House_pricing (2).ipynb

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    "colab": {
      "name": "House pricing.ipynb",
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "whlNZVAh1tNd"
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      "source": [
        "# Predicting Housing Prices in Ames, Iowa\n",
        "\n",
        "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n",
        "\n",
        "With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n",
        "\n",
        "\n",
        "The goal of this project is to predict the price of every house in this dataset "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KD9vEt8CByy"
      },
      "source": [
        "Importing useful libraries and reading in the csv file. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LleVrXpYAOQb"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "train=pd.read_csv('train1.csv')\n",
        "# test=pd.read_csv('test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdARFE7pAOQh"
      },
      "source": [
        "Housing pricing and ifomation about house in Ames, Iowa(USA)\n",
        "\n",
        "Our Aim in this project is to predict the final price of each house "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FV7rW0g5AOQi",
        "outputId": "47250d69-d25a-46e7-ee88-ae73423ffb0a"
      },
      "source": [
        "print(\"Train data shape:\",train.shape)\n",
        "print(\"Test data shape:\",test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train data shape: (1460, 81)\n",
            "Test data shape: (1459, 80)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "722269apCQdE"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6AYbT__AOQl"
      },
      "source": [
        "Displaying the First 5 rows of the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIQSxbQZaxdm"
      },
      "source": [
        "We can see the dataset cocntains both catergorica and numerical data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "jf8uaJwkAOQl",
        "outputId": "d121b4bf-9cb0-45bc-982e-96d3b00a6592"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
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              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
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              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
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              "      <th>HouseStyle</th>\n",
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              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
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              "      <th>Exterior1st</th>\n",
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              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>...</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
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              "      <th>FullBath</th>\n",
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              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
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              "      <td>2Story</td>\n",
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              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
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              "      <td>196.0</td>\n",
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              "      <td>TA</td>\n",
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              "      <td>GasA</td>\n",
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              "      <td>SBrkr</td>\n",
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              "      <td>2008</td>\n",
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              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
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              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
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              "      <td>978</td>\n",
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              "      <td>2007</td>\n",
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              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
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              "    <tr>\n",
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              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
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              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>434</td>\n",
              "      <td>920</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
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              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
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              "    <tr>\n",
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              "      <td>60.0</td>\n",
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              "      <td>IR1</td>\n",
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              "      <td>Gtl</td>\n",
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              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
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              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
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              "      <td>ALQ</td>\n",
