Assignment_Multi_Linear_Regression_Startup_.ipynb

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      "name": "Assignment Multi Linear Regression - Startup .ipynb",
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      "authorship_tag": "ABX9TyN1tH5SiL4DoZUa2VOlrJZW",
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Manu-Gr/Multi-Linear-Regression---Assignment---50-Startups/blob/main/Assignment_Multi_Linear_Regression_Startup_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gsko6i5395eH"
      },
      "source": [
        "# Prepare a prediction model for profit of 50_startups data.\n",
        "Do transformations for getting better predictions of profit and\n",
        "make a table containing R^2 value for each prepared model.\n",
        "\n",
        "R&D Spend -- Research and devolop spend in the past few years\n",
        "Administration -- spend on administration in the past few years\n",
        "Marketing Spend -- spend on Marketing in the past few years\n",
        "State -- states from which data is collected\n",
        "Profit  -- profit of each state in the past few years\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqKsMHcE-JFh",
        "outputId": "0805a38f-1c92-4012-8286-4b86ba70343a"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from statsmodels.graphics.regressionplots import influence_plot\n",
        "import statsmodels.formula.api as smf\n",
        "import numpy as np\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "O2bcTWZE--uR",
        "outputId": "c3576512-23c0-4921-ab4f-a6af64083d2d"
      },
      "source": [
        "#Read the data\n",
        "St = pd.read_csv(\"50_Startups.csv\")\n",
        "St.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83\n",
              "1  162597.70       151377.59        443898.53  California  191792.06\n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3  144372.41       118671.85        383199.62    New York  182901.99\n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRcC5Ngr_I9Q"
      },
      "source": [
        "##EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EF7lJ7A__dWa",
        "outputId": "70ffa96c-df4a-4787-d2af-d7e76122e5ab"
      },
      "source": [
        "St.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 5 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   R&D Spend        50 non-null     float64\n",
            " 1   Administration   50 non-null     float64\n",
            " 2   Marketing Spend  50 non-null     float64\n",
            " 3   State            50 non-null     object \n",
            " 4   Profit           50 non-null     float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 2.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "FvcxItjw_jAn",
        "outputId": "3d756c54-d2a7-402a-ea2f-165761fb9625"
      },
      "source": [
        "St.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>73721.615600</td>\n",
              "      <td>121344.639600</td>\n",
              "      <td>211025.097800</td>\n",
              "      <td>112012.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>45902.256482</td>\n",
              "      <td>28017.802755</td>\n",
              "      <td>122290.310726</td>\n",
              "      <td>40306.180338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>51283.140000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14681.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39936.370000</td>\n",
              "      <td>103730.875000</td>\n",
              "      <td>129300.132500</td>\n",
              "      <td>90138.902500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>73051.080000</td>\n",
              "      <td>122699.795000</td>\n",
              "      <td>212716.240000</td>\n",
              "      <td>107978.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>101602.800000</td>\n",
              "      <td>144842.180000</td>\n",
              "      <td>299469.085000</td>\n",
              "      <td>139765.977500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>165349.200000</td>\n",
              "      <td>182645.560000</td>\n",
              "      <td>471784.100000</td>\n",
              "      <td>192261.830000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           R&D Spend  Administration  Marketing Spend         Profit\n",
              "count      50.000000       50.000000        50.000000      50.000000\n",
              "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
              "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
              "min         0.000000    51283.140000         0.000000   14681.400000\n",
              "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
              "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
              "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
              "max    165349.200000   182645.560000    471784.100000  192261.830000"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFP1AaGR_rEk",
        "outputId": "259db38c-89af-47be-c33a-faabab3fc3b7"
      },
      "source": [
        "#check for missing values\n",
        "St.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R&D Spend          0\n",
              "Administration     0\n",
              "Marketing Spend    0\n",
              "State              0\n",
              "Profit             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "id": "DeuJ0W0f_27E",
        "outputId": "0a76ec3b-d55c-4b00-8cd2-012b7f443819"
      },
      "source": [
        "#check for duplicated records\n",
        "St[St.duplicated()]"
      ],
      "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>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [R&D Spend, Administration, Marketing Spend, State, Profit]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ws1LePsFth2"
      },
      "source": [
        "# Rename the columns \n",
        "\n",
        "St1 = St.rename({'R&D Spend': 'RDS','Administration': 'ADM','Marketing Spend': 'MKS'}, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZDluB2QAGH2"
      },
      "source": [
        "## Correlation Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "zCt9QHeYAf48",
        "outputId": "c89c412c-920b-45da-f17b-a8029c913dcc"
      },
      "source": [
        "St1.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RDS</th>\n",
              "      <th>ADM</th>\n",
              "      <th>MKS</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RDS</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.241955</td>\n",
              "      <td>0.724248</td>\n",
              "      <td>0.972900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ADM</th>\n",
              "      <td>0.241955</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.032154</td>\n",
              "      <td>0.200717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MKS</th>\n",
              "      <td>0.724248</td>\n",
              "      <td>-0.032154</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.747766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profit</th>\n",
              "      <td>0.972900</td>\n",
              "      <td>0.200717</td>\n",
              "      <td>0.747766</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             RDS       ADM       MKS    Profit\n",
              "RDS     1.000000  0.241955  0.724248  0.972900\n",
              "ADM     0.241955  1.000000 -0.032154  0.200717\n",
              "MKS     0.724248 -0.032154  1.000000  0.747766\n",
              "Profit  0.972900  0.200717  0.747766  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1OzYXnLAnFH"
      },
      "source": [
        "###Scatterplot between variables along with histograms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "b8NCLIIJBoI1",
        "outputId": "d6daf230-020e-4b57-8778-2893dacb55e2"
      },
      "source": [
        "sns.set_style(style='darkgrid')\n",
        "sns.pairplot(St1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f784dde6e50>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctS6RI0SCYRO"
      },
      "source": [
        "##Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5mskfgTDLTZ"
      },
      "source": [
        "model = smf.ols('Profit~RDS+ADM+MKS',data=St1).fit()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvNZAcLUHmEN"
      },
      "source": [
        "##Model Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bgcvt_l9Fc-l",
        "outputId": "0a118ca3-61a9-4ebf-eb32-0ee46b48e95d"
      },
      "source": [
        "#Co-efficient of model\n",
