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banglore-home-price

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abps commited on Mar 28, 2023

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.gitattributes +1 -0
Client/house.jpg +3 -0
Client/index.html +75 -0
Client/style.css +151 -0
assets/RealStatePrice.ipynb +2246 -0
assets/banglore_price_prediction_model.pickle +3 -0
assets/datasets/.ipynb_checkpoints/Bengaluru_House_Data-checkpoint.csv +0 -0
assets/datasets/Bengaluru_House_Data.csv +0 -0
assets/location_encoder.pickle +3 -0
assets/test.js +124 -0
server.py +44 -0
util.py +30 -0

.gitattributes CHANGED Viewed

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Client/house.jpg filter=lfs diff=lfs merge=lfs -text

Client/house.jpg ADDED Viewed

Git LFS Details

SHA256: 554bf4423826df49c3b38891f263e167e740d28f46c3410ede74d5232ad3317f
Pointer size: 132 Bytes
Size of remote file: 2.17 MB

Client/index.html ADDED Viewed

	@@ -0,0 +1,75 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <link rel="stylesheet" href="style.css">
+    <title>Home Price Predictor</title>
+</head>
+<body>
+    <div class="container">
+        <h1 id="form_title">House price</h1>
+        <div id="left">
+            <hr>
+        </div>
+        <form id="get_price">
+            <select id="locations" name="location" , placeholder="location">
+                <option value="" disable selected>
+                    --Please Select Location--
+                </option>
+            </select>
+            <input type="number" , name="bhk" , placeholder="No of Bedrooms (BHK)" required>
+            <input type="number" , name="bath" , placeholder="No of Bathrooms" required>
+            <input type="number" , name="total_sqft" , placeholder="Total area in Square ft." required>
+            <div class="res">
+                <button type="submit">Predict Price</button>
+                <textarea name="result" id="result" cols="10" rows="5" readonly></textarea>
+            </div>
+        </form>
+    </div>
+    <script src="https://code.jquery.com/jquery-3.6.4.min.js"
+        integrity="sha256-oP6HI9z1XaZNBrJURtCoUT5SUnxFr8s3BzRl+cbzUq8=" crossorigin="anonymous"></script>
+    <script>
+        function showLocations(res) {
+            var dropdown = document.getElementById('loc')
+            for (let i = 0; i < len(res); i++) {
+                var newNode = `<option value = ${res[i]} > ${res[i]} </option>`;
+                dropdown.appendChild(newNode);
+            }
+        }
+        let url_locations = '/api/loc'
+        $.get(url_locations, (data, status) => {
+            var dropdown = document.getElementById("locations");
+            for (i in data) {
+                let node = `<option value = ${data[i]} > ${data[i]} </option>`;
+                dropdown.insertAdjacentHTML("beforeend", node);
+            }
+        })
+        document.forms['get_price'].addEventListener('submit', (event) => {
+            event.preventDefault();
+            fetch('/api/get_price', {
+                method: 'POST',
+                body: new URLSearchParams(new FormData(event.target))
+            }).then((response) => {
+                if (!response.ok) {
+                    throw new Error(`HTTP error! Status: ${response.status}`);
+                }
+                return response.text();
+            }).then((body) => {
+                let price = parseInt(parseFloat(body) * 100000);
+                document.getElementById("result").innerText = `Rs. ${price}`;
+            }).catch((error) => {
+                // TODO handle error
+            });
+        });
+    </script>
+</body>
+</html>

Client/style.css ADDED Viewed

	@@ -0,0 +1,151 @@

+        * {
+            margin: 0;
+            font-size: 1.3rem;
+            box-sizing: border-box;
+        }
+        body {
+            text-align: center;
+            overflow: hidden;
+            background-image: url('house.jpg');
+            background-size: auto;
+            height: 100%;
+            background-size: 100%;
+            background-repeat: no-repeat;
+        }
+        form {
+            text-align: left;
+        }
+        .container {
+            background-color: rgba(242, 244, 239, 0.45);
+            width: 24rem;
+            height: 28rem;
+            border-radius: 5px;
+            box-shadow: 2px 3px 20px rgb(201, 199, 199);
+            padding: 3rem 2.5rem;
+            margin: auto;
+            position: absolute;
+            top: 7%;
+            left: 8%;
+            z-index: 2;
+        }
+        #form_title {
+            font-size: 2rem;
+            text-align: left;
+            color: rgb(81, 79, 79);
+        }
+        hr {
+            border: none;
+            border-bottom: 10px solid white;
+            height: 10px;
+            outline: none;
+            width: 100%;
+            text-align: left;
+            margin-top: 2%;
+        }
+        #left {
+            width: 100%;
+            padding: 0;
+            padding-right: 65%;
+        }
+        input,
+        select,
+        option {
+            display: block;
+            margin: 5% 0;
+            padding: 10px 15px;
+            border-radius: 8px;
+            width: 100%;
+            box-sizing: border-box;
+            text-align: left;
+            outline: none;
+            border: none;
+            background-color: #f8f7f6ca;
+            color: rgba(101, 98, 98, 0.791);
+        }
+        .res {
+            display: flex;
+            justify-content: space-between;
+            height: 80px;
+        }
+        button {
+            display: block;
+            font-size: 1.3rem;
+            font-weight: bold;
+            padding: 10px 20px;
+            border-radius: 25px;
+            text-align: left;
+            color: #f8f7f6ca;
+            display: inline-block;
+            height: fit-content;
+            border: none;
+            background-color: rgba(101, 98, 98, 0.791);
+        }
+        textarea {
+            display: inline-block;
+            outline: none;
+            padding: 5px;
+            color: rgba(101, 98, 98, 0.791);
+            background-color: #f8f7f6ca;
+        }
+        button:hover {
+            background-color: #515050e4;
+            color: #f8f7f6d5;
+            transition: 0.3s cubic-bezier(1, 0.25, 0.21, 1.32);
+        }
+        button:active {
+            transform: translateY(4px);
+            transition: 0.6s cubic-bezier(1, 0.25, 0.21, 1.32);
+        }
+        @media screen and (max-width: 1075px) {
+            body {
+                background-size: 170%;
+            }
+            .container {
+                height: 28rem;
+                padding: 2.7rem 2.2rem;
+                width: 24rem;
+            }
+        }
+        @media screen and (max-width: 700px) {
+            body {
+                background-size: 300%;
+            }
+            .container {
+                height: 27rem;
+                width: 20rem;
+                padding: 2.5rem 2rem;
+                top: 5%;
+                left: 5%;
+            }
+        }
+        @media screen and (max-width: 450px) {
+            body {
+                background-size: 300%;
+            }
+            .container {
+                height: 70%;
+                width: 90%;
+                padding: 1rem 0.8rem;
+                top: 15%;
+                left: 3%;
+            }
+        }