              "      <td>216</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>756</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
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              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal    208500\n",
              "1   2          20       RL  ...        WD         Normal    181500\n",
              "2   3          60       RL  ...        WD         Normal    223500\n",
              "3   4          70       RL  ...        WD        Abnorml    140000\n",
              "4   5          60       RL  ...        WD         Normal    250000\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PlAwVq-AOQm"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(style=\"ggplot\")\n",
        "plt.rcParams['figure.figsize']=(10,6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wi7uWuUmAOQn",
        "outputId": "8305ba31-3079-4993-e4aa-d813c07a4401"
      },
      "source": [
        "train['SalePrice'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      1460.000000\n",
              "mean     180921.195890\n",
              "std       79442.502883\n",
              "min       34900.000000\n",
              "25%      129975.000000\n",
              "50%      163000.000000\n",
              "75%      214000.000000\n",
              "max      755000.000000\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrcT8jneAOQn"
      },
      "source": [
        "With the describe funtion, we can see much about the sale price column. Here, the average price of houses is about 180921 USD and the min and max value are 34900 USD  and $755000 respectively with 1460 rows.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wu9RYbZnbcgY"
      },
      "source": [
        "**bold text**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vurw-RnnAOQo"
      },
      "source": [
        "# Skewness of the Dataset\n",
        "Checking for skewness in the salesprice of the dataset\n",
        "\n",
        "Our aim here is to get a low number, very close to zero will signify a linear function which will be suitable for a linear model. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "8yPq4oSDAOQo",
        "outputId": "4e2537b4-e1f1-4da2-94d1-68c7e246bca1"
      },
      "source": [
        "print(\"skew is:\",train.SalePrice.skew())\n",
        "plt.hist(train.SalePrice,color='blue')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "skew is: 1.8828757597682129\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([148., 723., 373., 135.,  51.,  19.,   4.,   3.,   2.,   2.]),\n",
              " array([ 34900., 106910., 178920., 250930., 322940., 394950., 466960.,\n",
              "        538970., 610980., 682990., 755000.]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amiR667Kb2Wb"
      },
      "source": [
        "We can a value of 1.88 skewness which means the data function is not so linear. Inorder to get a more linear function, we need to convert the salesprice into logarithm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyg_LdESAOQp",
        "outputId": "aa9e4874-1990-4f97-b03a-49acab1fa9f5"
      },
      "source": [
        "target= np.log(train.SalePrice)\n",
        "print(\"skew is:\",target.skew())\n",
        "plt.hist(target, color='blue')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "skew is: 0.12133506220520406\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x862fd70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XK0OKHyQAOQq"
      },
      "source": [
        "WORKING WITH NUMERICAL FEATURES\n",
        "\n",
        "We have a number much closer to zero after converting to log, 0.121,  which means we have improved the skewness of the model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpGWhM8LAOQq",
        "outputId": "54dd9f7b-2c14-4180-c98b-7f394352dd3b"
      },
      "source": [
        "#this function below is used to get the type of the numerical data\n",
        "numeric_features=train.select_dtypes(include=[np.number])\n",
        "numeric_features.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "MSSubClass         int64\n",
              "LotFrontage      float64\n",
              "LotArea            int64\n",
              "OverallQual        int64\n",
              "OverallCond        int64\n",
              "YearBuilt          int64\n",
              "YearRemodAdd       int64\n",
              "MasVnrArea       float64\n",
              "BsmtFinSF1         int64\n",
              "BsmtFinSF2         int64\n",
              "BsmtUnfSF          int64\n",
              "TotalBsmtSF        int64\n",
              "1stFlrSF           int64\n",
              "2ndFlrSF           int64\n",
              "LowQualFinSF       int64\n",
              "GrLivArea          int64\n",
              "BsmtFullBath       int64\n",
              "BsmtHalfBath       int64\n",
              "FullBath           int64\n",
              "HalfBath           int64\n",
              "BedroomAbvGr       int64\n",
              "KitchenAbvGr       int64\n",
              "TotRmsAbvGrd       int64\n",
              "Fireplaces         int64\n",
              "GarageYrBlt      float64\n",
              "GarageCars         int64\n",
              "GarageArea         int64\n",
              "WoodDeckSF         int64\n",
              "OpenPorchSF        int64\n",
              "EnclosedPorch      int64\n",
              "3SsnPorch          int64\n",
              "ScreenPorch        int64\n",
              "PoolArea           int64\n",
              "MiscVal            int64\n",
              "MoSold             int64\n",
