BengaluruHousePriceRegression.ipynb

{
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   },
   "source": [
    "\n",
    "### Predicting House Prices In Bengaluru"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HFpoZ3mXiRfP"
   },
   "source": [
    "<img src=\"https://www.machinehack.com/wp-content/uploads/2018/03/bangalore-now.jpg\" alt=\"drawing\" width=\"500\" height =\"400\"/>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "---\n",
    "## `Source=`[MachineHack](https://www.machinehack.com/course/predicting-house-prices-in-bengaluru/)\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uhj4eQBriRfU"
   },
   "source": [
    "Features\n",
    "\n",
    "- [Area_type](https://codeperfectplus.github.io/codes_perfect/) – `describes the area`\n",
    "- [Availability](https://codeperfectplus.github.io/codes_perfect/) – `when it can be possessed or when it is ready(categorical and time-series)`\n",
    "- [Location](https://codeperfectplus.github.io/codes_perfect/) – `where it is located in Bengaluru`\n",
    "- [Price](https://codeperfectplus.github.io/codes_perfect/) – `Value of the property in lakhs(INR)`\n",
    "- [Size](https://codeperfectplus.github.io/codes_perfect/) – `in BHK or Bedroom (1-10 or more)`\n",
    "- [Society](https://codeperfectplus.github.io/codes_perfect/) – `to which society it belongs`\n",
    "- [Total_sqft](https://codeperfectplus.github.io/codes_perfect/) – `size of the property in sq.ft`\n",
    "- [Bath](https://codeperfectplus.github.io/codes_perfect/) – `No. of bathrooms`\n",
    "- [Balcony](https://codeperfectplus.github.io/codes_perfect/) – `No. of the balcony` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yZ8nmz2BiRfX"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np  # For Linear Algebra and math\n",
    "import pandas as pd # For Dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lBUP0-d8iRfo"
   },
   "outputs": [],
   "source": [
    "# For Displaying Good Quality Graphs\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OdLADF30iRf0"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder           # Change String Data to int value by LabelEncoding \n",
    "from sklearn.model_selection import train_test_split     # Split Data Into train_test for Check accuracy\n",
    "from sklearn.metrics import mean_absolute_error          # Mean Absolute Error\n",
    "from sklearn.ensemble import RandomForestRegressor       # Random_Forest_Regressor\n",
    "from sklearn.metrics import r2_score                     # R2_Score\n",
    "from joblib import dump                                  # To Save Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KmSUR0nTiRgB"
   },
   "source": [
    "----\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TPuqMPoniRgE"
   },
   "outputs": [],
   "source": [
    "# Load train Data into train_df variable\n",
    "train_df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "gQNzse22iRgQ",
    "outputId": "9e5a440e-d8c9-45c0-aa6b-d33b50f3342f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset :\n",
      "\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>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Train Dataset :\\n\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "w6Zqm4-FiRgj",
    "outputId": "7105954b-8ca9-43df-e931-b143d397dd38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data-types of each columns in train dataset:\n",
      "\n",
      "area_type        object\n",
      "availability     object\n",
      "location         object\n",
      "size             object\n",
      "society          object\n",
      "total_sqft       object\n",
      "bath            float64\n",
      "balcony         float64\n",
      "price           float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Analyze DataType In Each-Columns\n",
    "print(\"Data-types of each columns in train dataset:\\n\")\n",
    "print(train_df.dtypes)\n",
    "#There Is Need to Change Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1dfgd2PniRgw",
    "outputId": "50307d5a-8a2a-41ff-e8c8-2162cfca2507"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset Size  : (13320, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Dataset Size  :\", train_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zaXLspJmiRhA"
   },
   "source": [
    "check missing values (NaNs) in train dataset 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "cDA7XieyiRhC",
    "outputId": "c367525b-93e2-46d8-deb2-1d07e147693e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing values in train dataset column wise:\n",
      "\n",
      "area_type          0\n",
      "availability       0\n",
      "location           1\n",
      "size              16\n",
      "society         5502\n",
      "total_sqft         0\n",
      "bath              73\n",
      "balcony          609\n",
      "price              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"missing values in train dataset column wise:\\n\")\n",
    "print(train_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_7wC0x5JiRhP",
