz5T8Dc+U6gMfK7gbzX5M1XeyIFCFCkEAAAAAAAEBAAAAAAAgIAAqDbCw8M1bdo0hYeHB7orqCX4TMHf+EzB3/hMoTLwuYK/1aXPFEUKAQAAAAAAIwgAAAAAAAABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAeBXa9eu1bBhwxQfHy/DMLRw4UK3/aZpavr06YqPj1dERIRuuOEG7d69261Ndna2JkyYoCZNmigyMlK33HKLjh496tbmzJkzuu+++2S1WmW1WnXffffp7NmzlXx1CITyPlNjxoyRYRhuj27durm14TOFQrNmzVKXLl0UHR2tmJgYjRgxQvv27XNrw/cUvOXJ54rvKnhj7ty56tChgywWiywWi7p3764vv/zStZ/vKXirvM8U31EXEBAAfmS329WxY0e99tprJe5/4YUXNHv2bL322mvatGmT4uLiNGjQIGVkZLjaTJw4UUlJSZo/f76Sk5OVmZmpm2++Wfn5+a42d999t7Zt26alS5dq6dKl2rZtm+67775Kvz5UvfI+U5J04403KiUlxfX44osv3PbzmUKhNWvWaPz48dq4caOWL1+uvLw8DR48WHa73dWG7yl4y5PPlcR3FTzXokULPffcc9q8ebM2b96s/v37a/jw4a4QgO8peKu8z5TEd5SLCaBSSDKTkpJcrwsKCsy4uDjzueeec207d+6cabVazddff900TdM8e/asGRoaas6fP9/V5tixY2ZQUJC5dOlS0zRNc8+ePaYkc+PGja4233zzjSnJ3Lt3byVfFQLp4s+UaZrm6NGjzeHDh5d6DJ8plOXEiROmJHPNmjWmafI9Bf+4+HNlmnxXoeIaNmxo/vOf/+R7Cn5T+JkyTb6jimIEAVBFDhw4oNTUVA0ePNi1LTw8XH379tWGDRskSVu2bFFubq5bm/j4eCUkJLjafPPNN7JarerataurTbdu3WS1Wl1tULesXr1aMTExuuKKK/Tggw/qxIkTrn18plCW9PR0SVKjRo0k8T0F/7j4c1WI7yr4Ij8/X/Pnz5fdblf37t35nkKFXfyZKsR3lFNIoDsA1BWpqamSpNjYWLftsbGxOnTokKtNWFiYGjZsWKxN4fGpqamKiYkpdv6YmBhXG9QdQ4cO1R133KHWrVvrwIEDevrpp9W/f39t2bJF4eHhfKZQKtM0NWnSJPXq1UsJCQmS+J5CxZX0uZL4roL3du7cqe7du+vcuXOKiopSUlKSrrnmGteNFt9T8FZpnymJ76iiCAiAKmYYhttr0zSLbbvYxW1Kau/JeVD7jBo1yvU8ISFBnTt3VuvWrbVkyRIlJiaWehyfKTzyyCPasWOHkpOTi+3jewq+Ku1zxXcVvHXllVdq27ZtOnv2rD777DONHj1aa9asce3newreKu0zdc011/AdVQRTDIAqEhcXJ0nFEsQTJ064UvC4uDjl5OTozJkzZbZJS0srdv6TJ08WS9NR9zRr1kytW7fWjz/+KInPFEo2YcIEff755/r666/VokUL13a+p1ARpX2uSsJ3FcoTFhamyy+/XJ07d9asWbPUsWNHvfLKK3xPwWelfaZKUpe/owgIgCrSpk0bxcXFafny5a5tOTk5WrNmjXr06CFJ6tSpk0JDQ93apKSkaNeuXa423bt3V3p6ur777jtXm2+//Vbp6emuNqi7Tp8+rSNHjqhZs2aS+EzBnWmaeuSRR7RgwQKtWrVKbdq0cdvP9xR8Ud7nqiR8V8FbpmkqOzub7yn4TeFnqiR1+juqSksiArVcRkaGuXXrVnPr1q2mJHP27Nnm1q1bzUOHDpmmaZrPPfecabVazQULFpg7d+4077rrLrNZs2amzWZznWPs2LFmixYtzBUrVpjff/+92b9/f7Njx45mXl6eq82NN95odujQwfzmm2/Mb775xmzfvr158803V/n1ovKV9ZnKyMgwJ0+ebG7YsME8cOCA+fXXX5vdu3c3mzdvzmcKJXr44YdNq9Vqrl692kxJSXE9srKyXG34noK3yvtc8V0Fb02dOtVcu3ateeDAAXPHjh3mk08+aQYFBZnLli0zTZPvKXivrM8U31HuCAgAP/r6669NScUeo0ePNk3TuYTYtGnTzLi4ODM8PNzs06ePuXPnTrdzOBwO85FHHjEbNWpkRkREmDfffLN5+PBhtzanT58277nnHjM6OtqMjo4277nnHvPMmTNVdJWoSmV9prKysszBgwebTZs2NUNDQ81WrVqZo0ePLvZ54TOFQiV9liSZ7777rqsN31PwVnmfK76r4K3777/fbN26tRkWFmY2bdrUHDBggCscME2+p+C9sj5TfEe5M0zTNKtuvAIAAAAAAKiOqEEAAAAAAAAICAAAAAAAAAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAADVxqKVK/WrESP0qxEj9MBTTwW6OwAAoI4JCXQHAACoLEMffFApJ0963P6tZ59Vl/btPW6/aOVKHT9xQpLUr2tXXXXppV730R82bN2qj5Ys0e4ff5QtM1P1wsNljY5Wy7g4XdGmje4ZNkyxjRsHpG/V1QNPPaUtu3eX2WbK/ffr3ltuqZL+7P35Z3397beSpPiYGA0fMMCn8zw7Z44+W7bM9fryVq306d//7pc+AgBqPwICAAB89PmqVa6bzPiYmIAEBPOXLNFzb73lti0zK0uZWVk6lpamjdu3q3enTgQE1dy+Awf0xscfS5I6tWvnU0CQnZOjZevXu23bf/iw9uzfr2suv9wv/QQA1G4EBACAWuvFJ55QTk6O6/XClSu1aOVKSVKThg3118cec2t/eevWVdq/inJkZ+uVDz5wvb514ED169pVoaGhSjl5Uj/89JNWfPNNAHvoH+eysxUWGqqgoMqZGdnzuuv029tvL7a9eVxcpbxfZVm1caMy7PZi2z9ftcrrgMBx7pwi6tXzV9cAADUEAQEAoNZqd9FN0cbt213PQ0NCdO0117jtz3I49M9PPtGqjRt18Ngx5eblqUnDhuqUkKDRw4er7SWXSHJOLZj26qtux0579VXXtmH9+unZP/xBu/fv17zFi/W/gwd16uxZZdjtCg0JUXxMjHpee61+c9ttamix+Hx9+w8dkuPcOUmSJSpK0x55xL3BoEF64sEHlZeX57a5oKBA7yUlKWn5cqWdPq24pk11x4036spLLtHv/vQnSVKzpk315fmRCZt27tSDTz9dbLskzf3oI9cv34XXLUlHUlL09qefat+BAzrxyy+yZWYqOChIsU2aqEv79vpNYqKax8a6znPxe7w+Y4Ze+de/tGnnTmXY7Vr74YeyREUpPz9fSStWaMnq1dp/+LAc2dlq2rChuv3qV/rt7be7ndNTjazWYp+FknyVnKyk5cu19+eflelwqEF0tDonJOj+227TFec/G0Xt/N//9O/Fi7V1zx6dTk9XRHi4rrnsMt1188264frrXe1+NWKE23Fbdu9227Zt4UKPruPzVatcz4cPGOAKw5auW6fJv/mNQkND3doXnYIzZ9o0bdu7V0tWr1bqyZN6dMwY1/QKT69Dkr5cu1ZfJSfrpyNHdNZmU5bDofoREWrTooWG9OqlUb/+tUKCgz26HgBA1SMgAABA0i9nz+r+p57SwWPH3LannDyp/379tb5at05/efRRDe7Z0+Nz7tm/X0vWrHHblpeXp58OH9ZPhw9r5caN+uhvf5MlKsqnPkfVr+96bsvM1IvvvKNf9+mjtpdcotAQ5/+LDwkOLnZD9uycOUpascL1+vDx4/rbO+/omssu86kfJTmckqKF529QC+VKOnT8uA4dP67lGzZo3osvlnhDn2G3a8zUqTqTnu62/Vx2tib8+c/atHOn2/aUkyeVtHy5VmzYoDnTpqn9FVf47TokZ6Dy1Msv68u1a922nzpzRkvXrdOqjRv118ceU98iN8sff/GFnv/nP1VQUHDhuvLy9O2OHfp2xw7df9tt+v199/mtj2mnT+vbHTskOf83nzh6tHbu26efjx7V2YwMrdm0SQN79Cj1+OfeekuHjx8vtt3b61j93Xda/d13bufIsNu1Y98+7di3T1t279bsP/6xopcLAKgkBAQAAEia+cYbrnCgodWqCffeq8ZWqz7+8ktt2LpVuXl5mvbqq+rUrp16d+qkd2fO1HNvvaV9Bw5Ikh64/Xb1uu46SVKjBg0kSW0vuUSTxoxRi7g41Y+IUEhwsGyZmfr0q6+0YetWHUtLU9KKFRp90S/InmrVrJlax8fr0Pkbuw8//1wffv65wkJDddWll6r7r36l24YMUUyjRq5jtu7Z4xYO3Nyvn27s1Uv/O3hQcz/6yKd+lKRZ06b6/X33qWWzZoqMiFBYaKjsWVn6av16fbFmjdIzMvSvRYs09Xe/K3ZsZlaWoiMj9dgDD+jSli114OhRhYWG6vX5813hQPPYWP1u5Eg1bdRIqzZu1KdffaUMu11T//Y3Jf3jH66AxBOLv/5ai7/+utj2wlELn371lSscaGixaOydd6p18+basmuX/vnpp8rJzdVTL7+sL958U5aoKO0/fNh1Ux0UFKT7b7tNndq107G0NP39gw9ky8zUO599pm4dO+r6Dh307syZSv7+e7396aeSpCvbtNEfH3zQq7/3f7/+2nUT3/3aa9XQYtHQvn31j3//W5JzdEFZAcHh48c1fMAADejeXeeys9WkQQOvr0OS+nbpos4JCWraqJEiIyIkSSd++UWvf/SRjqSmatXGjdq9f3+x0T0AgOqBgAAAUOfZMjNdFeQl6Y8PPqghvXpJkq7v2FG/fvBBnbHZ5Dh3TsvXr9edN92kRg0auP2C36pZs2LD1BPattXen37SB4sW6eejR5Vptyu/yC+xkrRj716f+x0cHKznJk/Wo7NmKfXUKdf2nNxc1y+2HyxapFefflqd2rWTJLcidpe1aqU/n58S0KtTJ53NyNC/PBzOXp5LW7bUDz/9pE+/+ko/HjwoW2am8vLz3dqUde1//sMfXL/Id//Vr2SaphYVGUJ/5003qVWzZpKkm/r21ZrvvtPJM2d0NC1Nm3buVI9rr/XLdUjSwiKByi39+7umE/S49lqt27LFOeUgK0vL1q/X7UOG6POVK103613at1fP8325rGVL9e/a1TWyYsHy5bq+Qwdde801OpyS4nqPqPr1PZryUFTRgOOmvn0lSb/u00dz5s2TaZrasHWrTp89q8bnw6uLDejWTTMmTHDbNvvdd726jsK/yb8WLdInS5fqaGqqHNnZMk3T7bw79u4lIACAaoqAAABQ5x06ftztxr3ozVlEeLiuufxyrf/+e0nSz0ePenze6a++qv+uXl1mG1sJReW8cfVll2nRnDla/d13+mbrVu343/904OhR101Z1rlzenbOHC38xz8kye1GtOOVV7qd69qrr/ZbQDBn3jy9+Z//lNmmtGsPCw1Vny5d3LadSU93m3Lwt3feKfW8Px0+7FVAUFqRwsJfwH8+csS17f2FC/V+KX+jnw4fdv5bpP2327fr2yK1L0pqX1Hb9+51jX6pX6+eK1hpHhura6++Wt/v2aO8/HwtWb1a/1fKaJUB3bsX75+X1+HIztaYqVOLTdO5WEU/8wCAykNAAABAOS7+BdQTaadPu4UDd918s3p36qSI8HCt2bRJ7yUlSZLb3G5fhYeFaUivXq5RD6fPntXL77/v+lX54LFjsmVmFqt1YBiGR+cv2u7iERBnbLZi7XPz8vTB55+7Xt/Yu7du6ttXUfXra9f+/a6b+9KuvXGDBh73rSR2h8Or9p4WKfT3+3rbvjRFixNmnTun7qNGldqutICgaZFpKN4qvI6vzxf3lKR64eF65J57dGWbNgoJDtYbH3/sKhLqj888AKByEBAAAOq81vHxCg4Kct38bvvhB1cxwnPZ2frh559dbds0b+56XnTZvYtDhLQiQ/6t0dF64re/db1esHy5X/qdnpGh/x08qC7t27ttb9yggUYOHVrivPpW8fGu0RDbLxriv+2HH0p8n6LBwlmbTTm5uQoLDVVefr42nD/Xxf0qXF1Bkp4eN871a/zGUn6FLk9Dq1UNLRZXIDFn2rQSRwnYHQ7Xe/lLmxYtXJ+B//fww7p9yJBibbJzclyfh0tbtHD9jW/s3VvPTZ5crL1pmjqXne16XdZnqSzZOTlu00bKsv/wYe3Zv9/jJQ+9vY6i01x6XnutaxWE3Lw8HT+/WgIAoHojIAAA1HmWqCj169pVK775RpL0/FtvKcvhUKPzRQoLh7ZH1Kunwed/pZecN/6Flm/YoPiYGIWGhKh18+ZqUaQ6f3pGht74+GO1v+IKfbN1a7nTDjyVnpmpB59+Wpe2aKEbunbVNZddJmt0tE6fPev2C/4lzZu7bvIH9+ypj/77X0nOG8Y//f3vGtyzp348dEgfLVlS4vu0iI1VSHCw8vLzlZObq8deeEE9rr1Wyzds0NG0tGLtGzdooIh69Vwhwd8/+EA3XH+9du7bp3c++8ynazUMQ7f07+8a3v/0K6/oN4mJurx1aznOnVPqyZP6budObdi6Vd+WM7XBWyMGDtQPb74pyTkv/4zNpnaXX668vDylnjqlbXv3as133+k/L7+s5rGxuqV/f/37v/9VQUGBlq5bp8iICPXp3FlhYWFKO3VK/zt4UCu/+Ubj7r5bwwcMkOT+WSrc38hqVXRkpC5v3brUvq3auFEZ54fsW6Oj9fCddxZr8/nXX2vP/v3O56tWeRwQeHsdRVek+HbHDn2+apUaWCz6z5dflrhCAgCg+iEgAABA0tSHHtL+w4d18NgxnT57VtNfe81tf2hIiGZMmOBW5K1bx45asWGDJGn999+7fm39y8SJuumGGzS0Tx9X9fuiKwR0ad++2FJ9FfHz0aOl1kYICQ7WlPvvd72+9uqrddvgwfps2TJJzhvGwiHqV116qfYWGS1RqH5EhG7p39818mHNpk1as2mTDMPQZa1aFZtLbxiG7r75ZldV/o+/+EIff/GFJOn69u31nY/X/vBdd2n3/v3avGuXTp89qxfLqEPgT3fceKO2/vCDlq5bp6xz51wrA5Sm7SWX6PHf/lYvnF8B4LNly1x/79L86qqrVC88XOeys5WZlaXJzz8vSeraoYPeeOaZUo8rOr2g3/XX686bbirWJiwsTM+cDwiWrlunyb/5jUJDQ8vsjy/X0bdLF7Vs1kxHUlKUmZWlP/3975KcdRHatW2r3T/+WO57AgACi4AAAAA5f/We9+KL+vd//6tV33yjg8ePKzcvT00aNFDnhAT934gRrur1hW4dOFDHT5zQktWrdfLMmWJzq/80fryaNmqk5evX65f0dF3SvLnuv+02ncvO9ktA0KxpU7385JPatHOndv7vfzr5yy/6JT1dpmmqScOGuvbqq3XvLbfo6ssuczvuqbFj1SIuTp999ZXSTp9WXJMmum3IEF1z2WX63Z/+VOJ7FYYMqzZulCM7W1e2aaOxd96p7Xv3llhs7+G77lJU/fpasHy50k6dUvPYWI369a91WcuWPgcE9cLD9caMGVq4cqW+XLtWPx46JHtWlqzR0Ypt3FjXtWunvhcVN/SHoKAgPTd5svp366ZFK1dqz08/KSMzU9GRkWraqJE6XHmlbrj+esU1aeI65s5f/1oJbdtq3n//q6179ujUmTMKDwtTk4YN1bZ1a/Xt0kU3dO3qam+JitLsP/5Rr/3739p/6JBycnPL7deJX37Rtzt2uF6Xtoxhv65d9Ze5c5VfUKCzGRlas2lTmUseFuXNddQLD9dbzzyjF999V1t27VJ2To46XHmlJo4erX8vXkxAAAA1gGH6UnkJAADUOpt27tSDTz8tyRk+fPnWWwHuEQAAqEpB5TcBAAAAAAC1HQEBAAAAAAAgIAAAAAAAANQgAAAAAAAAYgQBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAEAEBAAAAAAAQAQEAAAAAABABAQAAAAAAkPT/AZ+Ekpb15yyiAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
+       "<Figure size 1200x700 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2052,6 +2052,416 @@
    "source": [
     "![Hebbal_outliers.png](attachment:Hebbal_outliers.png)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 559,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count')"
+      ]
+     },