              "YrSold             int64\n",
              "SalePrice          int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcqNwmIsdt51"
      },
      "source": [
        "# Correlated Columns\n",
        "We have to get for highly correlated columns and remove one so the model does not get very similar information which leads to overfitting "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eI__CXX2AOQr",
        "outputId": "27be69ec-3d86-46c5-ea48-af365b6a52c2"
      },
      "source": [
        "#shows the relationship between columns \n",
        "corr=numeric_features.corr()\n",
        "print(corr['SalePrice'].sort_values(ascending=False)[:5],'\\n')\n",
        "print(corr['SalePrice'].sort_values(ascending=False)[-5:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SalePrice      1.000000\n",
            "OverallQual    0.790982\n",
            "GrLivArea      0.708624\n",
            "GarageCars     0.640409\n",
            "GarageArea     0.623431\n",
            "Name: SalePrice, dtype: float64 \n",
            "\n",
            "YrSold          -0.028923\n",
            "OverallCond     -0.077856\n",
            "MSSubClass      -0.084284\n",
            "EnclosedPorch   -0.128578\n",
            "KitchenAbvGr    -0.135907\n",
            "Name: SalePrice, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEzZ30imAOQs",
        "outputId": "78325106-b5d7-43df-c220-cd5e7d7aa2ea"
      },
      "source": [
        "train['OverallQual'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7,  6,  8,  5,  9,  4, 10,  3,  1,  2], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwQzise7AOQs"
      },
      "source": [
        "The overall data are interger values with interval from 1 to 10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzxTTMa0AOQt"
      },
      "source": [
        "quality_pivot=train.pivot_table(index= 'OverallQual',\n",
        "                                values='SalePrice', aggfunc=np.median)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzG4JkamAOQt",
        "outputId": "4ec0093d-51cb-4b08-a14d-ddffe5a428c5"
      },
      "source": [
        "quality_pivot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>SalePrice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallQual</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>86250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>108000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>133000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>200141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>269750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>345000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>432390</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             SalePrice\n",
              "OverallQual           \n",
              "1                50150\n",
              "2                60000\n",
              "3                86250\n",
              "4               108000\n",
              "5               133000\n",
              "6               160000\n",
              "7               200141\n",
              "8               269750\n",
              "9               345000\n",
              "10              432390"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hASCEsolAOQt",
        "outputId": "892712cc-0927-420d-d2ca-5f6618fecbb3"
      },
      "source": [
        "quality_pivot.plot(kind ='bar', color='blue')\n",
        "plt.xlabel('Overall Quality')\n",
        "plt.ylabel('Median Sale Price')\n",
        "#plt.xtick(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x867cf50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8t2H-6bHAOQu",
        "outputId": "d07e4b14-1426-4752-8075-5f7a6d848952"
      },
      "source": [
        "plt.scatter(x=train['GrLivArea'],y=target)\n",
        "plt.ylabel('Sale Price')\n",
        "plt.xlabel('Above grade(ground) living area square feet')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x86d60f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VONWL9CAOQu"
      },
      "source": [
        "We can see the bigger the living area, the higher the price "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPL7gYbwe089"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwU-Ao-PAOQv",
        "outputId": "c3c4c92e-d43f-4bb3-aa2d-42a90296858e"
      },
      "source": [
        "plt.scatter(x=train['GarageArea'],y=target)\n",
        "plt.ylabel('Sale Price')\n",
        "plt.xlabel('Garage Area')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x8673bd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rqLKPCJAOQv"
      },
      "source": [
        "We can see that a few house are on the zero mark for garage indicating that they dont have. We will confirm this later on\n",
        "\n",
        "Next step is to remove outliers so as not to get inaccurate regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wsSpfHvAOQw"
      },
      "source": [
        "train=train[train['GarageArea']<1200]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUqj02L7tiLD"
      },
      "source": [
        "The graph shows a positive relationship between the sale price and garade area. the bigger the garage area, the more the price."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8GmJBQKAOQw",
        "outputId": "7831a146-1bc7-4b75-8d9b-f2260094b99c"
      },
      "source": [
        "plt.scatter(x=train['GarageArea'],y=np.log(train.SalePrice))\n",
        "plt.xlim(-200, 1600)\n",
        "plt.ylabel('Sale Price')\n",