        "model.params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept    50122.192990\n",
              "RDS              0.805715\n",
              "ADM             -0.026816\n",
              "MKS              0.027228\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "g2ucie0nG7Vy",
        "outputId": "9d6fb5e8-72da-4316-fe78-4882c61f328d"
      },
      "source": [
        "# Summary of model\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.951</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.948</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   296.0</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 26 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>4.53e-30</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>07:18:16</td>     <th>  Log-Likelihood:    </th> <td> -525.39</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1059.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    46</td>      <th>  BIC:               </th> <td>   1066.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td> 5.012e+04</td> <td> 6572.353</td> <td>    7.626</td> <td> 0.000</td> <td> 3.69e+04</td> <td> 6.34e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>RDS</th>       <td>    0.8057</td> <td>    0.045</td> <td>   17.846</td> <td> 0.000</td> <td>    0.715</td> <td>    0.897</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ADM</th>       <td>   -0.0268</td> <td>    0.051</td> <td>   -0.526</td> <td> 0.602</td> <td>   -0.130</td> <td>    0.076</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MKS</th>       <td>    0.0272</td> <td>    0.016</td> <td>    1.655</td> <td> 0.105</td> <td>   -0.006</td> <td>    0.060</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>14.838</td> <th>  Durbin-Watson:     </th> <td>   1.282</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  21.442</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.949</td> <th>  Prob(JB):          </th> <td>2.21e-05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 5.586</td> <th>  Cond. No.          </th> <td>1.40e+06</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.4e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                 Profit   R-squared:                       0.951\n",
              "Model:                            OLS   Adj. R-squared:                  0.948\n",
              "Method:                 Least Squares   F-statistic:                     296.0\n",
              "Date:                Fri, 26 Nov 2021   Prob (F-statistic):           4.53e-30\n",
              "Time:                        07:18:16   Log-Likelihood:                -525.39\n",
              "No. Observations:                  50   AIC:                             1059.\n",
              "Df Residuals:                      46   BIC:                             1066.\n",
              "Df Model:                           3                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept   5.012e+04   6572.353      7.626      0.000    3.69e+04    6.34e+04\n",
              "RDS            0.8057      0.045     17.846      0.000       0.715       0.897\n",
              "ADM           -0.0268      0.051     -0.526      0.602      -0.130       0.076\n",
              "MKS            0.0272      0.016      1.655      0.105      -0.006       0.060\n",
              "==============================================================================\n",
              "Omnibus:                       14.838   Durbin-Watson:                   1.282\n",
              "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               21.442\n",
              "Skew:                          -0.949   Prob(JB):                     2.21e-05\n",
              "Kurtosis:                       5.586   Cond. No.                     1.40e+06\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 1.4e+06. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WfiqlccHG_HF"
      },
      "source": [
        "The Accuracy of the model is 95.1 %"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQ5f8BafHLJt"
      },
      "source": [
        "# Since the p value for Administration and Marketing Spend is > 0.05 build simple linear regression and multi linear regression models  to see insignificant variables"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xk2Mzn7IFrT"
      },
      "source": [
        "slr_adm = smf.ols('Profit~ADM',data=St1).fit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "wv2YAf8GIcz3",
        "outputId": "ed3c9ad1-94c7-42c9-9770-5eb328e1bc6a"
      },
      "source": [
        "slr_adm.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.040</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.020</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.015</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 26 Nov 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.162</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>07:24:55</td>     <th>  Log-Likelihood:    </th> <td> -599.63</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1203.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1207.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td> 7.697e+04</td> <td> 2.53e+04</td> <td>    3.040</td> <td> 0.004</td> <td> 2.61e+04</td> <td> 1.28e+05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ADM</th>       <td>    0.2887</td> <td>    0.203</td> <td>    1.419</td> <td> 0.162</td> <td>   -0.120</td> <td>    0.698</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 0.126</td> <th>  Durbin-Watson:     </th> <td>   0.099</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.939</td> <th>  Jarque-Bera (JB):  </th> <td>   0.110</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.093</td> <th>  Prob(JB):          </th> <td>   0.947</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.866</td> <th>  Cond. No.          </th> <td>5.59e+05</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.59e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                 Profit   R-squared:                       0.040\n",
              "Model:                            OLS   Adj. R-squared:                  0.020\n",
              "Method:                 Least Squares   F-statistic:                     2.015\n",
              "Date:                Fri, 26 Nov 2021   Prob (F-statistic):              0.162\n",
              "Time:                        07:24:55   Log-Likelihood:                -599.63\n",
              "No. Observations:                  50   AIC:                             1203.\n",
              "Df Residuals:                      48   BIC:                             1207.\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept   7.697e+04   2.53e+04      3.040      0.004    2.61e+04    1.28e+05\n",
              "ADM            0.2887      0.203      1.419      0.162      -0.120       0.698\n",
              "==============================================================================\n",
              "Omnibus:                        0.126   Durbin-Watson:                   0.099\n",
              "Prob(Omnibus):                  0.939   Jarque-Bera (JB):                0.110\n",
              "Skew:                           0.093   Prob(JB):                        0.947\n",
              "Kurtosis:                       2.866   Cond. No.                     5.59e+05\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 5.59e+05. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DBQkQ7iiIgcQ"
      },
      "source": [
        " After we build a simple linear regression model , we can see that administration has no signifcant p value "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVn1miwxb3vl"
      },
      "source": [
        "# Build a simple linear model to check the column Marketing\n",
        "\n",
        "slr_mar = smf.ols('Profit~MKS',data=St1).fit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "L4RBtordcDro",
        "outputId": "f075c077-ec8b-4119-e89d-896d44edd79f"
      },
      "source": [