assets/RealStatePrice.ipynb ADDED Viewed

	@@ -0,0 +1,2246 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "97e13347-621c-4656-a175-8ebcf9d842f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "pd.options.display.max_rows = 4000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aac70b32-b23f-4394-ba64-bc678a9429e7",
+   "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>area_type</th>\n",
+       "      <th>availability</th>\n",
+       "      <th>location</th>\n",
+       "      <th>size</th>\n",
+       "      <th>society</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>balcony</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Super built-up  Area</td>\n",
+       "      <td>19-Dec</td>\n",
+       "      <td>Electronic City Phase II</td>\n",
+       "      <td>2 BHK</td>\n",
+       "      <td>Coomee</td>\n",
+       "      <td>1056</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>39.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Plot  Area</td>\n",
+       "      <td>Ready To Move</td>\n",
+       "      <td>Chikka Tirupathi</td>\n",
+       "      <td>4 Bedroom</td>\n",
+       "      <td>Theanmp</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>120.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Built-up  Area</td>\n",
+       "      <td>Ready To Move</td>\n",
+       "      <td>Uttarahalli</td>\n",
+       "      <td>3 BHK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>62.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Super built-up  Area</td>\n",
+       "      <td>Ready To Move</td>\n",
+       "      <td>Lingadheeranahalli</td>\n",
+       "      <td>3 BHK</td>\n",
+       "      <td>Soiewre</td>\n",
+       "      <td>1521</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>95.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Super built-up  Area</td>\n",
+       "      <td>Ready To Move</td>\n",
+       "      <td>Kothanur</td>\n",
+       "      <td>2 BHK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>51.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              area_type   availability                  location       size  \\\n",
+       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
+       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
+       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
+       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
+       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
+       "\n",
+       "   society total_sqft  bath  balcony   price  \n",
+       "0  Coomee        1056   2.0      1.0   39.07  \n",
+       "1  Theanmp       2600   5.0      3.0  120.00  \n",
+       "2      NaN       1440   2.0      3.0   62.00  \n",
+       "3  Soiewre       1521   3.0      1.0   95.00  \n",
+       "4      NaN       1200   2.0      1.0   51.00  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"datasets/Bengaluru_House_Data.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bdd90a24-68a5-4d9d-87aa-05331c3e0c05",
+   "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>availability</th>\n",
+       "      <th>location</th>\n",
+       "      <th>size</th>\n",
+       "      <th>society</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>balcony</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Built-up  Area</th>\n",
+       "      <td>2418</td>\n",
+       "      <td>2418</td>\n",
+       "      <td>2418</td>\n",
+       "      <td>1215</td>\n",
+       "      <td>2418</td>\n",
+       "      <td>2410</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>2418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Carpet  Area</th>\n",
+       "      <td>87</td>\n",
+       "      <td>87</td>\n",
+       "      <td>87</td>\n",
+       "      <td>54</td>\n",
+       "      <td>87</td>\n",
+       "      <td>87</td>\n",
+       "      <td>82</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plot  Area</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>2025</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>311</td>\n",
+       "      <td>2025</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>1837</td>\n",
+       "      <td>2025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Super built-up  Area</th>\n",
+       "      <td>8790</td>\n",
+       "      <td>8789</td>\n",
+       "      <td>8790</td>\n",
+       "      <td>6238</td>\n",
+       "      <td>8790</td>\n",
+       "      <td>8741</td>\n",
+       "      <td>8482</td>\n",
+       "      <td>8790</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      availability  location  size  society  total_sqft  bath  \\\n",
+       "area_type                                                                       \n",
+       "Built-up  Area                2418      2418  2418     1215        2418  2410   \n",
+       "Carpet  Area                    87        87    87       54          87    87   \n",
+       "Plot  Area                    2025      2025  2009      311        2025  2009   \n",
+       "Super built-up  Area          8790      8789  8790     6238        8790  8741   \n",
+       "\n",
+       "                      balcony  price  \n",
+       "area_type                             \n",
+       "Built-up  Area           2310   2418  \n",
+       "Carpet  Area               82     87  \n",
+       "Plot  Area               1837   2025  \n",
+       "Super built-up  Area     8482   8790  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"area_type\").agg('count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "08bba0ab-e89d-43c1-9594-d3dd660be63d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(['area_type','availability','society','balcony'],axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca2ade22-d6ee-402a-9e4f-1aef26b2f89e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13320, 5)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aa521583-3810-439e-aa8f-7693ba9fdbab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "location       1\n",
+       "size          16\n",
+       "total_sqft     0\n",
+       "bath          73\n",
+       "price          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7a17c0de-5ab0-44c6-8e5f-a8b05797b383",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bc44288e-49f6-4972-9542-7b049368caa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "location      False\n",
+       "size          False\n",
+       "total_sqft    False\n",
+       "bath          False\n",
+       "price         False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "96bc00f7-97be-495e-9833-fdef0cc9edda",
+   "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>size</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Electronic City Phase II</td>\n",
+       "      <td>2 BHK</td>\n",
+       "      <td>1056</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>39.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Chikka Tirupathi</td>\n",
+       "      <td>4 Bedroom</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Uttarahalli</td>\n",
+       "      <td>3 BHK</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>62.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Lingadheeranahalli</td>\n",
+       "      <td>3 BHK</td>\n",
+       "      <td>1521</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>95.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kothanur</td>\n",
+       "      <td>2 BHK</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>51.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   location       size total_sqft  bath   price\n",
+       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
+       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
+       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
+       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
+       "4                  Kothanur      2 BHK       1200   2.0   51.00"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e69ba835-f0dc-4531-b45a-91fba2a62f26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
+       "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
+       "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
+       "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
+       "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
+       "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8c31f9f0-27ac-4aca-b167-792c048ab7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extractNum(s):\n",
+    "    num=0\n",
+    "    i = 0\n",
+    "    while s[i]>='0' and s[i]<='9':\n",
+    "        num = num*10+ int(s[i])\n",
+    "        i+=1\n",
