Multi_Linear_Regression_50Startups.ipynb

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      "cell_type": "markdown",
      "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",
        "\n",
        "Administration -- spend on administration in the past few years\n",
        "\n",
        "Marketing Spend -- spend on Marketing in the past few years\n",
        "\n",
        "State -- states from which data is collected\n",
        "\n",
        "Profit  -- profit of each state in the past few years\n"
      ],
      "metadata": {
        "id": "c_IMkHxHCEbN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Load Data***"
      ],
      "metadata": {
        "id": "12C0jSauFMuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/50_Startups.csv')"
      ],
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        "id": "zYkCjxAbFLWs"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "   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"
            ],
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              "      <th>Marketing Spend</th>\n",
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              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
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              "      <td>192261.83</td>\n",
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              "      <th>1</th>\n",
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              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
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              "      <td>91391.77</td>\n",
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              "      <td>Florida</td>\n",
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            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend       State    Profit\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"
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              "      <th>Marketing Spend</th>\n",
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              "      <th>Profit</th>\n",
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              "    <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",
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              "    <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",
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              "      <td>California</td>\n",
              "      <td>42559.73</td>\n",
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              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>45173.06</td>\n",
              "      <td>California</td>\n",
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              "                                                     [key], {});\n",
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      "source": [
        "df.shape"
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        "id": "jPxDL5jOIb1F",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***EDA***"
      ],
      "metadata": {
        "id": "yZH-HgySFgi1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "ojmTksXVFfmY",
        "outputId": "966e43ba-2db3-4581-800b-7ee3cda1a8fe"
      },
      "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": "markdown",
      "source": [
        "All dtypes are correct. Only the State column is object dtype so we will have to transform it using dummy variables."
      ],
      "metadata": {
        "id": "IZJbXS64nIRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for missing values\n",
        "df.isnull().sum()"
      ],
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        "colab": {
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        },
        "id": "R_QDmi8AFLKN",
        "outputId": "ed0e1a45-6734-4ac2-ecd1-4c7319ea186d"
      },
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to genrate heat map\n",
        "import seaborn as sns\n",
        "cols = df.columns\n",
        "colors = ['#000099', '#ffff00']   #define colors, yellow for missing, blue for non missing\n",
        "sns.heatmap(df[cols].isnull(),\n",
        "               cmap= sns.color_palette(colors))"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4e7a5d990>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, we can conclude no missing values in any column."
      ],
      "metadata": {
        "id": "4r8XTD9tGK4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for duplicate values\n",
        "df[df.duplicated()].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gew3VdqrFLA7",
        "outputId": "5a9ff269-912b-4249-bd42-984aecfb543d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "no duplicate rows"
      ],
      "metadata": {
        "id": "byqHDnfsH5tn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXOoB3krGjig",
        "outputId": "ff6323f6-14f5-4d32-eb16-ed8e8a9ce12d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R&D Spend          float64\n",
              "Administration     float64\n",
              "Marketing Spend    float64\n",
              "State               object\n",
              "Profit             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#check for outliers"
      ],
      "metadata": {
        "id": "U6Xt-XY1Km8O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Yh-LQohzIYn1",
        "outputId": "3d179223-7b23-424f-9846-b691124afc6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d425b77-3dfc-4853-b46b-702843467e0e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      display:flex;\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7d425b77-3dfc-4853-b46b-702843467e0e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7d425b77-3dfc-4853-b46b-702843467e0e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outlier Detection\n",
        "import matplotlib.pyplot as plt\n",
        "box = plt.boxplot(df['R&D Spend'])\n",
        "plt.title('R&D Spend')\n",
        "[item.get_ydata() for item in box['fliers']]   #fliers for otuliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "fSjX59fOKqn2",
        "outputId": "4aed2a05-b911-4cc2-ce65-56f3c36f6fa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([], dtype=float64)]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['R&D Spend'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "XllIH_QEK0MS",
        "outputId": "6691fd9b-77f9-4105-845d-e677edb0747e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4e26bb810>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "box1 = plt.boxplot(df['Administration'])\n",
        "plt.title('Administration')\n",
        "[item.get_ydata() for item in box1['fliers']]   #fliers for otuliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ssTqLCOJK8Aw",
        "outputId": "5179b4ee-baab-475a-e762-76f35bea7ea4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([], dtype=float64)]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Administration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "9mv2evtwLJaT",