        "slr_mar.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.559</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.550</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   60.88</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 26 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>4.38e-10</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>08:51:15</td>     <th>  Log-Likelihood:    </th> <td> -580.18</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1164.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   1168.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>     6e+04</td> <td> 7684.530</td> <td>    7.808</td> <td> 0.000</td> <td> 4.46e+04</td> <td> 7.55e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MKS</th>       <td>    0.2465</td> <td>    0.032</td> <td>    7.803</td> <td> 0.000</td> <td>    0.183</td> <td>    0.310</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 4.420</td> <th>  Durbin-Watson:     </th> <td>   1.178</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.110</td> <th>  Jarque-Bera (JB):  </th> <td>   3.882</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.336</td> <th>  Prob(JB):          </th> <td>   0.144</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 4.188</td> <th>  Cond. No.          </th> <td>4.89e+05</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.89e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                 Profit   R-squared:                       0.559\n",
              "Model:                            OLS   Adj. R-squared:                  0.550\n",
              "Method:                 Least Squares   F-statistic:                     60.88\n",
              "Date:                Fri, 26 Nov 2021   Prob (F-statistic):           4.38e-10\n",
              "Time:                        08:51:15   Log-Likelihood:                -580.18\n",
              "No. Observations:                  50   AIC:                             1164.\n",
              "Df Residuals:                      48   BIC:                             1168.\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept       6e+04   7684.530      7.808      0.000    4.46e+04    7.55e+04\n",
              "MKS            0.2465      0.032      7.803      0.000       0.183       0.310\n",
              "==============================================================================\n",
              "Omnibus:                        4.420   Durbin-Watson:                   1.178\n",
              "Prob(Omnibus):                  0.110   Jarque-Bera (JB):                3.882\n",
              "Skew:                          -0.336   Prob(JB):                        0.144\n",
              "Kurtosis:                       4.188   Cond. No.                     4.89e+05\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 4.89e+05. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AC5-TKtxcQ_i"
      },
      "source": [
        "SInce the p value is less than 0.05 we can say that it is a significant column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbgZwL37cdHi"
      },
      "source": [
        "# Build the Multi Linear regression model of Adminstration and Marketing together to check for collinearity problem\n",
        "\n",
        "slr_AM = smf.ols('Profit~ADM+MKS',data=St1).fit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "eIpLGvHnc35t",
        "outputId": "322b1630-c3ec-419e-9d63-6a29b5db15ce"
      },
      "source": [
        "slr_AM.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.610</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.593</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   36.71</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 26 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>2.50e-10</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>08:54:06</td>     <th>  Log-Likelihood:    </th> <td> -577.13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1160.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    47</td>      <th>  BIC:               </th> <td>   1166.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td> 2.022e+04</td> <td> 1.77e+04</td> <td>    1.143</td> <td> 0.259</td> <td>-1.54e+04</td> <td> 5.58e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ADM</th>       <td>    0.3237</td> <td>    0.131</td> <td>    2.468</td> <td> 0.017</td> <td>    0.060</td> <td>    0.588</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MKS</th>       <td>    0.2488</td> <td>    0.030</td> <td>    8.281</td> <td> 0.000</td> <td>    0.188</td> <td>    0.309</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 6.584</td> <th>  Durbin-Watson:     </th> <td>   1.279</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.037</td> <th>  Jarque-Bera (JB):  </th> <td>   6.524</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.512</td> <th>  Prob(JB):          </th> <td>  0.0383</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 4.443</td> <th>  Cond. No.          </th> <td>1.30e+06</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.3e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                 Profit   R-squared:                       0.610\n",
              "Model:                            OLS   Adj. R-squared:                  0.593\n",
              "Method:                 Least Squares   F-statistic:                     36.71\n",
              "Date:                Fri, 26 Nov 2021   Prob (F-statistic):           2.50e-10\n",
              "Time:                        08:54:06   Log-Likelihood:                -577.13\n",
              "No. Observations:                  50   AIC:                             1160.\n",
              "Df Residuals:                      47   BIC:                             1166.\n",
              "Df Model:                           2                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept   2.022e+04   1.77e+04      1.143      0.259   -1.54e+04    5.58e+04\n",
              "ADM            0.3237      0.131      2.468      0.017       0.060       0.588\n",
              "MKS            0.2488      0.030      8.281      0.000       0.188       0.309\n",
              "==============================================================================\n",
              "Omnibus:                        6.584   Durbin-Watson:                   1.279\n",
              "Prob(Omnibus):                  0.037   Jarque-Bera (JB):                6.524\n",
              "Skew:                          -0.512   Prob(JB):                       0.0383\n",
              "Kurtosis:                       4.443   Cond. No.                     1.30e+06\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 1.3e+06. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkglGS01c67U"
      },
      "source": [
        "When we build the model together both of the columns have significant p value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wm-aVfNh6UyB"
      },
      "source": [
        "# Model Validation Techniques\n",
        "\n",
        "## Two Techniques \n",
        "### 1) Collinearity Check - VIF\n",
        "### 2) Residual Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dYEF8a9kd0CT"
      },
      "source": [
        "Calculating Variance Inflation Factor (VIF) = 1/1-r^2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQ-arWqnd7Vv",
        "outputId": "e9a416c7-ca59-42c6-b8be-4773ee5c68c6"
      },
      "source": [
        "rsq_rds = smf.ols('RDS~ADM+MKS',data=St1).fit().rsquared  \n",
        "vif_rds = 1/(1-rsq_rds) \n",
        "vif_rds\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4689030699947017"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UM98Rh3DegDx",
        "outputId": "473c4a9f-301b-47f3-c1bc-2f79c99ab3fb"
      },
      "source": [
        "rsq_adm = smf.ols('ADM~RDS+MKS',data=St1).fit().rsquared  \n",
        "vif_adm = 1/(1-rsq_adm) \n",
        "vif_adm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.1750910070550453"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ji_aI_YveswK",
        "outputId": "47ba0717-f912-48e9-ff62-ed28625f5348"
      },
      "source": [
        "rsq_mks = smf.ols('MKS~RDS+ADM',data=St1).fit().rsquared  \n",
        "vif_mks = 1/(1-rsq_mks) \n",
        "vif_mks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.3267732905308773"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "-04f09sWe3DK",
        "outputId": "4ed3247e-aad1-45cc-e094-be9dc9607f0b"
      },
      "source": [
        "# Storing vif values in a data frame\n",
        "d1 = {'Variables':['RDS','ADM','MKS'],'VIF':[vif_rds,vif_adm,vif_mks]}\n",
        "Vif_frame = pd.DataFrame(d1)  \n",
        "Vif_frame"
      ],
      "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>Variables</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RDS</td>\n",
              "      <td>2.468903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ADM</td>\n",
              "      <td>1.175091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MKS</td>\n",
              "      <td>2.326773</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Variables       VIF\n",