+    "    return num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1e83fd89-ed93-4aa2-9d7c-2ebd90f132be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(extractNum(\"2 bhk\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9e70e370-1883-4c3e-8651-92bdc7e5c603",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['size']=df['size'].apply(extractNum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "477c0c01-c8c2-4833-a317-ff9fa2bdabc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2,  4,  3,  6,  1,  8,  7,  5, 11,  9, 27, 10, 19, 16, 43, 14, 12,\n",
+       "       13, 18], dtype=int64)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3105a155-468d-4c85-a6a8-6dd3b27fb987",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('ygug.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "402344c6-aaed-4de4-9b13-ad5e77bc21bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.total_sqft.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ec23ac37-68ec-4ade-a189-c800fa06835c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rangeToMean(x):\n",
+    "    try:\n",
+    "        float(x)\n",
+    "    except:\n",
+    "        nums = x.split('-')\n",
+    "        try:\n",
+    "            float((float(nums[0])+float(nums[1]))/2)\n",
+    "        except:\n",
+    "            return None\n",
+    "        return float((float(nums[0])+float(nums[1]))/2)\n",
+    "    return float(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a1de141c-81b3-4a1b-b664-025d2fdffc82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['total_sqft'] = df['total_sqft'].apply(rangeToMean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3be0c853-0947-4b15-be72-187a708fc54c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7d44c637-c9cd-4139-a7d2-57d227da687c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "location      False\n",
+       "size          False\n",
+       "total_sqft    False\n",
+       "bath          False\n",
+       "price         False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b03ac292-fea6-438c-82b0-7b9a29a0ea80",
+   "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>size</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Electronic City Phase II</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1056.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>39.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Chikka Tirupathi</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Uttarahalli</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1440.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>62.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Lingadheeranahalli</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1521.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>95.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kothanur</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>51.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   location  size  total_sqft  bath   price\n",
+       "0  Electronic City Phase II     2      1056.0   2.0   39.07\n",
+       "1          Chikka Tirupathi     4      2600.0   5.0  120.00\n",
+       "2               Uttarahalli     3      1440.0   2.0   62.00\n",
+       "3        Lingadheeranahalli     3      1521.0   3.0   95.00\n",
+       "4                  Kothanur     2      1200.0   2.0   51.00"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "315a28ae-b560-4252-b3dc-3d926c0bcec2",
+   "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>size</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>price_per_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Electronic City Phase II</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1056.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>39.07</td>\n",
+       "      <td>3699.810606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Chikka Tirupathi</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120.00</td>\n",
+       "      <td>4615.384615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Uttarahalli</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1440.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>62.00</td>\n",
+       "      <td>4305.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Lingadheeranahalli</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1521.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>95.00</td>\n",
+       "      <td>6245.890861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kothanur</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>51.00</td>\n",
+       "      <td>4250.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   location  size  total_sqft  bath   price  price_per_sqft\n",
+       "0  Electronic City Phase II     2      1056.0   2.0   39.07     3699.810606\n",
+       "1          Chikka Tirupathi     4      2600.0   5.0  120.00     4615.384615\n",
+       "2               Uttarahalli     3      1440.0   2.0   62.00     4305.555556\n",
+       "3        Lingadheeranahalli     3      1521.0   3.0   95.00     6245.890861\n",
+       "4                  Kothanur     2      1200.0   2.0   51.00     4250.000000"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['price_per_sqft']=df['price']*100000/df['total_sqft']\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f5e4a78-29b9-4173-8c93-64a299b2bfff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a629008-8a90-4270-8a64-eecf0c871221",
+   "metadata": {},
+   "source": [
+    "## Outlier Detection and Removal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ac9d1ebb-749c-4729-9dd5-f8cd12c074f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[~(df['total_sqft']/df['size']<300)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9a089a6a-0e86-4b15-9239-f4954326b8e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     12456.000000\n",
+       "mean       6308.502826\n",
+       "std        4168.127339\n",
+       "min         267.829813\n",
+       "25%        4210.526316\n",
+       "50%        5294.117647\n",
+       "75%        6916.666667\n",
+       "max      176470.588235\n",
+       "Name: price_per_sqft, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['price_per_sqft'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "46907e8a-87ca-4cbb-bbc4-e37dae7a4a91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing outliers for location-wise price_per_sqft  \n",
+    "df.location=df.location.apply(lambda x: x.strip())\n",
+    "l= df.groupby('location')\n",
+    "new_df = pd.DataFrame()\n",
+    "for key, group in l:\n",
+    "    if(len(group)>1):\n",
+    "        m = np.mean(df['price_per_sqft'])\n",
+    "        std = np.std(df['price_per_sqft'])\n",
+    "        red_df = group[((group['price_per_sqft']>=(m-std)) & (group['price_per_sqft']<=(m+std)))]\n",
+    "        new_df = pd.concat([new_df, red_df])\n",
+    "df = new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ad168cf9-2709-4331-8322-c7cb2cc42cc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10959, 6)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2493a43a-c464-41ac-b183-21a21e635acc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3,  1,  4,  2,  5,  6,  7,  8,  9, 16, 10], dtype=int64)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0bb99211-d1da-4556-8b5f-9703b58a3d03",
+   "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"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x360 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter(df[df['size']==2]['total_sqft'],df[df['size']==2]['price'],color='red',marker='+')\n",
+    "plt.scatter(df[df['size']==3]['total_sqft'],df[df['size']==3]['price'],color='green',marker='.')\n",
+    "plt.xlabel(\"total_sqft\")\n",
+    "plt.ylabel(\"Price\")\n",
+    "plt.figure(figsize=(15,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca9fddd-d8fe-4de2-baf3-8cf3d0b1e7fd",
+   "metadata": {},
+   "source": [
+    "## Visualisation with datashader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "824fabbb-6e9b-45cc-98b0-e17d7df3993e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Abhay\\anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py:369: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
+      "  _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
+      "C:\\Users\\Abhay\\anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py:369: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
+      "  _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
+      "C:\\Users\\Abhay\\anaconda3\\lib\\site-packages\\dask\\dataframe\\utils.py:369: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