    "outputId": "ebff9693-92ff-43c7-d78f-97022ccdbc15"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of Missing Values Society Column In Train dataset : 41 %\n"
     ]
    }
   ],
   "source": [
    "print(\"percentage of Missing Values Society Column In Train dataset :\",int(5502/train_df.shape[0]*100),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t7OjWcXOiRhc"
   },
   "source": [
    "###### Reason  For Missing Values\n",
    "  - Maybe all these house doesn't belong to any society.\n",
    "  - Or, because of some data gathering problem these records has missing society value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6yEzniCdiRhe"
   },
   "source": [
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-5JDaC3qiRhh"
   },
   "source": [
    "## `Outlier Value Visualizations`\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "id": "mY4nsEfsiRhk",
    "outputId": "fda771f0-c237-4684-ab3c-4a3f7a51c690"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "sns.boxplot(train_df['price'], ax=ax)\n",
    "ax.set(xlabel= 'price in lakhs(INR)', title='Box plot of price')\n",
    "#plt.savefig(\"Graph/Box plot of price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Y9fO6RCLiRhw",
    "outputId": "e6595048-33ae-478f-ee1a-7e0117cf6d46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode of Price : 75.0\n"
     ]
    }
   ],
   "source": [
    "# find mode (most frequent occuring price value) of price\n",
    "print(\"Mode of Price :\", train_df['price'].mode()[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "a2MRiKsviRh8",
    "outputId": "fb2552c2-0a49-4df4-c942-3a99ea0713bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptive statistics of price column:\n",
      "\n",
      "count    13320.000000\n",
      "mean       112.565627\n",
      "std        148.971674\n",
      "min          8.000000\n",
      "25%         50.000000\n",
      "50%         72.000000\n",
      "75%        120.000000\n",
      "max       3600.000000\n",
      "Name: price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Descriptive statistics of price column:\\n')\n",
    "print(train_df['price'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UZL25K-ciRiI"
   },
   "source": [
    "----\n",
    "## `Missing Value Visualization`\n",
    "### Column-Wise\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "su2CdTRaiRiK",
    "outputId": "11aac49a-ac3c-46f1-a22d-57608e3ed758"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Missing Value Visualization For Training Dataset \\n Column : Balcony')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "train_couts = train_df['balcony'].fillna('missing').value_counts(normalize=True)\n",
    "sns.barplot(y = train_couts.index, x= train_couts.values*100)\n",
    "ax.set(title='Missing Value Visualization For Training Dataset \\n Column : Balcony')\n",
    "#plt.savefig(\"Graph/Balcony Data Visualization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "snQtmLxsiRiX",
    "outputId": "9e03bc6e-d4a6-4dad-8fbf-d83b390d48df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Missing Value Visualization For Training Dataset \\n Column : Bathroom')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "train_couts = train_df['bath'].fillna('missing').value_counts(normalize=True)\n",
    "sns.barplot(y = train_couts.index, x= train_couts.values*100)\n",
    "ax.set(title='Missing Value Visualization For Training Dataset \\n Column : Bathroom')\n",
    "#plt.savefig(\"Graph/Bathroom Data Visualization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "colab_type": "code",
    "id": "_88QU2A0iRik",
    "outputId": "33ca2326-38b7-4aa7-d1cb-2469fe420185"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Missing Value Visualization \\n Column : Area Type')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "train_couts = train_df['area_type'].fillna('missing').value_counts(normalize=True)\n",
    "sns.barplot(y = train_couts.index, x= train_couts.values*100)\n",
    "ax.set(title='Missing Value Visualization \\n Column : Area Type')\n",
    "#plt.savefig(\"Graph/Area Type Data Visualization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bxNrHfNaiRi0"
   },
   "source": [
    "No. of House For Ready TO Move"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "9ksVCj2WiRjA",
    "outputId": "cf30855f-9e7a-4faf-a3cd-09da5b6bbbc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ready To Move    10581\n",
       "Name: availability, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['availability'].value_counts()[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "tSPd2oiNiRja",
    "outputId": "a954c4ca-d28c-43bd-ede2-17be31b51b93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Super built-up  Area    8790\n",
       "Built-up  Area          2418\n",
       "Plot  Area              2025\n",
       "Carpet  Area              87\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.area_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "bPspXKPPiRjn",