        "outputId": "92875f6d-ed22-4428-b421-d3f6f67a567c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4e056d690>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "box2 = plt.boxplot(df['Marketing Spend'])\n",
        "plt.title('Marketing Spend')\n",
        "[item.get_ydata() for item in box2['fliers']]   #fliers for otuliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "olQu5-ccLObh",
        "outputId": "f86fd463-1213-47fc-9009-803cb0c29ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([], dtype=float64)]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Marketing Spend'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "dkmsgnIuLj9L",
        "outputId": "d552525d-99f2-460b-a3e2-0a09e537aee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4dff6af90>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.State.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vCViS_LlBlV",
        "outputId": "1730ba66-4d1f-447d-c87c-e9b41167f02a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "New York      17\n",
              "California    17\n",
              "Florida       16\n",
              "Name: State, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['State'].value_counts().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "OfvE7YADlGGZ",
        "outputId": "fbb0f50e-0d50-47f0-8303-8c214c69bf27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4d48b3750>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "box3 = plt.boxplot(df['Profit'])\n",
        "plt.title('Profit')\n",
        "[item.get_ydata() for item in box3['fliers']]   #fliers for otuliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "YewovxkyLsQ5",
        "outputId": "a471f785-82e8-4824-cf2a-4ff3bf5c6e87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([14681.4])]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Profit'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "m-01y1CsLyn5",
        "outputId": "b765fe33-078a-4aa6-b4ee-3f2d6ecf467b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4dfe72910>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can check here, we do not have outliers in independent variable but it is in the dependent variable. "
      ],
      "metadata": {
        "id": "cg9ZYDG2YkWR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier in the dependent variable:**"
      ],
      "metadata": {
        "id": "jBQXHdItk3T0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "Q1 = np.quantile(df.Profit,0.25)\n",
        "Q3 = np.quantile(df.Profit,0.75)\n",
        "med = np.median(df.Profit)\n",
        "IQR = Q3 - Q1\n",
        "upper_bound = Q3+(1.5*IQR)\n",
        "lower_bound = Q1-(1.5*IQR)\n",
        "print('First Quantile=', Q1,'\\n' 'Second Quantile=', med,'\\n' 'Third Quantile=', Q3,'\\n'\n",
        "      'Inter-Quartile Range=', IQR,'\\n' 'Upper Whisker=', upper_bound,'\\n' 'Lower Whisker=', lower_bound)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBsDQiwWYjEI",
        "outputId": "c510dd16-395d-402c-b57a-f9a618cb3041"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First Quantile= 90138.9025 \n",
            "Second Quantile= 107978.19 \n",
            "Third Quantile= 139765.97749999998 \n",
            "Inter-Quartile Range= 49627.07499999998 \n",
            "Upper Whisker= 214206.58999999997 \n",
            "Lower Whisker= 15698.290000000023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Outliers = df.Profit[(df.Profit <= lower_bound) | (df.Profit >= upper_bound)]\n",
        "print('The outlier in the boxplot:',Outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEViUA9mYZGF",
        "outputId": "cbc91ee2-8f0e-43ce-9555-195ac185d8ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The outlier in the boxplot: 49    14681.4\n",
            "Name: Profit, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(df[df.index.isin([49])],)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "ZfjeTFllZw8I",
        "outputId": "ca0fb25d-91e2-4d51-ea55-daad97fe4573"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend       State   Profit\n",
              "49        0.0        116983.8         45173.06  California  14681.4"
            ],
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              "      <th>49</th>\n",
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              "      <td>45173.06</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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        "df.head()"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w_uryXV0Z86t",
        "outputId": "3ff04b61-0f9a-4542-be7f-db706ac73c15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
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              "      <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",
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              "    <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",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a7fb699-ff4a-4065-90b1-9d04e75848fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (9,6))\n",
        "plt.subplot(131)\n",
        "plt.boxplot(np.log(df['Profit']))\n",
        "plt.subplot(132)\n",
        "plt.boxplot(np.sqrt(df['Profit']))\n",
        "plt.subplot(133)\n",
        "plt.boxplot(np.cbrt(df['Profit']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "NrYdKJcvaOsG",
        "outputId": "8962bb7d-8f5c-4f2d-99ff-2d3b4ee7f9d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7fb4d47381d0>],\n",
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              " 'whiskers': [<matplotlib.lines.Line2D at 0x7fb4d4738710>,\n",
              "  <matplotlib.lines.Line2D at 0x7fb4d4738c50>]}"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can observe, even after doing transformation we are not able to remove the outliers."
      ],
      "metadata": {
        "id": "uUibashujqHh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Let us create a model and then we will compare models.***"
      ],
      "metadata": {
        "id": "DziZEhDDtFKp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Renaming columns to make names shorter\n",
        "df1 = df.rename({'R&D Spend':'RD', 'Marketing Spend':'Marketing'}, axis = 1)"
      ],
      "metadata": {
        "id": "TsKlOY3CMlNC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CwHNm3d1Pxek",
        "outputId": "374ff529-fbc0-47f4-b599-873920c44c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          RD  Administration  Marketing       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"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>RD</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c80ca1b-516e-4d47-b3c4-02f7ea2ace29')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-6c80ca1b-516e-4d47-b3c4-02f7ea2ace29 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c80ca1b-516e-4d47-b3c4-02f7ea2ace29');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As \"State\" is a categorical object column we will have to perform transformation to this column."