+      "  _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAADeUlEQVR4nO3cMc5MYRSA4bnCBsQK7OBP9CqFHYhVWMKd3gpswRJYgkStVgmVjr+4GqIZV3Xc1+R5ypnkfNO8OckUZ9m27QT03Dn6BwCXiROixAlR4oQocULU3b0vz+ezv3Lhp3VdR+Zu27Zc+tzmhChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTojavb4H/DZ1fe9PbE6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEqKu6vjdxHe1fX1yDX2xOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVGHHPh6M3Q0yzEuronNCVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBB1yPW9h8vM3PcD1/du/rNLgS4QXg+bE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFq9/re1/M68uizoZt/r25n5k5wJY+/sTkhSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTonbv4L18PvPo29czc7/MjIVD2JwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUbvX95YX90YeffzhdmTug3cjY+EQNidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEqN0DX6dH30ce/fZpGZl7s64jc+EINidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcECVOiBInRIkTosQJUeKEKHFC1P71vc/3Rx59+nFk7OnJzFg4hM0JUeKEKHFClDghSpwQJU6IEidEiROixAlR4oQocUKUOCFKnBAlTogSJ0SJE6LECVHihChxQpQ4IUqcELVs23b0bwAusDkhSpwQJU6IEidEiROixAlRPwAfPC9vgQglngAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import datashader as ds\n",
+    "import colorcet as cc\n",
+    "bhk_2 = df[df['size']==2]\n",
+    "x_r = [bhk_2['total_sqft'].min(),bhk_2['total_sqft'].max()]\n",
+    "y_r = [bhk_2['price'].min(),bhk_2['price'].max()]\n",
+    "cvs = ds.Canvas(plot_width=10, plot_height=10,x_range=x_r,y_range=y_r)  # auto range or provide the `bounds` argument\n",
+    "agg = cvs.points(bhk_2, 'total_sqft', 'price')  # this is the histogram\n",
+    "img = ds.tf.set_background(ds.tf.shade(agg, how=\"log\", cmap=cc.fire), \"grey\").to_pil()  # create a rasterized image\n",
+    "plt.imshow(img)\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3258accc-e592-4db1-9354-bdb81c1b0081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bhk_outlier(d,location):\n",
+    "    plt.title(location)\n",
+    "    plt.xlabel(\"total Sqft Area\")\n",
+    "    plt.ylabel(\"Price\")\n",
+    "    d_loc = d[d['location']==location]\n",
+    "    bhk_2 = d_loc[d_loc['size']==2]\n",
+    "    bhk_3 = d_loc[d_loc['size']==3]\n",
+    "    plt.scatter(bhk_2['total_sqft'],bhk_2['price'],marker='+',label='2 bhk',color='green')\n",
+    "    plt.scatter(bhk_3['total_sqft'],bhk_3['price'],marker='.',label='3_bhk',color='blue')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "dea56f46-e3b4-448b-bbc4-7f9cfaf4a10f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# unique_locations = df.location.unique()\n",
+    "# for location in unique_locations:\n",
+    "#     bhk_outlier(df,location)\n",
+    "#     print('\\n\\n\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f96c2b1e-7efe-4d5a-8692-289d76e7aeab",
+   "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>size</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>price_per_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5610</th>\n",
+       "      <td>1st Block BEL Layout</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1540.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>5519.480519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8470</th>\n",
+       "      <td>1st Block HBR Layout</td>\n",
+       "      <td>1</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>7500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12564</th>\n",
+       "      <td>1st Block HBR Layout</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3150.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>4761.904762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2308</th>\n",
+       "      <td>1st Block HRBR Layout</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>3478.260870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7715</th>\n",
+       "      <td>1st Block HRBR Layout</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>5360.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    location  size  total_sqft  bath  price  price_per_sqft\n",
+       "5610    1st Block BEL Layout     3      1540.0   3.0   85.0     5519.480519\n",
+       "8470    1st Block HBR Layout     1       600.0   1.0   45.0     7500.000000\n",
+       "12564   1st Block HBR Layout     4      3150.0   4.0  150.0     4761.904762\n",
+       "2308   1st Block HRBR Layout     3      2300.0   3.0   80.0     3478.260870\n",
+       "7715   1st Block HRBR Layout     2      1250.0   2.0   67.0     5360.000000"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "394d151c-c806-4d0a-be81-f91f8e5f5ead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bhk_outlier_remover():\n",
+    "    exclude_indices = np.array([])\n",
+    "    for loc,loc_df in df.groupby('location'):\n",
+    "        # creating stats {mean,count,std} for each bhk\n",
+    "        stats = {}\n",
+    "        bhks = loc_df.groupby('size')\n",
+    "        for bhk,bhk_group in bhks:\n",
+    "            stats[bhk] = {\n",
+    "                'mean':np.mean(bhk_group['price_per_sqft']) ,\n",
+    "                'std': np.std(bhk_group['price_per_sqft']),\n",
+    "                'count':bhk_group.shape[0]\n",
+    "            }\n",
+    "#         print(stats)\n",
+    "        # adding indexes coressponding to rows where price_per_sqft of bhk_n is less than mean of price_per_sqft of bhk_n-1 into exclude_indices\n",
+    "        for bhk,bhk_group in bhks:\n",
+    "            stats_prev = stats.get(bhk-1)\n",
+    "            if stats_prev and stats_prev['count']>5:\n",
+    "                index_to_del = bhk_group[bhk_group['price_per_sqft'] < (stats_prev['mean'])].index.values\n",
+    "                exclude_indices = np.append(exclude_indices,index_to_del)\n",
+    "    return df.drop(exclude_indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c2bf8e0f-6e97-4a4e-8a71-1334a0f88efd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = bhk_outlier_remover()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "63c7ebf2-a16b-4be3-bd58-b87890b5354b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8764, 6)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9d0156ce-912a-406f-83f7-7288e9ab83a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10959, 6)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "95ebb8af-8b2f-45c8-a82b-887a57ec6a08",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# df.location =df.location.apply(lambda x: x.strip())\n",
+    "# location_stats = df.groupby('location')['location'].agg('count')\n",
+    "# location_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "18a3b81b-487c-4fc9-b010-aae5a63f0e4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "752"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df2.location.unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61531f21-5d53-48d5-a2ed-b73c127d77f4",
+   "metadata": {},
+   "source": [
+    "### model training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "eb533a5c-70dc-4735-821d-4f35fc65a0bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2.drop('price_per_sqft',axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b272fca5-4feb-4bd0-9caa-dec1cc875626",
+   "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>size</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5610</th>\n",
+       "      <td>1st Block BEL Layout</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1540.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>85.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8470</th>\n",
+       "      <td>1st Block HBR Layout</td>\n",
+       "      <td>1</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12564</th>\n",
+       "      <td>1st Block HBR Layout</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3150.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2308</th>\n",
+       "      <td>1st Block HRBR Layout</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7715</th>\n",
+       "      <td>1st Block HRBR Layout</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    location  size  total_sqft  bath  price\n",
+       "5610    1st Block BEL Layout     3      1540.0   3.0   85.0\n",
+       "8470    1st Block HBR Layout     1       600.0   1.0   45.0\n",
+       "12564   1st Block HBR Layout     4      3150.0   4.0  150.0\n",
+       "2308   1st Block HRBR Layout     3      2300.0   3.0   80.0\n",
+       "7715   1st Block HRBR Layout     2      1250.0   2.0   67.0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2a2ec5e7-d63a-463c-a447-5f0b6ef818b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "03a87d19-bd9a-46bf-a1fb-80d58250e54e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ohe = OneHotEncoder()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "c391eb23-71b2-4dd0-9bbd-7d067ddbe92e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OneHotEncoder()"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe.fit(df2[['location']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "1f2d6a85-6d02-43be-a513-f1b9fa5b6258",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8764, 752)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "location_encoding = ohe.transform(df2[['location']]).toarray()\n",