    "outputId": "092e815f-8718-4fb3-85ac-cbe3a69f467a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OEFVVL2HiRj3"
   },
   "source": [
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lc60OviXiRj_"
   },
   "outputs": [],
   "source": [
    "replace_area_type = {'Super built-up  Area': 0, 'Built-up  Area': 1, 'Plot  Area': 2, 'Carpet  Area': 3}\n",
    "train_df['area_type'] = train_df.area_type.map(replace_area_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FLNo8B8biRkN"
   },
   "outputs": [],
   "source": [
    "def replace_availabilty(my_string):\n",
    "    if my_string == 'Ready To Move':\n",
    "        return 0\n",
    "    elif my_string == 'Immediate Possession':\n",
    "        return 1\n",
    "    else:\n",
    "        return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NoQtIdNViRkZ"
   },
   "outputs": [],
   "source": [
    "train_df['availability'] = train_df.availability.apply(replace_availabilty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vCjLf5pQiRko"
   },
   "outputs": [],
   "source": [
    "def preprocess_total_sqft(my_list):\n",
    "    if len(my_list) == 1:\n",
    "        \n",
    "        try:\n",
    "            return float(my_list[0])\n",
    "        except:\n",
    "            strings = ['Sq. Meter', 'Sq. Yards', 'Perch', 'Acres', 'Cents', 'Guntha', 'Grounds']\n",
    "            split_list = re.split('(\\d*.*\\d)', my_list[0])[1:]\n",
    "            area = float(split_list[0])\n",
    "            type_of_area = split_list[1]\n",
    "            \n",
    "            if type_of_area == 'Sq. Meter':\n",
    "                area_in_sqft = area * 10.7639\n",
    "            elif type_of_area == 'Sq. Yards':\n",
    "                area_in_sqft = area * 9.0\n",
    "            elif type_of_area == 'Perch':\n",
    "                area_in_sqft = area * 272.25\n",
    "            elif type_of_area == 'Acres':\n",
    "                area_in_sqft = area * 43560.0\n",
    "            elif type_of_area == 'Cents':\n",
    "                area_in_sqft = area * 435.61545\n",
    "            elif type_of_area == 'Guntha':\n",
    "                area_in_sqft = area * 1089.0\n",
    "            elif type_of_area == 'Grounds':\n",
    "                area_in_sqft = area * 2400.0\n",
    "            return float(area_in_sqft)\n",
    "        \n",
    "    else:\n",
    "        return (float(my_list[0]) + float(my_list[1]))/2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hPzHjEAviRk4"
   },
   "outputs": [],
   "source": [
    "train_df['total_sqft'] = train_df.total_sqft.str.split('-').apply(preprocess_total_sqft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kNm9JrIhiRlL"
   },
   "outputs": [],
   "source": [
    "size_mode = train_df['size'].mode()[0]\n",
    "train_df.loc[train_df['size'].isna(), 'size'] = size_mode "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "FdqFC2wRiRlY",
    "outputId": "3767f793-c5b6-47e0-a057-44df0943615a"
   },
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Tnh0-gmCiRll"
   },
   "outputs": [],
   "source": [
    "train_df['size'] = train_df['size'].apply(lambda x: x.split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yYaxnrX1iRly"
   },
   "outputs": [],
   "source": [
    "train_df['size'] = train_df['size'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "xgsTfRzQiRl8",
    "outputId": "eed772b3-df9d-4742-9d39-f43c2389ac15"
   },
   "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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200.0</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  society  \\\n",
       "0          0             2  Electronic City Phase II   2.0  Coomee    \n",
       "1          2             0          Chikka Tirupathi   4.0  Theanmp   \n",
       "2          1             0               Uttarahalli   3.0      NaN   \n",
       "3          0             0        Lingadheeranahalli   3.0  Soiewre   \n",
       "4          0             0                  Kothanur   2.0      NaN   \n",
       "\n",
       "   total_sqft  bath  balcony   price  \n",
       "0      1056.0   2.0      1.0   39.07  \n",
       "1      2600.0   5.0      3.0  120.00  \n",
       "2      1440.0   2.0      3.0   62.00  \n",
       "3      1521.0   3.0      1.0   95.00  \n",
       "4      1200.0   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "cyKqezDeiRmI",
    "outputId": "e054e38b-430a-403f-f830-aead9e2a89c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type         int64\n",
       "availability      int64\n",
       "location         object\n",
       "size            float64\n",
       "society          object\n",
       "total_sqft      float64\n",
       "bath            float64\n",
       "balcony         float64\n",
       "price           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "Lk_OwERniRmX",
    "outputId": "789d7294-08e5-45d7-9b90-d190ee6767f5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type          0\n",
       "availability       0\n",
       "location           1\n",
       "size               0\n",