              "0       RDS  2.468903\n",
              "1       ADM  1.175091\n",
              "2       MKS  2.326773"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIm6IlrlfIu5"
      },
      "source": [
        "Since No value of VIF is greater than 20 , we can say that there is no collinearity problem. So we shall consider all columns for building the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfSiQLfSfSQ7"
      },
      "source": [
        "##Residual Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9t4qMC2fqol"
      },
      "source": [
        "##Test for Normality of Residuals (Q-Q Plot)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "IOLsCRtmfj6n",
        "outputId": "8b9facdf-522b-4451-c3d5-48a703e46381"
      },
      "source": [
        "import statsmodels.api as sm\n",
        "sns.set_style(style='darkgrid')\n",
        "qqplot=sm.qqplot(model.resid,line='q') # line = 45 to draw the diagnoal line\n",
        "plt.title(\"Normal Q-Q plot of residuals\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_MnZ3CafolT",
        "outputId": "6e753a2a-e343-4731-a1fc-5f6f16c501c7"
      },
      "source": [
        "list(np.where(model.resid<-10000))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([14, 15, 48, 49])]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUvJijTjgJzZ"
      },
      "source": [
        "Residual Plot for **Homoscedasticity** or **Heteroscedasticity**  (plotting \n",
        "model's standardized fitted values vs standardized residual values)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CR1nQVCUgPvl"
      },
      "source": [
        "def get_standardized_values( vals ):\n",
        "    return (vals - vals.mean())/vals.std()  #User Defined Function Z = (x-mu)/sigma"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "oTYKztoMgSF_",
        "outputId": "18473744-6fca-45ed-9741-1844f8b6ac67"
      },
      "source": [
        "plt.scatter(get_standardized_values(model.fittedvalues),\n",
        "            get_standardized_values(model.resid))\n",
        "\n",
        "plt.title('Residual Plot')\n",
        "plt.xlabel('Standardized Fitted values')\n",
        "plt.ylabel('Standardized residual values')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7u4kgvZgcs4"
      },
      "source": [
        "Residual Vs Regressors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "1m_eIbokgiEj",
        "outputId": "dbfd00fe-3f2f-4ea2-844f-844809fd9d95"
      },
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(model, \"RDS\", fig=fig)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "CEPyF9A-gocg",
        "outputId": "186428ff-c1a3-4380-860f-94bcdd5c0ddf"
      },
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(model, \"ADM\", fig=fig)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "tE5bD3QFgvCe",
        "outputId": "71495236-c67c-44aa-a0d7-cf2892e3216a"
      },
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(model, \"MKS\", fig=fig)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5ywrKz0hIyg"
      },
      "source": [
        "#Model Deletion Diagnostics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qjd3QuTcgyXj"
      },
      "source": [
        "##Detecting Influuencers / Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8jmpUDxhVlP"
      },
      "source": [
        "###Cooks Distance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLagiPWlhZKb"
      },
      "source": [
        "  model_influence = model.get_influence()\n",
        "  (c, _) = model_influence.cooks_distance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "t8h9CGKIhcI0",
        "outputId": "1c422315-875c-4afb-9a72-1aa2aab8349a"
      },
      "source": [
        "#Plot the influencers values using stem plot\n",
        "sns.set_style(style='darkgrid')\n",
        "fig = plt.subplots(figsize=(20, 7))\n",
        "plt.stem(np.arange(len(St1)), np.round(c, 3))\n",
        "plt.xlabel('Row index')\n",
        "plt.ylabel('Cooks Distance')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlUPTpjnibk9"
      },
      "source": [
        "There are no datapoints whose value is greater than 0.5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVR13MK6hslc"
      },
      "source": [
        "##High Influence Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "gaigjqj0hewA",
        "outputId": "db9a8ce5-0316-47ee-9302-297fea1b475e"
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(15,10))\n",
        "fig=influence_plot(model,ax = ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyL93Gibhvze",
        "outputId": "053b0e62-0a6b-4b28-e47c-6ed686169061"
      },
      "source": [
        "k = St1.shape[1]     # k = no.of features/columns & n = no. of datapoints\n",
        "n = St1.shape[0] \n",
        "leverage_cutoff = 3*((k + 1)/n)\n",
        "leverage_cutoff"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.36"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "siP347e_kim-",
        "outputId": "61f3881a-b0b0-4fd0-f5cf-2399f88dbbfb"
      },
      "source": [
        "St1[St1.index.isin([49])]"
      ],
      "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>RDS</th>\n",
              "      <th>ADM</th>\n",
              "      <th>MKS</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.0</td>\n",
              "      <td>116983.8</td>\n",
              "      <td>45173.06</td>\n",
              "      <td>California</td>\n",
              "      <td>14681.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    RDS       ADM       MKS       State   Profit\n",
              "49  0.0  116983.8  45173.06  California  14681.4"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhgo2bXIkob9",
        "outputId": "ce5be2c7-e25e-4d2a-e4b5-c946c882e4a8"
      },
      "source": [
        "St1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHwgGlyUkxA1"
      },
      "source": [
        "##Improving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Qg-qswFgk149",
        "outputId": "e7ec48d8-b4ea-4702-e951-a31f81d77e6f"
      },
      "source": [
        "#Discard the data points which are influencers and reasign the row number (reset_index())\n",
        "St2=St1.copy()\n",
        "St2"
      ],
      "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>RDS</th>\n",
              "      <th>ADM</th>\n",
              "      <th>MKS</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>131876.90</td>\n",
              "      <td>99814.71</td>\n",
              "      <td>362861.36</td>\n",
              "      <td>New York</td>\n",
              "      <td>156991.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>134615.46</td>\n",
              "      <td>147198.87</td>\n",
              "      <td>127716.82</td>\n",
              "      <td>California</td>\n",
              "      <td>156122.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>130298.13</td>\n",
              "      <td>145530.06</td>\n",
              "      <td>323876.68</td>\n",
              "      <td>Florida</td>\n",
              "      <td>155752.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>120542.52</td>\n",
              "      <td>148718.95</td>\n",
              "      <td>311613.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>152211.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>123334.88</td>\n",
              "      <td>108679.17</td>\n",
              "      <td>304981.62</td>\n",
              "      <td>California</td>\n",
              "      <td>149759.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101913.08</td>\n",
              "      <td>110594.11</td>\n",
              "      <td>229160.95</td>\n",
              "      <td>Florida</td>\n",
              "      <td>146121.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100671.96</td>\n",
              "      <td>91790.61</td>\n",
              "      <td>249744.55</td>\n",
              "      <td>California</td>\n",
              "      <td>144259.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>Florida</td>\n",
              "      <td>141585.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>91992.39</td>\n",
              "      <td>135495.07</td>\n",
              "      <td>252664.93</td>\n",
              "      <td>California</td>\n",
              "      <td>134307.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>119943.24</td>\n",
              "      <td>156547.42</td>\n",
              "      <td>256512.92</td>\n",
              "      <td>Florida</td>\n",
              "      <td>132602.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>114523.61</td>\n",