+    "location_encoding.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "04a57321-7031-465e-a507-8610abdb3b82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['1st Block BEL Layout', '1st Block HBR Layout',\n",
+       "        '1st Block HRBR Layout', '1st Block Jayanagar',\n",
+       "        '1st Block Koramangala', '1st Phase JP Nagar',\n",
+       "        '1st Stage Indira Nagar', '2nd Block Bel Layout',\n",
+       "        '2nd Block Hrbr Layout', '2nd Block Jayanagar',\n",
+       "        '2nd Phase JP Nagar', '2nd Phase Judicial Layout',\n",
+       "        '2nd Stage Arekere Mico Layout', '2nd Stage Nagarbhavi',\n",
+       "        '3rd Block Banashankari', '3rd Block Hrbr Layout',\n",
+       "        '3rd Block Jayanagar', '3rd Block Koramangala',\n",
+       "        '3rd Phase JP Nagar', '4th Block Jayanagar',\n",
+       "        '4th Block Koramangala', '4th Phase JP Nagar',\n",
+       "        '4th T block Jayanagar', '5th Block Hbr Layout',\n",
+       "        '5th Phase JP Nagar', '5th Stage BEML Layout',\n",
+       "        '6th Phase JP Nagar', '6th block Koramangala',\n",
+       "        '7th Block Jayanagar', '7th Phase JP Nagar', '8th Block Jayanagar',\n",
+       "        '8th Phase JP Nagar', '8th block Koramangala',\n",
+       "        '9th Phase JP Nagar', 'A Narayanapura', 'AECS Layout',\n",
+       "        'AGS Layout', 'AMS Layout', 'Abbaiah Reddy Layout', 'Abbigere',\n",
+       "        'Adityanagar', 'Agrahara Dasarahalli', 'Aishwarya Crystal Layout',\n",
+       "        'Akshaya Nagar', 'Akshaya Vana', 'Akshayanagara East',\n",
+       "        'Akshayanagara West', 'Akshya Nagar', 'Alfa Garden Layout', 'Alur',\n",
+       "        'Amam Enclave Layout', 'Amarjyothi Colony', 'Ambalipura',\n",
+       "        'Ambedkar Colony', 'Ambedkar Nagar', 'Amblipura', 'Amruthahalli',\n",
+       "        'Amruthnagar', 'Anand Nagar', 'Anand nagar', 'Anandapura',\n",
+       "        'Anantapura', 'Ananth Nagar', 'Anekal', 'Anjanapura',\n",
+       "        'Anjappa Layout', 'Ankappa Layout', 'Annaiah Reddy Layout',\n",
+       "        'Annapurneshwari Nagar', 'Anugrah Layout', 'Anwar Layout',\n",
+       "        'Ardendale', 'Arehalli', 'Arekere', 'Ashirvad Colony',\n",
+       "        'Ashok Nagar', 'Ashwath Nagar', 'Ashwathnagar', 'Ashwini layout',\n",
+       "        'Atmananda Colony', 'Attibele', 'Attur Layout', 'Austin Town',\n",
+       "        'Avalahalli', 'Ayappa Nagar', 'B Channasandra', 'B Narayanapura',\n",
+       "        'BCC Layout', 'BCMC Layout', 'BEL Road', 'BEML Layout',\n",
+       "        'BHEL Layout', 'BSM Extension', 'BTM 1st Stage', 'BTM 2nd Stage',\n",
+       "        'BTM 4th Stage', 'BTM Layout', 'Baba Nagar', 'Babusapalaya',\n",
+       "        'Badavala Nagar', 'Bagalakunte', 'Bagalur', 'Bagalur Main Road',\n",
+       "        'Balagere', 'Balaji Gardens Layout', 'Banagiri Nagar',\n",
+       "        'Banashankari', 'Banashankari Stage II', 'Banashankari Stage III',\n",
+       "        'Banashankari Stage V', 'Banashankari Stage VI', 'Banaswadi',\n",
+       "        'Banjara Layout', 'Bank Of Baroda Colony', 'Bannerghatta',\n",
+       "        'Bannerghatta Road', 'Basapura', 'Basava Nagar', 'Basavanagara',\n",
+       "        'Basavanapura', 'Basavangudi', 'Basavanna Nagar',\n",
+       "        'Basaveshwara Nagar', 'Basaveshwara Nagar Yelahanka',\n",
+       "        'Battarahalli', 'Begur', 'Begur Road', 'Belathur', 'Belatur',\n",
+       "        'Bellandur', 'Bellari Road', 'Bendiganahalli', 'Benson Town',\n",
+       "        'Bethel Nagar', 'Bettahalsoor', 'Bhagyalakshmi Avenue',\n",
+       "        'Bharathi Nagar', 'Bhoganhalli', 'Bhoopsandra',\n",
+       "        'Bhuvaneshwari Nagar', 'Bhuvaneswari Nagar', 'Bidadi',\n",
+       "        'Bidrahalli', 'Bikasipura', 'Bileshivale', 'Billekahalli',\n",
+       "        'Binny Pete', 'Bisuvanahalli', 'Bommanahalli', 'Bommasandra',\n",
+       "        'Bommasandra Industrial Area', 'Bommenahalli', 'Brindavan Layout',\n",
+       "        'Brindavan Nagar', 'Brooke Bond First Cross', 'Brookefield',\n",
+       "        'Budigere', 'Byadarahalli', 'Byagadadhenahalli', 'Byatarayanapura',\n",
+       "        'Byrasandra', 'Byrathi Village', 'CQAL Layout', 'CV Raman Nagar',\n",
+       "        'Cambridge Layout', 'Canara Bank Colony', 'Canara Bank Layout',\n",
+       "        'Carmelaram', 'Celebrity Paradise Layout', 'Challaghatta',\n",
+       "        'Chamrajpet', 'Chamundi Nagar', 'Chandapura', 'Chandra Layout',\n",
+       "        'Channasandra', 'Channasandra Layout', 'Chelekare',\n",
+       "        'Chennammana Kere', 'Chennammanakere Achukattu',\n",
+       "        'Chennappa Layout', 'Chikka Banaswadi', 'Chikka Tirupathi',\n",
+       "        'Chikkabanavar', 'Chikkadunnasandra', 'Chikkakannalli',\n",
+       "        'Chikkalasandra', 'Chikkasandra', 'Chikkathoguru',\n",
+       "        'Chinnapanahalli', 'Chokkanahalli', 'Cholanayakanahalli',\n",
+       "        'Choodasandra', 'Classic Paradise Layout', 'Cleveland Town',\n",
+       "        'Coconut Grove Layout', 'Coffee Board Layout', 'Cooke Town',\n",
+       "        'Cottonpet', 'Cox Town', 'Crimson Layout',\n",
+       "        'D Group Employees Layout', 'Daadys Gaarden Layout',\n",
+       "        'Dairy Circle', 'Dasanapura', 'Dasarahalli', 'Dena Bank Colony',\n",
+       "        'Devanahalli', 'Devanahalli Int. Airport', 'Devarabeesana Halli',\n",
+       "        'Devarachikkanahalli', 'Devasthanagalu', 'Devi Nagar', 'Dinnur',\n",
+       "        'Divya Unnathi Layout', 'Doctors Layout', 'Dodda Banaswadi',\n",
+       "        'Dodda Kempaiah Layout', 'Dodda Nekkundi',\n",
+       "        'Dodda Nekkundi Extension', 'Doddaballapur', 'Doddabanahalli',\n",
+       "        'Doddabidrakallu', 'Doddabommasandra', 'Doddagubbi',\n",
+       "        'Doddakallasandra', 'Doddakammanahalli', 'Doddakannelli',\n",
+       "        'Doddanakundi Industrial Area 2', 'Doddanekundi', 'Doddathoguru',\n",
+       "        'Dodsworth Layout', 'Dollar Scheme Colony', 'Dollars Colony',\n",
+       "        'Dollars Layout', 'Domlur', 'Domlur Layout', 'Dommasandra',\n",
+       "        'Doopanahalli', 'Dooravani Nagar', 'Dr Shivarama Karantha Nagar',\n",
+       "        'Dwarka Nagar', 'ECC Road, Whitefield,', 'EPIP Zone', 'Ejipura',\n",
+       "        'Electronic City', 'Electronic City Phase II',\n",
+       "        'Electronic city Phase 1,', 'Electronics City Phase 1',\n",
+       "        'Esther Enclave Layout', 'Ferrar Nagar', 'Frazer Town',\n",
+       "        'Friends Colony', 'GD Layout', 'GM Palaya', 'Ganesha Block',\n",
+       "        'Ganga Nagar', 'Garebhavipalya', 'Garudachar Palya', 'Gattahalli',\n",
+       "        'Gaurava Nagar', 'Geddalahalli', 'Giri Nagar', 'Gkvk Layout',\n",
+       "        'Glass Factory Layout', 'Gnana Bharathi', 'Gokula Extension',\n",
+       "        'Gollahalli', 'Gollarapalya Hosahalli', 'Gopalapura',\n",
+       "        'Gopalkrishna Nagar', 'Gottigere', 'Govindapura',\n",
+       "        'Govindaraja Nagar Ward', 'Govindpura', 'Gowdanapalya',\n",
+       "        'Green Domain Layout', 'Green Garden Layout', 'Green Glen Layout',\n",
+       "        'Green View Layout', 'Green Woods Layout', 'Gubbalala',\n",
+       "        'Guddadahalli', 'Gulimangala', 'Gunjur', 'Gunjur Palya',\n",
+       "        'HAL 2nd Stage', 'HAL 3rd Stage', 'HBR Layout', 'HMT Layout',\n",
+       "        'HOSUR MAIN ROAD', 'HRBR Layout', 'HSR Layout', 'Hadosiddapura',\n",
+       "        'Hagadur', 'Hallehalli', 'Hanumanth Nagar', 'Hanumantha Nagar',\n",
+       "        'Haralur Road', 'Harappanahalli', 'Harlur', 'Harsha Layout',\n",
+       "        'Hebbal', 'Hebbal Kempapura', 'Hegde Nagar', 'Hegganahalli',\n",
+       "        'Hennur', 'Hennur Bande', 'Hennur Gardens', 'Hennur Road',\n",
+       "        'Herohalli', 'Hessarghatta', 'Himagiri Meadows', 'Hiremath Layout',\n",
+       "        'Hongasandra', 'Hoodi', 'Hoodi Circle,', 'Hoodi Layout',\n",
+       "        'Horamavu Agara', 'Horamavu Banaswadi', 'Hormavu', 'Hosa Road',\n",
+       "        'Hosahalli Extension', 'Hosakerehalli', 'Hosakerehalli Layout',\n",
+       "        'Hosapalya', 'Hoskote', 'Hosur Road', 'Hoysalanagar', 'Hulimavu',\n",
+       "        'Huskur', 'ISRO Layout', 'ITI Layout', 'ITPL', 'Iblur Village',\n",
+       "        'Immadihalli', 'Indira Nagar', 'Ittamadu', 'J C Nagar',\n",
+       "        'JCR Layout', 'JP Nagar', 'JP Nagar 7th Phase,',\n",