       "society         5502\n",
       "total_sqft         0\n",
       "bath              73\n",
       "balcony          609\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "YZ0dMmEviRmr",
    "outputId": "8ffad044-9004-430f-864c-b0d1de4bb2f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    5113\n",
       "1.0    4897\n",
       "3.0    1672\n",
       "0.0    1029\n",
       "Name: balcony, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.balcony.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SfeJF4nKiRm4"
   },
   "outputs": [],
   "source": [
    "bath_med = train_df['bath'].median()\n",
    "balcony_med = train_df['balcony'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HLXlgh89iRnD"
   },
   "outputs": [],
   "source": [
    "train_df.loc[train_df['bath'].isna(), 'bath'] = bath_med\n",
    "train_df.loc[train_df['balcony'].isna(), 'balcony'] = balcony_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "g7RIPQytiRnV",
    "outputId": "738cb5ab-a0dd-4f2e-cd3c-800a5af298b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type          0\n",
       "availability       0\n",
       "location           1\n",
       "size               0\n",
       "society         5502\n",
       "total_sqft         0\n",
       "bath               0\n",
       "balcony            0\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "G7I1OAFQiRnj",
    "outputId": "f2ddf833-cb92-4bb7-ae98-4633e5a87b9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                   540\n",
       "Sarjapur  Road               399\n",
       "Electronic City              302\n",
       "Kanakpura Road               273\n",
       "Thanisandra                  234\n",
       "                            ... \n",
       "Double Road                    1\n",
       "Ganga Nagar Extension          1\n",
       "Pai layout , Mahadevapura      1\n",
       "Medaralli                      1\n",
       "KAMAKIYA                       1\n",
       "Name: location, Length: 1305, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xwTQKHitiRnv"
   },
   "outputs": [],
   "source": [
    "mode_loc =train_df['location'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CxB0bwnMiRn-"
   },
   "outputs": [],
   "source": [
    "train_df.loc[train_df['location'].isna(), 'location'] = mode_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "UXO5OhU4iRog",
    "outputId": "213ddae9-4fb7-4865-c17d-d81da295a9c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type          0\n",
       "availability       0\n",
       "location           0\n",
       "size               0\n",
       "society         5502\n",
       "total_sqft         0\n",
       "bath               0\n",
       "balcony            0\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zSRJ1NIZiRpD"
   },
   "source": [
    "----\n",
    "## `Society :`\n",
    "##### Society Has Lot Of Null Values So It Will Not Consider.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "I53i5GiUiRpI",
    "outputId": "496e9449-91c4-45ee-993f-5da373a7ac45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type         int64\n",
       "availability      int64\n",
       "location         object\n",
       "size            float64\n",
       "society          object\n",
       "total_sqft      float64\n",
       "bath            float64\n",
       "balcony         float64\n",
       "price           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "U4lUwY0wiRpV",
    "outputId": "1eed41ce-e8f1-4a11-c56a-52811c7cca82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GrrvaGr    80\n",
       "PrarePa    76\n",
       "Sryalan    59\n",
       "Prtates    59\n",
       "GMown E    56\n",
       "           ..\n",
       "Krone S     1\n",
       "Beesta      1\n",
       "Sonet G     1\n",
       "Siisega     1\n",
       "SLoraFl     1\n",
       "Name: society, Length: 2688, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['society'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "vekxvdaFiRpl",
    "outputId": "439519ec-2fa7-4150-a629-c3a4854a0b53"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corelation =train_df.corr()\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(corelation,cmap=\"YlGnBu\")\n",
    "plt.title(\"Corelation Graph\")\n",
    "#plt.savefig(\"Graph/Corelation Graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "b-qr5Yf8iRp1",
    "outputId": "d2ded606-cf72-4683-8a2a-4f05ec85a527"
   },
   "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>size</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",
       "      <td>area_type</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.141044</td>\n",
       "      <td>0.373806</td>\n",
       "      <td>0.039349</td>\n",
       "      <td>0.340031</td>\n",
       "      <td>-0.112658</td>\n",
       "      <td>0.237181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>availability</td>\n",
       "      <td>-0.141044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.099558</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>-0.075379</td>\n",