              "      <td>122616.84</td>\n",
              "      <td>261776.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>129917.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>78013.11</td>\n",
              "      <td>121597.55</td>\n",
              "      <td>264346.06</td>\n",
              "      <td>California</td>\n",
              "      <td>126992.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>94657.16</td>\n",
              "      <td>145077.58</td>\n",
              "      <td>282574.31</td>\n",
              "      <td>New York</td>\n",
              "      <td>125370.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>91749.16</td>\n",
              "      <td>114175.79</td>\n",
              "      <td>294919.57</td>\n",
              "      <td>Florida</td>\n",
              "      <td>124266.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>86419.70</td>\n",
              "      <td>153514.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>122776.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>76253.86</td>\n",
              "      <td>113867.30</td>\n",
              "      <td>298664.47</td>\n",
              "      <td>California</td>\n",
              "      <td>118474.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>78389.47</td>\n",
              "      <td>153773.43</td>\n",
              "      <td>299737.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>111313.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>73994.56</td>\n",
              "      <td>122782.75</td>\n",
              "      <td>303319.26</td>\n",
              "      <td>Florida</td>\n",
              "      <td>110352.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>67532.53</td>\n",
              "      <td>105751.03</td>\n",
              "      <td>304768.73</td>\n",
              "      <td>Florida</td>\n",
              "      <td>108733.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77044.01</td>\n",
              "      <td>99281.34</td>\n",
              "      <td>140574.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>108552.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64664.71</td>\n",
              "      <td>139553.16</td>\n",
              "      <td>137962.62</td>\n",
              "      <td>California</td>\n",
              "      <td>107404.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>75328.87</td>\n",
              "      <td>144135.98</td>\n",
              "      <td>134050.07</td>\n",
              "      <td>Florida</td>\n",
              "      <td>105733.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>72107.60</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>353183.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>105008.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>66051.52</td>\n",
              "      <td>182645.56</td>\n",
              "      <td>118148.20</td>\n",
              "      <td>Florida</td>\n",
              "      <td>103282.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65605.48</td>\n",
              "      <td>153032.06</td>\n",
              "      <td>107138.38</td>\n",
              "      <td>New York</td>\n",
              "      <td>101004.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>61994.48</td>\n",
              "      <td>115641.28</td>\n",
              "      <td>91131.24</td>\n",
              "      <td>Florida</td>\n",
              "      <td>99937.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>61136.38</td>\n",
              "      <td>152701.92</td>\n",
              "      <td>88218.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>97483.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>63408.86</td>\n",
              "      <td>129219.61</td>\n",
              "      <td>46085.25</td>\n",
              "      <td>California</td>\n",
              "      <td>97427.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>55493.95</td>\n",
              "      <td>103057.49</td>\n",
              "      <td>214634.81</td>\n",
              "      <td>Florida</td>\n",
              "      <td>96778.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>46426.07</td>\n",
              "      <td>157693.92</td>\n",
              "      <td>210797.67</td>\n",
              "      <td>California</td>\n",
              "      <td>96712.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>46014.02</td>\n",
              "      <td>85047.44</td>\n",
              "      <td>205517.64</td>\n",
              "      <td>New York</td>\n",
              "      <td>96479.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>28663.76</td>\n",
              "      <td>127056.21</td>\n",
              "      <td>201126.82</td>\n",
              "      <td>Florida</td>\n",
              "      <td>90708.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>California</td>\n",
              "      <td>89949.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20229.59</td>\n",
              "      <td>65947.93</td>\n",
              "      <td>185265.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>81229.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>38558.51</td>\n",
              "      <td>82982.09</td>\n",
              "      <td>174999.30</td>\n",
              "      <td>California</td>\n",
              "      <td>81005.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>28754.33</td>\n",
              "      <td>118546.05</td>\n",
              "      <td>172795.67</td>\n",
              "      <td>California</td>\n",
              "      <td>78239.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>27892.92</td>\n",
              "      <td>84710.77</td>\n",
              "      <td>164470.71</td>\n",
              "      <td>Florida</td>\n",
              "      <td>77798.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>23640.93</td>\n",
              "      <td>96189.63</td>\n",
              "      <td>148001.11</td>\n",
              "      <td>California</td>\n",
              "      <td>71498.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>15505.73</td>\n",
              "      <td>127382.30</td>\n",
              "      <td>35534.17</td>\n",
              "      <td>New York</td>\n",
              "      <td>69758.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>22177.74</td>\n",
              "      <td>154806.14</td>\n",
              "      <td>28334.72</td>\n",
              "      <td>California</td>\n",
              "      <td>65200.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1000.23</td>\n",
              "      <td>124153.04</td>\n",
              "      <td>1903.93</td>\n",
              "      <td>New York</td>\n",
              "      <td>64926.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1315.46</td>\n",
              "      <td>115816.21</td>\n",
              "      <td>297114.46</td>\n",
              "      <td>Florida</td>\n",
              "      <td>49490.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.00</td>\n",
              "      <td>135426.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>California</td>\n",
              "      <td>42559.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
              "      <td>51743.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>35673.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.00</td>\n",
              "      <td>116983.80</td>\n",
              "      <td>45173.06</td>\n",
              "      <td>California</td>\n",
              "      <td>14681.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          RDS        ADM        MKS       State     Profit\n",
              "0   165349.20  136897.80  471784.10    New York  192261.83\n",
              "1   162597.70  151377.59  443898.53  California  191792.06\n",
              "2   153441.51  101145.55  407934.54     Florida  191050.39\n",
              "3   144372.41  118671.85  383199.62    New York  182901.99\n",
              "4   142107.34   91391.77  366168.42     Florida  166187.94\n",
              "5   131876.90   99814.71  362861.36    New York  156991.12\n",
              "6   134615.46  147198.87  127716.82  California  156122.51\n",
              "7   130298.13  145530.06  323876.68     Florida  155752.60\n",
              "8   120542.52  148718.95  311613.29    New York  152211.77\n",
              "9   123334.88  108679.17  304981.62  California  149759.96\n",
              "10  101913.08  110594.11  229160.95     Florida  146121.95\n",