+       "        'JP Nagar 8th Phase,', 'Jai Bheema Nagar', 'Jakkasandra Extension',\n",
+       "        'Jakkur', 'Jakkur Plantation', 'Jakkuru Layout', 'Jalahalli',\n",
+       "        'Jalahalli East', 'Jalahalli West', 'Janatha Colony',\n",
+       "        'Jaya Mahal layout', 'Jayamahal', 'Jayanagar', 'Jayanti Nagar',\n",
+       "        'Jeevan bima nagar', 'Jigani', 'Jinkethimmanahalli',\n",
+       "        'Jnana Ganga Nagar', 'Jnanabharathi Layout', 'Judicial Layout',\n",
+       "        'Judicial Layout, Kanakapura Road,', 'Jyothi Nagar', 'KEB Colony',\n",
+       "        'KR Garden', 'KR Layout', 'KR Puram', 'KSRTC Layout',\n",
+       "        'KUDLU MAIN ROAD', 'Kachanayakanahalli', 'Kacharakanahalli',\n",
+       "        'Kada Agrahara', 'Kadabagere', 'Kadubeesanahalli', 'Kadugodi',\n",
+       "        'Kadugondanahalli', 'Kaggadasapura', 'Kaggalipura',\n",
+       "        'Kaikondrahalli', 'Kalena Agrahara', 'Kalkere', 'Kallumantapa',\n",
+       "        'Kalyan nagar', 'Kamakshipalya', 'Kamakya Layout', 'Kamala Nagar',\n",
+       "        'Kambipura', 'Kammagondahalli', 'Kammanahalli', 'Kammasandra',\n",
+       "        'Kanaka Nagar', 'Kanakapura', 'Kanakpura Road', 'Kannamangala',\n",
+       "        'Kariyammana Agrahara', 'Karuna Nagar', 'Kasavanhalli',\n",
+       "        'Kashi Nagar', 'Kasturi Nagar', 'Kathriguppe', 'Kattigenahalli',\n",
+       "        'Kaval Byrasandra', 'Kaverappa Layout', 'Kaveri Nagar',\n",
+       "        'Kempapura', 'Kempegowda Nagar', 'Kenchenahalli', 'Kenchenhalli',\n",
+       "        'Kengeri', 'Kengeri Hobli', 'Kengeri Satellite Town',\n",
+       "        'Kereguddadahalli', 'Keshava Nagar', 'Kirloskar Layout',\n",
+       "        'Kithaganur', 'Kodathi', 'Kodbisanhalli', 'Kodichikkanahalli',\n",
+       "        'Kodigehaali', 'Kodigehalli', 'Kodihalli', 'Kodipalya', 'Kogilu',\n",
+       "        'Konanakunte', 'Konanakunte Cross', 'Konena Agrahara',\n",
+       "        'Koramangala', 'Koramangala Industrial Layout', 'Kothannur',\n",
+       "        'Kothanur', 'Kothnoor Dinne', 'Krishna Reddy Layout', 'Kudlu',\n",
+       "        'Kudlu Gate', 'Kullappa Colony', 'Kumara Park', 'Kumarapalli',\n",
+       "        'Kumaraswami Layout', 'Kumbena Agrahara', 'Kundalahalli',\n",
+       "        'Kundalahalli Colony', 'Kurubarahalli', 'Kuvempu Nagar',\n",
+       "        'Kyalasanahalli', 'LB Shastri Nagar', 'Laggere', 'Lake City',\n",
+       "        'Lakshmiamma Garden', 'Lakshminarayana Pura', 'Lakshmipura',\n",
+       "        'Lal Bahadur Shastri Nagar', 'Langford Town', 'Lavakusha Nagar',\n",
+       "        'Laxmi Sagar Layout', 'Lingadheeranahalli', 'Lingarajapuram',\n",
+       "        'Lottegolla Halli', 'MCECHS  layout', 'MLA Layout', 'MS Pallya',\n",
+       "        'Madiwala', 'Magadi Road', 'Mahadevpura', 'Mahaganapathy Nagar',\n",
+       "        'Mahalakshmi Layout', 'Mahalakshmi Puram', 'Maithri Layout',\n",
+       "        'Makali', 'Mallasandra', 'Mallathahalli', 'Malleshpalya',\n",
+       "        'Malleshwaram', 'Manayata Tech Park', 'Mangammanapalya',\n",
+       "        'Manjunatha Layout', 'Manorayana Palya', 'Maragondanahalli',\n",
+       "        'Marasandra', 'Marathahalli', 'Marenahalli', 'Margondanahalli',\n",
+       "        'Mariyannapalya', 'Marsur', 'Maruthi Nagar', 'Maruthi Sevanagar',\n",
+       "        'Mathikere', 'Mathikere Extension', 'Medahalli', 'Meenakunte',\n",
+       "        'Mico Layout', 'Moodalapalya', 'Motappa Layout',\n",
+       "        'Muneshwara Nagar', 'Munivenkatppa Layout', 'Munnekollal',\n",
+       "        'Murugeshpalya', 'Muthurayya Swamy Layout', 'Mylasandra',\n",
+       "        'Mysore Highway', 'Mysore Road', 'NGR Layout', 'NRI Layout',\n",
+       "        'NS Palya', 'Nagadevanahalli', 'Naganathapura',\n",
+       "        'Nagappa Reddy Layout', 'Nagaraja Garden', 'Nagarbhavi',\n",
+       "        'Nagasandra', 'Nagashetty Halli', 'Nagavara', 'Nagavarapalya',\n",
+       "        'Nagawara Junction', 'Nagondanahalli', 'Naidu Layout',\n",
+       "        'Nallurhalli', 'Nandi Durga Road', 'Nandi Hills', 'Nandini Layout',\n",
+       "        'Nanjappa Garden', 'Nanjappa Layout', 'Narayana Nagar 1st Block',\n",
+       "        'Narayanapura', 'Nayandanahalli', 'Near International Airport',\n",
+       "        'Neeladri Nagar', 'Neelamangala', 'Nehru Nagar', 'Nelamangala',\n",
+       "        'New Gurappana Palya', 'New Thippasandra', 'Ngef Layout',\n",
+       "        'Nobo Nagar', 'Nyanappana Halli', 'OLd Gurappanapalya',\n",
+       "        'OMBR Layout', 'Off Sarjapur Road,', 'Old Airport Road',\n",
+       "        'Old Madras Road', 'Omarbagh Layout', 'Omkar Nagar',\n",
+       "        'Outer Ring Road East', 'P&T Layout', 'Padmanabhanagar',\n",
+       "        'Pai Layout', 'Palace Road', 'Pampa Extension', 'Panathur',\n",
+       "        'Panduranga Nagar', 'Parappana Agrahara', 'Patelappa Layout',\n",
+       "        'Pattanagere', 'Pattandur Agrahara', 'Peenya',\n",
+       "        'Phase 1 Kammasandra', 'Poorna Pragna Layout',\n",
+       "        'Poornapragna Housing Society Layout', 'Pragathi Nagar',\n",
+       "        'Prashanth Nagar', 'Prithvi Layout', 'Pulkeshi Nagar',\n",
+       "        'Puttanahalli', 'R.T. Nagar', 'RMV', 'RMV 2nd Stage',\n",
+       "        'RMV Extension', 'RMV Extension Stage 2', 'RPC layout',\n",
+       "        'RR Layout', 'RWF West Colony', 'Rachenahalli',\n",
+       "        'Raghavendra Layout', 'Raghavendra Nagar', 'Raghuvanahalli',\n",
+       "        'Raja Rajeshwari Nagar', 'Raja Rajeshwari Nagar 5th Stage',\n",
+       "        'Rajaji Nagar', 'Rajankunte', 'Rajarajeshwari Nagara',\n",
+       "        'Rajarajeshwari nagar', 'Rajarajeshwarinagar', 'Rajasree Layout',\n",
+       "        'Rajiv Gandhi Nagar', 'Rajiv Nagar', 'Ramagondanahalli',\n",
+       "        'Ramakrishnappa Layout', 'Ramamurthy Nagar',\n",
+       "        'Ramamurthy Nagar Extension', 'Ramanashree Enclave',\n",
+       "        'Ramanjaneyanagar', 'Ramesh Nagar', 'Rayasandra',\n",
+       "        'Reliaable Tranquil Layout', 'Remco Bhel Layout', 'Richards Town',\n",
+       "        'Richmond Town', 'Roopena Agrahara', 'Rustam Bagh Layout',\n",
+       "        'SRINIVASAPURA', 'Sadanand Nagar', 'Sadaramangala',\n",
+       "        'Sahakara Nagar', 'Sai Gardens', 'Samethanahalli',\n",
+       "        'Sampangi Rama Nagar', 'Sampigehalli', 'Sanjay nagar',\n",
+       "        'Sanjeevini Nagar', 'Sanne Amanikere', 'Sarakki Nagar', 'Sarjapur',\n",
+       "        'Sarjapur  Road', 'Sarjapur Road,', 'Sarjapura - Attibele Road',\n",
+       "        'Sarvabhouma Nagar', 'Sathya Layout', 'Sathya Sai Layout',\n",
+       "        'Sector 1 HSR Layout', 'Sector 2 HSR Layout',\n",
+       "        'Sector 6 HSR Layout', 'Sector 7 HSR Layout', 'Seegehalli',\n",
+       "        'Seetharampalya', 'Seshadripuram', 'Shampura', 'Shankarapuram',\n",
+       "        'Shanthi Layout', 'Shanti Nagar', 'Shantiniketan Layout',\n",
+       "        'Shettigere', 'Shetty Halli', 'Shikaripalya', 'Shingapura',\n",
+       "        'Shirdi Sai Layout', 'Shivaji Nagar', 'Shree Ananth Nagar Layout',\n",
+       "        'Siddapura', 'Sidedahalli', 'Silk Board', 'Silver Springs Layout',\n",
+       "        'Singanayakanahalli', 'Singapura Village', 'Singasandra',\n",
+       "        'Singena Agrahara', 'Sneha Colony', 'Somasundara Palya', 'Sompura',\n",
+       "        'Sonnenahalli', 'Soundarya Layout', 'Sri Balaji Krupa Layout',\n",
+       "        'Sri Sai Layout', 'Sri Venkateshpura Layout', 'Srinagar',\n",
+       "        'Srinivasa Nagar', 'Srirampura', 'Srirampuram', \"St. John's Road\",\n",
+       "        'Stage-4 Bommanahalli', 'Subash Nagar', 'Subramanyapura',\n",
+       "        'Suddaguntepalya', 'Sultan Palaya', 'Sunder Ram Shetty Nagar',\n",
+       "        'Sunkadakatte', 'Surabhi Layout', 'Suraksha Nagar',\n",
+       "        'Syndicate Bank Colony', 'T Dasarahalli', 'T.C PALYA', 'TC Palaya',\n",
+       "        'Tala Cauvery Layout', 'Talaghattapura', 'Tasker Town',\n",
+       "        'Tata Nagar', 'Tavarekere', 'Teachers Colony', 'Tejaswini Nagar',\n",
+       "        'Telecom Layout', 'Thanisandra', 'Thanisandra Main Road,',\n",
+       "        'Thigalarapalya', 'Thippasandra', 'Thirumenahalli', 'Thomas Town',\n",
+       "        'Thubarahalli', 'Thyagaraja Nagar', 'Tigalarpalya', 'Tindlu',\n",
+       "        'Tirumanahalli', 'Tumkur Road', 'Tunganagara', 'Udaya Nagar',\n",
+       "        'Udayapur Village', 'Ullal Uppanagar', 'Ulsoor',\n",
+       "        'Upadhyaya Layout', 'Upkar Layout', 'Uttarahalli', 'VGP Layout',\n",