       "      <td>-0.113996</td>\n",
       "      <td>-0.034416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>size</td>\n",
       "      <td>0.373806</td>\n",
       "      <td>-0.099558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054243</td>\n",
       "      <td>0.895786</td>\n",
       "      <td>0.194358</td>\n",
       "      <td>0.398074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_sqft</td>\n",
       "      <td>0.039349</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.054243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052990</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>0.048995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bath</td>\n",
       "      <td>0.340031</td>\n",
       "      <td>-0.075379</td>\n",
       "      <td>0.895786</td>\n",
       "      <td>0.052990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207970</td>\n",
       "      <td>0.454449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balcony</td>\n",
       "      <td>-0.112658</td>\n",
       "      <td>-0.113996</td>\n",
       "      <td>0.194358</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>0.207970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>price</td>\n",
       "      <td>0.237181</td>\n",
       "      <td>-0.034416</td>\n",
       "      <td>0.398074</td>\n",
       "      <td>0.048995</td>\n",
       "      <td>0.454449</td>\n",
       "      <td>0.124951</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type  availability      size  total_sqft      bath  \\\n",
       "area_type      1.000000     -0.141044  0.373806    0.039349  0.340031   \n",
       "availability  -0.141044      1.000000 -0.099558    0.004717 -0.075379   \n",
       "size           0.373806     -0.099558  1.000000    0.054243  0.895786   \n",
       "total_sqft     0.039349      0.004717  0.054243    1.000000  0.052990   \n",
       "bath           0.340031     -0.075379  0.895786    0.052990  1.000000   \n",
       "balcony       -0.112658     -0.113996  0.194358    0.008810  0.207970   \n",
       "price          0.237181     -0.034416  0.398074    0.048995  0.454449   \n",
       "\n",
       "               balcony     price  \n",
       "area_type    -0.112658  0.237181  \n",
       "availability -0.113996 -0.034416  \n",
       "size          0.194358  0.398074  \n",
       "total_sqft    0.008810  0.048995  \n",
       "bath          0.207970  0.454449  \n",
       "balcony       1.000000  0.124951  \n",
       "price         0.124951  1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corelation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EDErarIwiRqG"
   },
   "source": [
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "786FSRMliRqJ"
   },
   "source": [
    "# Apply RandomForest\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FTgWdF7JiRqN"
   },
   "outputs": [],
   "source": [
    "X = train_df[['area_type', 'availability' ,'size', 'total_sqft', 'bath', 'balcony']]\n",
    "y = train_df[['price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EUOL60PNiRqZ"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "colab_type": "code",
    "id": "OHrNu8UWiRqp",
    "outputId": "c0b49d80-92f5-4464-f2b4-c93bbd308c25"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Flash\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "                      max_features='auto', max_leaf_nodes=15,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=3, min_samples_split=10,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=600,\n",
       "                      n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=600,\n",
    "                             max_depth= 5,\n",
    "                             max_leaf_nodes= 15,\n",
    "                             min_samples_leaf= 3,\n",
    "                             min_samples_split= 10,\n",
    "                             random_state=0)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XBBiyvTiiRq2"
   },
   "outputs": [],
   "source": [
    "predict_y = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FW-Itfs3iRrC"
   },
   "source": [
    "# ` Mean-Absolute-Error`\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xHm0wFWKiRrE",
    "outputId": "d87b9afd-612a-4b11-f27b-bc44cb2f8d64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.98678246899915"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,predict_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "inDzBO1iiRrT"
   },
   "source": [
    "# `Accuracy Score`\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "G_HOgLnliRrV",
    "outputId": "94279581-9248-43ba-82ec-8d3bfcc609ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 60 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score :\",int(r2_score(y_test, predict_y)*100),\"%\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "BengaluruHousePriceRegression.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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