              "11  100671.96   91790.61  249744.55  California  144259.40\n",
              "12   93863.75  127320.38  249839.44     Florida  141585.52\n",
              "13   91992.39  135495.07  252664.93  California  134307.35\n",
              "14  119943.24  156547.42  256512.92     Florida  132602.65\n",
              "15  114523.61  122616.84  261776.23    New York  129917.04\n",
              "16   78013.11  121597.55  264346.06  California  126992.93\n",
              "17   94657.16  145077.58  282574.31    New York  125370.37\n",
              "18   91749.16  114175.79  294919.57     Florida  124266.90\n",
              "19   86419.70  153514.11       0.00    New York  122776.86\n",
              "20   76253.86  113867.30  298664.47  California  118474.03\n",
              "21   78389.47  153773.43  299737.29    New York  111313.02\n",
              "22   73994.56  122782.75  303319.26     Florida  110352.25\n",
              "23   67532.53  105751.03  304768.73     Florida  108733.99\n",
              "24   77044.01   99281.34  140574.81    New York  108552.04\n",
              "25   64664.71  139553.16  137962.62  California  107404.34\n",
              "26   75328.87  144135.98  134050.07     Florida  105733.54\n",
              "27   72107.60  127864.55  353183.81    New York  105008.31\n",
              "28   66051.52  182645.56  118148.20     Florida  103282.38\n",
              "29   65605.48  153032.06  107138.38    New York  101004.64\n",
              "30   61994.48  115641.28   91131.24     Florida   99937.59\n",
              "31   61136.38  152701.92   88218.23    New York   97483.56\n",
              "32   63408.86  129219.61   46085.25  California   97427.84\n",
              "33   55493.95  103057.49  214634.81     Florida   96778.92\n",
              "34   46426.07  157693.92  210797.67  California   96712.80\n",
              "35   46014.02   85047.44  205517.64    New York   96479.51\n",
              "36   28663.76  127056.21  201126.82     Florida   90708.19\n",
              "37   44069.95   51283.14  197029.42  California   89949.14\n",
              "38   20229.59   65947.93  185265.10    New York   81229.06\n",
              "39   38558.51   82982.09  174999.30  California   81005.76\n",
              "40   28754.33  118546.05  172795.67  California   78239.91\n",
              "41   27892.92   84710.77  164470.71     Florida   77798.83\n",
              "42   23640.93   96189.63  148001.11  California   71498.49\n",
              "43   15505.73  127382.30   35534.17    New York   69758.98\n",
              "44   22177.74  154806.14   28334.72  California   65200.33\n",
              "45    1000.23  124153.04    1903.93    New York   64926.08\n",
              "46    1315.46  115816.21  297114.46     Florida   49490.75\n",
              "47       0.00  135426.92       0.00  California   42559.73\n",
              "48     542.05   51743.15       0.00    New York   35673.41\n",
              "49       0.00  116983.80   45173.06  California   14681.40"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZLga4HJHlD6_",
        "outputId": "0e58e64a-902d-40a4-e63b-738755661708"
      },
      "source": [
        "# Discard the data points which are influencers and reassign the row number (reset_index(drop=True))\n",
        "St2=St1.drop(St1.index[[49]],axis=0).reset_index(drop=True)\n",
        "St2"
      ],
      "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>RDS</th>\n",
              "      <th>ADM</th>\n",
              "      <th>MKS</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>131876.90</td>\n",
              "      <td>99814.71</td>\n",
              "      <td>362861.36</td>\n",
              "      <td>New York</td>\n",
              "      <td>156991.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>134615.46</td>\n",
              "      <td>147198.87</td>\n",
              "      <td>127716.82</td>\n",
              "      <td>California</td>\n",
              "      <td>156122.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>130298.13</td>\n",
              "      <td>145530.06</td>\n",
              "      <td>323876.68</td>\n",
              "      <td>Florida</td>\n",
              "      <td>155752.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>120542.52</td>\n",
              "      <td>148718.95</td>\n",
              "      <td>311613.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>152211.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>123334.88</td>\n",
              "      <td>108679.17</td>\n",
              "      <td>304981.62</td>\n",
              "      <td>California</td>\n",
              "      <td>149759.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101913.08</td>\n",
              "      <td>110594.11</td>\n",
              "      <td>229160.95</td>\n",
              "      <td>Florida</td>\n",
              "      <td>146121.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100671.96</td>\n",
              "      <td>91790.61</td>\n",
              "      <td>249744.55</td>\n",
              "      <td>California</td>\n",
              "      <td>144259.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>Florida</td>\n",
              "      <td>141585.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>91992.39</td>\n",
              "      <td>135495.07</td>\n",
              "      <td>252664.93</td>\n",
              "      <td>California</td>\n",
              "      <td>134307.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>119943.24</td>\n",
              "      <td>156547.42</td>\n",
              "      <td>256512.92</td>\n",
              "      <td>Florida</td>\n",
              "      <td>132602.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>114523.61</td>\n",
              "      <td>122616.84</td>\n",
              "      <td>261776.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>129917.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>78013.11</td>\n",
              "      <td>121597.55</td>\n",
              "      <td>264346.06</td>\n",
              "      <td>California</td>\n",
              "      <td>126992.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>94657.16</td>\n",
              "      <td>145077.58</td>\n",
              "      <td>282574.31</td>\n",
              "      <td>New York</td>\n",
              "      <td>125370.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>91749.16</td>\n",
              "      <td>114175.79</td>\n",
              "      <td>294919.57</td>\n",
              "      <td>Florida</td>\n",
              "      <td>124266.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>86419.70</td>\n",
              "      <td>153514.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>122776.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>76253.86</td>\n",
              "      <td>113867.30</td>\n",
              "      <td>298664.47</td>\n",
              "      <td>California</td>\n",
              "      <td>118474.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>78389.47</td>\n",
              "      <td>153773.43</td>\n",
              "      <td>299737.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>111313.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>73994.56</td>\n",
              "      <td>122782.75</td>\n",
              "      <td>303319.26</td>\n",
              "      <td>Florida</td>\n",
              "      <td>110352.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>67532.53</td>\n",
              "      <td>105751.03</td>\n",
              "      <td>304768.73</td>\n",
              "      <td>Florida</td>\n",
              "      <td>108733.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77044.01</td>\n",
              "      <td>99281.34</td>\n",
              "      <td>140574.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>108552.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64664.71</td>\n",
              "      <td>139553.16</td>\n",
              "      <td>137962.62</td>\n",
              "      <td>California</td>\n",
              "      <td>107404.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>75328.87</td>\n",
              "      <td>144135.98</td>\n",
              "      <td>134050.07</td>\n",
              "      <td>Florida</td>\n",
              "      <td>105733.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>72107.60</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>353183.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>105008.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>66051.52</td>\n",