        "plt.xlabel('Garage Area')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x87415b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_VGGrH2AOQx"
      },
      "source": [
        "Handling null Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFdjfwDbAOQx",
        "outputId": "9e0725aa-6c95-47ac-d6b6-0abf746c42d9"
      },
      "source": [
        "nulls= pd.DataFrame(train.isnull().sum().sort_values(ascending=False)[:25])\n",
        "nulls.columns=['Null Count']\n",
        "nulls.index.name='Features'\n",
        "nulls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Null Count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Features</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PoolQC</th>\n",
              "      <td>1449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscFeature</th>\n",
              "      <td>1402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alley</th>\n",
              "      <td>1364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fence</th>\n",
              "      <td>1174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FireplaceQu</th>\n",
              "      <td>689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotFrontage</th>\n",
              "      <td>258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageCond</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageType</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageFinish</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageQual</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtExposure</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtCond</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtQual</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrArea</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrType</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electrical</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Utilities</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSSubClass</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Foundation</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExterCond</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExterQual</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Null Count\n",
              "Features                \n",
              "PoolQC              1449\n",
              "MiscFeature         1402\n",
              "Alley               1364\n",
              "Fence               1174\n",
              "FireplaceQu          689\n",
              "LotFrontage          258\n",
              "GarageCond            81\n",
              "GarageType            81\n",
              "GarageYrBlt           81\n",
              "GarageFinish          81\n",
              "GarageQual            81\n",
              "BsmtExposure          38\n",
              "BsmtFinType2          38\n",
              "BsmtFinType1          37\n",
              "BsmtCond              37\n",
              "BsmtQual              37\n",
              "MasVnrArea             8\n",
              "MasVnrType             8\n",
              "Electrical             1\n",
              "Utilities              0\n",
              "YearRemodAdd           0\n",
              "MSSubClass             0\n",
              "Foundation             0\n",
              "ExterCond              0\n",
              "ExterQual              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPx7TAaVAOQx",
        "outputId": "b12a44ae-5111-49c1-adcd-881655843226"
      },
      "source": [
        "print(\"Unique values are:\", train.MiscFeature.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique values are: [nan 'Shed' 'Gar2' 'Othr' 'TenC']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XL4hiUyoAOQy",
        "outputId": "eb63de46-1420-4fbf-8905-abcfcc6efb84"
      },
      "source": [
        "categoricals=train.select_dtypes(exclude=[np.number])\n",
        "categoricals.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>MSZoning</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>...</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>91</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "      <td>...</td>\n",
              "      <td>1374</td>\n",
              "      <td>1374</td>\n",
              "      <td>1374</td>\n",
              "      <td>1374</td>\n",
              "      <td>1455</td>\n",
              "      <td>6</td>\n",
              "      <td>281</td>\n",
              "      <td>53</td>\n",
              "      <td>1455</td>\n",
              "      <td>1455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Grvl</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>Unf</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>Ex</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1147</td>\n",
              "      <td>1450</td>\n",
              "      <td>50</td>\n",
              "      <td>921</td>\n",
              "      <td>1309</td>\n",
              "      <td>1454</td>\n",
              "      <td>1048</td>\n",
              "      <td>1378</td>\n",
              "      <td>225</td>\n",
              "      <td>1257</td>\n",
              "      <td>...</td>\n",
              "      <td>867</td>\n",
              "      <td>605</td>\n",
              "      <td>1306</td>\n",
              "      <td>1321</td>\n",
              "      <td>1335</td>\n",
              "      <td>2</td>\n",
              "      <td>157</td>\n",
              "      <td>48</td>\n",
              "      <td>1266</td>\n",