+       "        'VHBCS Layout', 'Vadarpalya', 'Vaderahalli', 'Vaishnavi Layout',\n",
+       "        'Vajarahalli', 'Varanasi', 'Varsova Layout', 'Varthur',\n",
+       "        'Varthur Road', 'Varthur Road,', 'Vasantha Vallabha Nagar',\n",
+       "        'Vasanthapura', 'Veer Sandra', 'Veerannapalya', 'Veersandra',\n",
+       "        'Venkatadri Layout', 'Venkatapura', 'Venugopal Reddy Layout',\n",
+       "        'Vibuthipura', 'Victoria Layout', 'Vidyaranyapura',\n",
+       "        'Vignana Nagar', 'Vijaya Bank Layout', 'Vijayanagar', 'Vijinapura',\n",
+       "        'Vimanapura', 'Vinayak Nagar', 'Vinayaka Nagar', 'Virat Nagar',\n",
+       "        'Virupakshapura', 'Vishveshwarya Layout',\n",
+       "        'Vishwanatha Nagenahalli', 'Vishwapriya Layout',\n",
+       "        'Vishwapriya Nagar', 'Vittal Nagar', 'Vittasandra', 'Vivek Nagar',\n",
+       "        'Volagerekallahalli', 'Weavers Colony', 'Whitefield',\n",
+       "        'Whitefield,', 'Wilson Garden', 'Yarandahalli', 'Yelachenahalli',\n",
+       "        'Yelahanka', 'Yelahanka New Town', 'Yelenahalli', 'Yemlur',\n",
+       "        'Yeshwanthpur', 'Yeshwanthpur Industrial Suburb', 'cooketown',\n",
+       "        'manyata park', 'tc.palya'], dtype=object)]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "4db98a15-351d-4aaa-b296-0177bb60cd94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.00e+00, 0.00e+00, 0.00e+00, ..., 3.00e+00, 1.54e+03, 3.00e+00],\n",
+       "       [1.00e+00, 0.00e+00, 0.00e+00, ..., 1.00e+00, 6.00e+02, 1.00e+00],\n",
+       "       [1.00e+00, 0.00e+00, 0.00e+00, ..., 4.00e+00, 3.15e+03, 4.00e+00],\n",
+       "       ...,\n",
+       "       [0.00e+00, 0.00e+00, 0.00e+00, ..., 2.00e+00, 8.80e+02, 2.00e+00],\n",
+       "       [0.00e+00, 0.00e+00, 0.00e+00, ..., 2.00e+00, 1.00e+03, 2.00e+00],\n",
+       "       [0.00e+00, 0.00e+00, 0.00e+00, ..., 3.00e+00, 1.40e+03, 2.00e+00]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.append(location_encoding[:,1:],np.array(df2.drop(['location','price'],axis=1)),axis=1)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1648f0c5-19ba-474f-8e4b-72fa066a6972",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "44a3f1b9-c16e-4829-893c-598a042819e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8764,)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df2['price']\n",
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "91524bc6-2a66-4543-a4d3-75b19c9c5a70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8764, 754)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "5f82b539-6aa7-4546-967a-40558898c55b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "4a3e86aa-e91a-43f0-865a-889d0b4f3ae1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "lr = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "f313918d-6d8b-4868-8f3b-b793de473bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1667207110599217.2"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr.fit(x_train,y_train)\n",
+    "lr.score(x_test,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "636ea141-22a8-4338-89ea-da3f47b4c298",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV,cross_val_score,ShuffleSplit\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.linear_model import Lasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a4117694-4aa7-4773-ab21-34b4f3d1516b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "choices = {\n",
+    "    'lr':{\n",
+    "        'model':LinearRegression(),\n",
+    "        'params':{\n",
+    "            'normalize':[True,False]\n",
+    "        }\n",
+    "    },\n",
+    "    'lasso': {\n",
+    "        'model':Lasso(),\n",
+    "        'params':{\n",
+    "            'alpha':[1,2],\n",
+    "            'selection' : ['cyclic', 'random']\n",
+    "        }\n",
+    "    },\n",
+    "    'tree': {\n",
+    "        'model':DecisionTreeRegressor(),\n",
+    "        'params':{\n",
+    "            'criterion' : [\"mse\", \"friedman_mse\"],\n",
+    "            'splitter' : [\"best\", \"random\"]\n",
+    "        }\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "41880a33-cc7b-48e1-b650-cbdb32ed9243",
+   "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>model</th>\n",
+       "      <th>best_score</th>\n",
+       "      <th>best_params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>lr</td>\n",
+       "      <td>-7.365020e+14</td>\n",
+       "      <td>{'normalize': False}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>lasso</td>\n",
+       "      <td>8.020881e-01</td>\n",
+       "      <td>{'alpha': 1, 'selection': 'random'}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>tree</td>\n",
+       "      <td>7.988632e-01</td>\n",
+       "      <td>{'criterion': 'mse', 'splitter': 'random'}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   model    best_score                                 best_params\n",
+       "0     lr -7.365020e+14                        {'normalize': False}\n",
+       "1  lasso  8.020881e-01         {'alpha': 1, 'selection': 'random'}\n",
+       "2   tree  7.988632e-01  {'criterion': 'mse', 'splitter': 'random'}"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def best_model_param_pair(choices):\n",
+    "    cv = ShuffleSplit(n_splits=5,test_size=0.2,random_state=0)\n",
+    "    data = []\n",
+    "    for mn,mp in choices.items():\n",
+    "        gs = GridSearchCV(mp['model'],mp['params'],cv=cv)\n",
+    "        gs.fit(x,y)\n",
+    "        data.append({\n",
+    "        'model':mn,\n",
+    "        'best_score': gs.best_score_,\n",
+    "        'best_params':gs.best_params_\n",
+    "        })\n",
+    "    return pd.DataFrame(data)\n",
+    "daaa= best_model_param_pair(choices)\n",
+    "daaa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "cc5af773-698a-4e68-b974-5190b4dac88b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(location,bhk,tsqft,bath):\n",
+    "    x=ohe.transform([[location]]).toarray()\n",
+    "    x=np.append(x[:,1:],np.array([bhk,tsqft,bath]))\n",
+    "    print(lr.predict(x.reshape(1,-1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "fa5a025a-35df-4bed-853d-1aeabbae4583",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[94.38034082]\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict('Devarabeesana Halli',2,1100.0,2.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "6e3dca9e-dd82-4add-80ed-d4ef7b3a2f07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-57-5d7303250545>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df2[df2.location=='Devarabeesana Halli'][df2.total_sqft==1100]\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>location</th>\n",
+       "      <th>size</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2764</th>\n",
+       "      <td>Devarabeesana Halli</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1100.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>70.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 location  size  total_sqft  bath  price\n",
+       "2764  Devarabeesana Halli     2      1100.0   2.0   70.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[df2.location=='Devarabeesana Halli'][df2.total_sqft==1100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "3214fdf2-6268-4c8f-9534-e9d106fec8f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[84.99999962]\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict('1st Block BEL Layout',3,1540.0,3.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "c5fd9afa-fd72-41c9-8610-f8712d55af35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open('banglore_price_prediction_model.pickle','wb') as f:\n",
+    "    pickle.dump(lr,f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "40dc20e3-6872-4eec-80b3-dab5c140e445",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('location_encoder.pickle','wb') as l:\n",
+    "    pickle.dump(ohe,l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "b2cf33b0-97d1-4a53-9972-9d26b4b6c3b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1st Block BEL Layout'"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open('location_encoder.pickle','rb') as lc:\n",
+    "    le= pickle.load(lc)\n",
+    "loc = le.categories_[0]\n",
+    "loc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "79ba7e3d-9b96-4d22-a2f9-c79f46ba20e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "       0., 0., 0.])"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le.transform([['1st Block BEL Layout']]).toarray()[0][1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad3fb7ef-6346-414e-9ddd-24d4743830a0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdcc5725563728fae38ba901ff4850b8c3df42614eebafa92d2781dbc36756fd
+size 12533