              "      <td>182645.56</td>\n",
              "      <td>118148.20</td>\n",
              "      <td>Florida</td>\n",
              "      <td>103282.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65605.48</td>\n",
              "      <td>153032.06</td>\n",
              "      <td>107138.38</td>\n",
              "      <td>New York</td>\n",
              "      <td>101004.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>61994.48</td>\n",
              "      <td>115641.28</td>\n",
              "      <td>91131.24</td>\n",
              "      <td>Florida</td>\n",
              "      <td>99937.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>61136.38</td>\n",
              "      <td>152701.92</td>\n",
              "      <td>88218.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>97483.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>63408.86</td>\n",
              "      <td>129219.61</td>\n",
              "      <td>46085.25</td>\n",
              "      <td>California</td>\n",
              "      <td>97427.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>55493.95</td>\n",
              "      <td>103057.49</td>\n",
              "      <td>214634.81</td>\n",
              "      <td>Florida</td>\n",
              "      <td>96778.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>46426.07</td>\n",
              "      <td>157693.92</td>\n",
              "      <td>210797.67</td>\n",
              "      <td>California</td>\n",
              "      <td>96712.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>46014.02</td>\n",
              "      <td>85047.44</td>\n",
              "      <td>205517.64</td>\n",
              "      <td>New York</td>\n",
              "      <td>96479.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>28663.76</td>\n",
              "      <td>127056.21</td>\n",
              "      <td>201126.82</td>\n",
              "      <td>Florida</td>\n",
              "      <td>90708.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>California</td>\n",
              "      <td>89949.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20229.59</td>\n",
              "      <td>65947.93</td>\n",
              "      <td>185265.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>81229.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>38558.51</td>\n",
              "      <td>82982.09</td>\n",
              "      <td>174999.30</td>\n",
              "      <td>California</td>\n",
              "      <td>81005.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>28754.33</td>\n",
              "      <td>118546.05</td>\n",
              "      <td>172795.67</td>\n",
              "      <td>California</td>\n",
              "      <td>78239.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>27892.92</td>\n",
              "      <td>84710.77</td>\n",
              "      <td>164470.71</td>\n",
              "      <td>Florida</td>\n",
              "      <td>77798.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>23640.93</td>\n",
              "      <td>96189.63</td>\n",
              "      <td>148001.11</td>\n",
              "      <td>California</td>\n",
              "      <td>71498.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>15505.73</td>\n",
              "      <td>127382.30</td>\n",
              "      <td>35534.17</td>\n",
              "      <td>New York</td>\n",
              "      <td>69758.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>22177.74</td>\n",
              "      <td>154806.14</td>\n",
              "      <td>28334.72</td>\n",
              "      <td>California</td>\n",
              "      <td>65200.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1000.23</td>\n",
              "      <td>124153.04</td>\n",
              "      <td>1903.93</td>\n",
              "      <td>New York</td>\n",
              "      <td>64926.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1315.46</td>\n",
              "      <td>115816.21</td>\n",
              "      <td>297114.46</td>\n",
              "      <td>Florida</td>\n",
              "      <td>49490.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.00</td>\n",
              "      <td>135426.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>California</td>\n",
              "      <td>42559.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
              "      <td>51743.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>35673.41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          RDS        ADM        MKS       State     Profit\n",
              "0   165349.20  136897.80  471784.10    New York  192261.83\n",
              "1   162597.70  151377.59  443898.53  California  191792.06\n",
              "2   153441.51  101145.55  407934.54     Florida  191050.39\n",
              "3   144372.41  118671.85  383199.62    New York  182901.99\n",
              "4   142107.34   91391.77  366168.42     Florida  166187.94\n",
              "5   131876.90   99814.71  362861.36    New York  156991.12\n",
              "6   134615.46  147198.87  127716.82  California  156122.51\n",
              "7   130298.13  145530.06  323876.68     Florida  155752.60\n",
              "8   120542.52  148718.95  311613.29    New York  152211.77\n",
              "9   123334.88  108679.17  304981.62  California  149759.96\n",
              "10  101913.08  110594.11  229160.95     Florida  146121.95\n",
              "11  100671.96   91790.61  249744.55  California  144259.40\n",
              "12   93863.75  127320.38  249839.44     Florida  141585.52\n",
              "13   91992.39  135495.07  252664.93  California  134307.35\n",
              "14  119943.24  156547.42  256512.92     Florida  132602.65\n",
              "15  114523.61  122616.84  261776.23    New York  129917.04\n",
              "16   78013.11  121597.55  264346.06  California  126992.93\n",
              "17   94657.16  145077.58  282574.31    New York  125370.37\n",
              "18   91749.16  114175.79  294919.57     Florida  124266.90\n",
              "19   86419.70  153514.11       0.00    New York  122776.86\n",
              "20   76253.86  113867.30  298664.47  California  118474.03\n",
              "21   78389.47  153773.43  299737.29    New York  111313.02\n",
              "22   73994.56  122782.75  303319.26     Florida  110352.25\n",
              "23   67532.53  105751.03  304768.73     Florida  108733.99\n",
              "24   77044.01   99281.34  140574.81    New York  108552.04\n",
              "25   64664.71  139553.16  137962.62  California  107404.34\n",
              "26   75328.87  144135.98  134050.07     Florida  105733.54\n",
              "27   72107.60  127864.55  353183.81    New York  105008.31\n",
              "28   66051.52  182645.56  118148.20     Florida  103282.38\n",
              "29   65605.48  153032.06  107138.38    New York  101004.64\n",
              "30   61994.48  115641.28   91131.24     Florida   99937.59\n",
              "31   61136.38  152701.92   88218.23    New York   97483.56\n",
              "32   63408.86  129219.61   46085.25  California   97427.84\n",
              "33   55493.95  103057.49  214634.81     Florida   96778.92\n",
              "34   46426.07  157693.92  210797.67  California   96712.80\n",
              "35   46014.02   85047.44  205517.64    New York   96479.51\n",
              "36   28663.76  127056.21  201126.82     Florida   90708.19\n",
              "37   44069.95   51283.14  197029.42  California   89949.14\n",
              "38   20229.59   65947.93  185265.10    New York   81229.06\n",
              "39   38558.51   82982.09  174999.30  California   81005.76\n",
              "40   28754.33  118546.05  172795.67  California   78239.91\n",
              "41   27892.92   84710.77  164470.71     Florida   77798.83\n",
              "42   23640.93   96189.63  148001.11  California   71498.49\n",
              "43   15505.73  127382.30   35534.17    New York   69758.98\n",
              "44   22177.74  154806.14   28334.72  California   65200.33\n",
              "45    1000.23  124153.04    1903.93    New York   64926.08\n",
              "46    1315.46  115816.21  297114.46     Florida   49490.75\n",
              "47       0.00  135426.92       0.00  California   42559.73\n",
              "48     542.05   51743.15       0.00    New York   35673.41"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K6PiIa4ciSlL"
      },
      "source": [