+     "execution_count": 559,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Depicting the normal distribution \n",
+    "import matplotlib\n",
+    "matplotlib.rcParams[\"figure.figsize\"] = (12,7)\n",
+    "plt.hist(df8.Price_per_sqft,rwidth=0.8)\n",
+    "plt.xlabel(\"Price Per Square Feet\")\n",
+    "plt.ylabel(\"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b><h4 style = \"color:green;\">Removing the Bathroom Feature Outliers</h4><b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 560,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.,  3.,  4.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
+      ]
+     },
+     "execution_count": 560,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df8.bath.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 561,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count')"
+      ]
+     },
+     "execution_count": 561,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df8.bath,rwidth=0.8)\n",
+    "plt.xlabel(\"Number of bathrooms\")\n",
+    "plt.ylabel(\"Count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 562,
+   "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>location</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>Size_in_BHK</th>\n",
+       "      <th>Price_per_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5271</th>\n",
+       "      <td>Neeladri Nagar</td>\n",
+       "      <td>4000.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8478</th>\n",
+       "      <td>others</td>\n",
+       "      <td>12000.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4375.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8567</th>\n",
+       "      <td>others</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>550.0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>5500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9307</th>\n",
+       "      <td>others</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9639</th>\n",
+       "      <td>others</td>\n",
+       "      <td>5425.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>275.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>5069.124424</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            location  total_sqft  bath  price  Size_in_BHK  Price_per_sqft\n",
+       "5271  Neeladri Nagar      4000.0  12.0  160.0           10     4000.000000\n",
+       "8478          others     12000.0  12.0  525.0           10     4375.000000\n",
+       "8567          others     10000.0  16.0  550.0           16     5500.000000\n",
+       "9307          others      6000.0  12.0  150.0           11     2500.000000\n",
+       "9639          others      5425.0  13.0  275.0           13     5069.124424"
+      ]
+     },