              "      <td>1196</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       MSZoning Street Alley LotShape LandContour Utilities LotConfig  \\\n",
              "count      1455   1455    91     1455        1455      1455      1455   \n",
              "unique        5      2     2        4           4         2         5   \n",
              "top          RL   Pave  Grvl      Reg         Lvl    AllPub    Inside   \n",
              "freq       1147   1450    50      921        1309      1454      1048   \n",
              "\n",
              "       LandSlope Neighborhood Condition1      ...      GarageType  \\\n",
              "count       1455         1455       1455      ...            1374   \n",
              "unique         3           25          9      ...               6   \n",
              "top          Gtl        NAmes       Norm      ...          Attchd   \n",
              "freq        1378          225       1257      ...             867   \n",
              "\n",
              "       GarageFinish GarageQual GarageCond PavedDrive PoolQC  Fence  \\\n",
              "count          1374       1374       1374       1455      6    281   \n",
              "unique            3          5          5          3      3      4   \n",
              "top             Unf         TA         TA          Y     Ex  MnPrv   \n",
              "freq            605       1306       1321       1335      2    157   \n",
              "\n",
              "       MiscFeature SaleType SaleCondition  \n",
              "count           53     1455          1455  \n",
              "unique           4        9             6  \n",
              "top           Shed       WD        Normal  \n",
              "freq            48     1266          1196  \n",
              "\n",
              "[4 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRsnFuyqAOQy",
        "outputId": "d999f9b9-9bc6-4b7f-c4d1-345bba1cec86"
      },
      "source": [
        "print(\"Orignal: \\n\")\n",
        "print(train.Street.value_counts(), \"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Orignal: \n",
            "\n",
            "Pave    1450\n",
            "Grvl       5\n",
            "Name: Street, dtype: int64 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aT5ctpU8AOQz"
      },
      "source": [
        "create new column to transform the data into bolean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiQ-d-CiAOQz"
      },
      "source": [
        "train['enc_street']=pd.get_dummies(train.Street, drop_first=True)\n",
        "#test['enc_street']=pd.get_dummies(train.Street, drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EK9-80Z2AOQ0",
        "outputId": "0f8ee65b-bf62-4b2a-9801-41cbf4c7e908"
      },
      "source": [
        "print('Encoded: \\n')\n",
        "print(train.enc_street.value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Encoded: \n",
            "\n",
            "1    1450\n",
            "0       5\n",
            "Name: enc_street, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaWsGjLvAOQ0",
        "outputId": "d6bb3052-a8c2-4654-8c46-a74a94754184"
      },
      "source": [
        "condition_pivot =train.pivot_table(index = \"SaleCondition\", values='SalePrice', aggfunc=np.median)                                   \n",
        "condition_pivot.plot(kind ='bar', color='blue')\n",
        "plt.xlabel('Sale Condition')\n",
        "plt.ylabel('Median Sale Price') \n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x87d6510>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwWhTLrwAOQ1"
      },
      "source": [
        "def encode(x):\n",
        "    return 1 if x == 'Partial' else 0\n",
        "train['enc_condition']=train.SaleCondition.apply(encode)\n",
        "#test['enc_condition']=test.SaleCondition.apply(encode)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFDsVC_PAOQ1",
        "outputId": "e23a0c8e-4c77-49ef-b137-6f117498c6bc"
      },
      "source": [
        "condition_pivot =train.pivot_table(index = \"enc_condition\", values='SalePrice', aggfunc=np.median)                                   \n",
        "condition_pivot.plot(kind ='bar', color='blue')\n",
        "plt.xlabel('Encoded Sale Condition')\n",
        "plt.ylabel('Median Sale Price')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x8761090>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qB_TCzzrAOQ2",
        "outputId": "15e485a3-a879-49c2-8281-61bde1ab9fa8"
      },
      "source": [
        "data=train.select_dtypes(include=[np.number]).interpolate().dropna()\n",
        "sum(data.isnull().sum() !=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2TGFhiaf3lR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ds3eBMTAOQ3"
      },
      "source": [
        "# Modeling\n",
        "\n",
        "Building a Generalised Linear Model(GLB)\n",
        "\n",
        "The representation is a linear equation that combines a specific set of input values (x) the solution to which is the predicted output for that set of input values (y). As such, both the input values (x) and the output value are numeric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iizHl67QAOQ3"
      },
      "source": [
        "y=np.log(train.SalePrice)\n",
        "X= data.drop(['SalePrice','Id'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSAqbSykAOQ3"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "                          X, y, random_state= 42, test_size=.33)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0dtdQ5TAOQ4"
      },
      "source": [
        "from sklearn import linear_model\n",