        "##Model Deletion Diagoniostics & Final Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RrMnwWojniA"
      },
      "source": [
        "Final_Model = smf.ols('Profit~RDS+ADM+MKS',data=St2).fit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "hDFxRdHPj1eK",
        "outputId": "5d648aa7-52cd-47ee-d362-3b9663af27d9"
      },
      "source": [
        "Final_Model .summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.961</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.959</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   372.8</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 26 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>8.85e-32</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>09:31:09</td>     <th>  Log-Likelihood:    </th> <td> -506.28</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    49</td>      <th>  AIC:               </th> <td>   1021.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   1028.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td> 5.238e+04</td> <td> 5542.657</td> <td>    9.451</td> <td> 0.000</td> <td> 4.12e+04</td> <td> 6.35e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>RDS</th>       <td>    0.7830</td> <td>    0.038</td> <td>   20.470</td> <td> 0.000</td> <td>    0.706</td> <td>    0.860</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ADM</th>       <td>   -0.0222</td> <td>    0.043</td> <td>   -0.518</td> <td> 0.607</td> <td>   -0.109</td> <td>    0.064</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MKS</th>       <td>    0.0252</td> <td>    0.014</td> <td>    1.825</td> <td> 0.075</td> <td>   -0.003</td> <td>    0.053</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 0.082</td> <th>  Durbin-Watson:     </th> <td>   1.598</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.960</td> <th>  Jarque-Bera (JB):  </th> <td>   0.232</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.082</td> <th>  Prob(JB):          </th> <td>   0.890</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.706</td> <th>  Cond. No.          </th> <td>1.41e+06</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.41e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                 Profit   R-squared:                       0.961\n",
              "Model:                            OLS   Adj. R-squared:                  0.959\n",
              "Method:                 Least Squares   F-statistic:                     372.8\n",
              "Date:                Fri, 26 Nov 2021   Prob (F-statistic):           8.85e-32\n",
              "Time:                        09:31:09   Log-Likelihood:                -506.28\n",
              "No. Observations:                  49   AIC:                             1021.\n",
              "Df Residuals:                      45   BIC:                             1028.\n",
              "Df Model:                           3                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept   5.238e+04   5542.657      9.451      0.000    4.12e+04    6.35e+04\n",
              "RDS            0.7830      0.038     20.470      0.000       0.706       0.860\n",
              "ADM           -0.0222      0.043     -0.518      0.607      -0.109       0.064\n",
              "MKS            0.0252      0.014      1.825      0.075      -0.003       0.053\n",
              "==============================================================================\n",
              "Omnibus:                        0.082   Durbin-Watson:                   1.598\n",
              "Prob(Omnibus):                  0.960   Jarque-Bera (JB):                0.232\n",
              "Skew:                          -0.082   Prob(JB):                        0.890\n",
              "Kurtosis:                       2.706   Cond. No.                     1.41e+06\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 1.41e+06. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tetx8lijj5ru"
      },
      "source": [
        "Thus the Accuracy of the model has improved to 96.1%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQqGmdU2llfS"
      },
      "source": [
        "##Model Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "4QMtCT87lrRc",
        "outputId": "335d537e-ae4c-4b63-f722-1ea4bed29e58"
      },
      "source": [
        "#New data for prediction\n",
        "new_data=pd.DataFrame({'RDS':78000,'ADM':127864.55,\"MKS\":249839.44},index=[1])\n",
        "new_data"
      ],
      "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>RDS</th>\n",
              "      <th>ADM</th>\n",
              "      <th>MKS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>78000</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>249839.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     RDS        ADM        MKS\n",
              "1  78000  127864.55  249839.44"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2EhBcyXmNje",
        "outputId": "50f0df47-4f48-4a00-efa1-0056c41721d2"
      },
      "source": [
        "# Manual prediction\n",
        "Final_Model.predict(new_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    116922.172202\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a9-gRuPmP2F",
        "outputId": "30c91290-1b7d-42ae-cab8-ad7d01f323c7"
      },
      "source": [
        "#Automatic Prediction \n",
        "pred_y = Final_Model.predict(St2)\n",
        "pred_y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     190716.676999\n",
              "1     187537.122227\n",
              "2     180575.526396\n",
              "3     172461.144642\n",
              "4     170863.486721\n",
              "5     162582.583177\n",
              "6     157741.338633\n",
              "7     159347.735318\n",
              "8     151328.826941\n",
              "9     154236.846778\n",
              "10    135507.792682\n",
              "11    135472.855621\n",
              "12    129355.599449\n",
              "13    127780.129139\n",
              "14    149295.404796\n",
              "15    145937.941975\n",
              "16    117437.627921\n",
              "17    130408.626295\n",
              "18    129129.234457\n",
              "19    116641.003121\n",
              "20    117097.731866\n",
              "21    117911.019038\n",
              "22    115248.217796\n",
              "23    110603.139045\n",
              "24    114051.073877\n",
              "25    103398.054385\n",
              "26    111547.638935\n",
              "27    114916.165026\n",
              "28    103027.229434\n",
              "29    103057.621761\n",
              "30    100656.410227\n",
              "31     99088.213693\n",
              "32    100325.741335\n",
              "33     98962.303136\n",
              "34     90552.307809\n",
              "35     91709.288672\n",
              "36     77080.554255\n",
              "37     90722.503244\n",
              "38     71433.021956\n",
              "39     85147.375646\n",
              "40     76625.510303\n",
              "41     76492.145175\n",
              "42     72492.394974\n",
              "43     62592.049718\n",
              "44     67025.731107\n",
              "45     50457.297206\n",
              "46     58338.443625\n",
              "47     49375.776655\n",
              "48     51658.096812\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZTuC4I2mUXM"
      },
      "source": [
        "Table Containing R square values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ohwTSYVmtFs"
      },
      "source": [
        "Prepared_Models=pd.DataFrame(['Model','Fianl_Model'],index=[1])\n",
        "new_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Jyp86D-_nGpb",
        "outputId": "b7ae474a-60b7-4b2f-83c5-f51e69232f5c"
      },
      "source": [
        "d2={'Prepared_models':['Model','Final_Model'],'Rsquared':[model.rsquared,Final_Model.rsquared]}\n",
        "table=pd.DataFrame(d2)\n",
        "table"
      ],
      "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>Prepared_models</th>\n",
              "      <th>Rsquared</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Model</td>\n",
              "      <td>0.950746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Final_Model</td>\n",
              "      <td>0.961316</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Prepared_models  Rsquared\n",
              "0           Model  0.950746\n",
              "1     Final_Model  0.961316"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcNeDmFVoNO0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}