+     "execution_count": 562,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df8[df8.bath>10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>It is unusual to have 2 more bathrooms than number of bedrooms in a home</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 563,
+   "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>location</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>Size_in_BHK</th>\n",
+       "      <th>Price_per_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6916.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1170.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3418.803419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1425.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4561.403509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>947.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4540.654699</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               location  total_sqft  bath  price  Size_in_BHK  Price_per_sqft\n",
+       "1   Devarachikkanahalli      1250.0   2.0   40.0            2     3200.000000\n",
+       "2   Devarachikkanahalli      1200.0   2.0   83.0            2     6916.666667\n",
+       "3   Devarachikkanahalli      1170.0   2.0   40.0            2     3418.803419\n",
+       "4   Devarachikkanahalli      1425.0   2.0   65.0            3     4561.403509\n",
+       "5   Devarachikkanahalli       947.0   2.0   43.0            2     4540.654699"
+      ]
+     },
+     "execution_count": 563,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df9 = df8[df8.bath<df8.Size_in_BHK+2]\n",
+    "df9.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Now since the whole dataset has been refined ; and there is no need of Price_per_sqft we can drop it</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 564,
+   "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>location</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>Size_in_BHK</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1170.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>1425.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Devarachikkanahalli</td>\n",
+       "      <td>947.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               location  total_sqft  bath  price  Size_in_BHK\n",
+       "1   Devarachikkanahalli      1250.0   2.0   40.0            2\n",
+       "2   Devarachikkanahalli      1200.0   2.0   83.0            2\n",
+       "3   Devarachikkanahalli      1170.0   2.0   40.0            2\n",
+       "4   Devarachikkanahalli      1425.0   2.0   65.0            3\n",
+       "5   Devarachikkanahalli       947.0   2.0   43.0            2"
+      ]
+     },
+     "execution_count": 564,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df10 = df9.drop(\"Price_per_sqft\", axis=\"columns\")\n",
+    "df10.head()"
+   ]
   }
  ],
  "metadata": {