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+size 13121

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+function n(a, b) {
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+     catch (e) {
+ } return A[a]
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server.py ADDED Viewed

	@@ -0,0 +1,44 @@

+import util
+import json
+from flask import Flask, request, jsonify
+app = Flask(__name__)
+white =['http://127.0.0.1:5500']
+@app.after_request
+def add_cors_headers(response):
+    r = request.referrer[:-1]
+    if r in white:
+        response.headers.add('Access-Control-Allow-Origin', r)
+        response.headers.add('Access-Control-Allow-Credentials', 'true')
+        response.headers.add('Access-Control-Allow-Headers', 'Content-Type')
+        response.headers.add('Access-Control-Allow-Headers', 'Cache-Control')
+        response.headers.add('Access-Control-Allow-Headers', 'X-Requested-With')
+        response.headers.add('Access-Control-Allow-Headers', 'Authorization')
+        response.headers.add('Access-Control-Allow-Methods', 'GET, POST, OPTIONS, PUT, DELETE')
+    return response
+@app.route('/h')
+def hello():
+    return "hi"
+@app.route('/loc')
+def get_location():
+    util.load_assests()
+    obj = jsonify(util.__location_list.tolist())
+    return obj
+@app.route('/get_price', methods=['POST'])
+def get_price():
+    totat_sqft = float(request.form['total_sqft'])
+    location = request.form['location']
+    bhk = int(request.form['bhk'])
+    bath = int(request.form['bath'])
+    util.load_assests()
+    price = util.get_estimated_price(location=location, bhk=bhk, bath=bath, tsqft=totat_sqft)
+    return str(price)
+if __name__ == "__main__":
+    print("starting")
+    app.run()

util.py ADDED Viewed

	@@ -0,0 +1,30 @@

+import pickle
+import numpy as np
+__model =None
+__location_encoder = None
+__location_list = None
+def load_assests():
+    global __model
+    global __location_encoder
+    global __location_list
+    with open('assets/banglore_price_prediction_model.pickle', 'rb') as f:
+        __model = pickle.load(f)
+    with open('assets/location_encoder.pickle', 'rb') as ld:
+        __location_encoder= pickle.load(ld)
+    __location_list = __location_encoder.categories_[0]
+def get_estimated_price(location,bhk,tsqft,bath):
+    try:
+        x = __location_encoder.transform([[location]]).toarray()[0]
+    except:
+        x = np.zeros(len(__location_list))
+    x = np.append(x[1:], np.array([bhk, tsqft, bath]))
+    return __model.predict(x.reshape(1, -1))[0]
+# load_assests()
+# get_estimated_price('Devarabeesana Halli', 2, 1100.0, 2.0)