        "lr=linear_model.LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLbmFVYWAOQ4"
      },
      "source": [
        "model= lr.fit(X_train, y_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLd9aEhAAOQ5",
        "outputId": "47f6ccc0-7cc5-4e61-abf7-2856cad67574"
      },
      "source": [
        "print(\"R^2 is:\\n\", model.score(X_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 is:\n",
            " 0.8654124583373178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbQqHZrpAOQ5"
      },
      "source": [
        "predictions= model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIKCfyBHAOQ6",
        "outputId": "8e5c4179-1787-4fa1-dbfa-26011fb540d3"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "print('RMSE is \\n', mean_squared_error (y_test, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE is \n",
            " 0.022937940902518574\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jkQWMsBAOQ6",
        "outputId": "1f9a77ac-2aa3-4d70-e528-ae8e134b1943"
      },
      "source": [
        "actual_values = y_test\n",
        "plt.scatter(predictions, actual_values, alpha=.7, color='b')\n",
        "plt.xlabel('Predicted Price')\n",
        "plt.ylabel('Actual Price')\n",
        "plt.title('Linear Regression Model')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x91f3810>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odfF17zEHCyK",
        "outputId": "1f9a77ac-2aa3-4d70-e528-ae8e134b1943"
      },
      "source": [
        "actual_values = y_test\n",
        "plt.scatter(predictions, actual_values, alpha=.7, color='b')\n",
        "plt.xlabel('Predicted Price')\n",
        "plt.ylabel('Actual Price')\n",
        "plt.title('Linear Regression Model')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x91f3810>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zeg4AjV_AOQ7",
        "outputId": "f596f591-bd41-4652-8da4-33342d222367"
      },
      "source": [
        "for i in range(-2,3):\n",
        "    alpha= 10**i\n",
        "    rm=linear_model.Ridge(alpha=alpha)\n",
        "    ridge_model=rm.fit(X_train, y_train)\n",
        "    preds_ridge = ridge_model.predict(X_test)\n",
        "    \n",
        "    plt.scatter(preds_ridge, actual_values, alpha=.75, color='b')\n",
        "    plt.xlabel('Predicted Price')\n",
        "    plt.ylabel('Actual Price')\n",
        "    plt.title('Ridge Regularization with alpha={}'. format(alpha))\n",
        "    overlay= 'R^2 is :{}\\nRMSE is:{}'.format(\n",
        "                  ridge_model.score(X_test,y_test),\n",
        "                  mean_squared_error(y_test, preds_ridge))\n",
        "    plt.annotate(s=overlay, xy=(12.1,10.6), size='x-large')\n",
        "    plt.show()\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0xa1ebd90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0xa22dfd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0xa2f49d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0xa223270>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0xa1eb110>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-r-RvNPAOQ8"
      },
      "source": [
        "Adding more features to our data since the different alpha numbers had little effect"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVb-sfeRAOQ8"
      },
      "source": [
        "submission=pd.DataFrame()\n",
        "submission['id']= test.Id"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxcGFHiAAOQ8"
      },
      "source": [
        "feats = test.select_dtypes(\n",
        "        include=[np.number]).drop(['Id'],axis=1).interpolate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klFgvpt_AOQ9"
      },
      "source": [
        "predictions = model.predict(feats)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ry3a6Rr8AOQ9"
      },
      "source": [
        "final_predictions = np.exp(predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGnt66ypAOQ9",
        "outputId": "ec543fd3-9cce-4112-dfb1-87fdfa359e8c"
      },
      "source": [
        "print(\"Original predictions are:\\n\", predictions[:5],\"\\n\")\n",
        "print(\"Final predictions are:\\n\", final_predictions[:5])      "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original predictions are:\n",
            " [11.76810976 11.72758634 12.07666908 12.2062551  12.11442371] \n",
            "\n",
            "Final predictions are:\n",
            " [129069.94683234 123944.14989588 175723.86262344 200036.49399745\n",
            " 182485.08209715]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8f0lMkJ--GBG"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "The several factors are very significant in determining the price of a house. In determining these factors we used the Ames, Iowa(USA) Dataset. After cleaning the dataset, Exploration and Features seletion. I was able to show several attributes that had significant effect of the price of homes. Attributes like neigbourhood, size of hose, number of rooms, present of garade and parking space showed an incline in the price of homes. \n",
        "\n",
        "**Building a linearn model with Linear Regression.**\n",
        "A 67% to 33% split was used to train and test the model respectively. Atfer testing the model with previously unseen data. A suitable performance metric was choosen to see how well the model performed. Root mean square error(RMSE) was used for evaluation and returned a low value, showing minimal error. Thus, suggesting the model performance well. I was able to predict the price of houses with good accuracy "
      ]
    }
  ]
}