      ],
      "metadata": {
        "id": "Vhb_M9tsVUb8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.formula.api as smf"
      ],
      "metadata": {
        "id": "K11LOZwIPzST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = smf.ols('Profit~RD+Administration+Marketing', data = df1).fit()"
      ],
      "metadata": {
        "id": "Km6uFAUMUvft"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model parameters\n",
        "model.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YH4Cl0XStdXg",
        "outputId": "22698c53-5fb4-49ed-c65b-69ad8fcf6717"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept         50122.192990\n",
              "RD                    0.805715\n",
              "Administration       -0.026816\n",
              "Marketing             0.027228\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(model.rsquared, model.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNss2aADtliK",
        "outputId": "3f5d2c7e-607a-4a97-b9b9-c5f58d5f115c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9507459940683246, 0.9475337762901719)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us try median imputation for profit outlier."
      ],
      "metadata": {
        "id": "5ylq7IyvuHtN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.Profit.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G00uGYoht0fm",
        "outputId": "75264bdc-105b-4b43-9494-74d2a0c9e057"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "107978.19"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df1.copy()\n",
        "sns.boxplot(df1['Profit'])\n",
        "plt.title('Profit before median imputation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "9hWStyyvuOpf",
        "outputId": "a47adff9-b1f9-40fe-d92b-042e720176a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Profit before median imputation')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df2['Profit']:\n",
        "    q1 = np.quantile(df1.Profit,0.25)\n",
        "    q3 = np.quantile(df1.Profit,0.75)\n",
        "    med = np.median(df1.Profit)\n",
        "    iqr = q3 - q1\n",
        "    upper_bound = q3+(1.5*iqr)\n",
        "    lower_bound = q1-(1.5*iqr)\n",
        "    if i > upper_bound or i < lower_bound:\n",
        "        df2['Profit'] = df2['Profit'].replace(i, np.median(df2['Profit']))\n",
        "sns.boxplot(df2['Profit'])\n",
        "plt.title('Profit after median imputation')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "rdSZCVUGuXA6",
        "outputId": "8b80d102-f873-4080-c96f-02aee36c81aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compare median imputation model with first model\n",
        "model1 = smf.ols('Profit~RD+Administration+Marketing', data = df2).fit()\n",
        "(model1.rsquared, model1.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZ6Mx6MOu5jK",
        "outputId": "63a43e0c-41f1-494f-8a55-f875e4d46d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9182958360807372, 0.91296730365122)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By median imputation the model accuracy has reduced. So we will not follow this method."
      ],
      "metadata": {
        "id": "y_eiP3UnvuP0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us try with Mean imputation."
      ],
      "metadata": {
        "id": "UENLMIr2v2vj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.Profit.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxGPY44KvqUI",
        "outputId": "4c71fa65-24b9-48de-98c4-c0fc397ea716"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112012.63920000002"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3=df1.copy()\n",
        "sns.boxplot(df1['Profit'])\n",
        "plt.title('Profit before mean imputation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "eHN1Ck-awao5",
        "outputId": "1feae63f-71ad-4216-f96d-a21021689c36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Profit before mean imputation')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df3['Profit']:\n",
        "    q1 = np.quantile(df1.Profit,0.25)\n",
        "    q3 = np.quantile(df1.Profit,0.75)\n",
        "    me = np.mean(df1.Profit)\n",
        "    iqr = q3 - q1\n",
        "    upper_bound = q3+(1.5*iqr)\n",
        "    lower_bound = q1-(1.5*iqr)\n",
        "    if i > upper_bound or i < lower_bound:\n",
        "        df3['Profit'] = df3['Profit'].replace(i, np.mean(df3['Profit']))\n",
        "sns.boxplot(df3['Profit'])\n",
        "plt.title('Profit after mean imputation')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "bWf34xjiv_L6",
        "outputId": "3ff53744-4db5-4266-a94d-7b0bac44f1ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEWCAYAAACUg3d7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATPklEQVR4nO3de7CdVXnH8e9DjpBwMRBCIwmXAClWmY6I1MsoNiBVwAt0xrZ4mQGRXsTGSK2OiLXUVmcE2xGiFqgXRBEBRaVURKVQcTpCCYaLAnKAIBwBQxAkEi/A6h9rHfOenXPZe+ecvfdZ+X5m9uTd7209e+39/vZ71vuek0gpIUma3bbpdwGSpC1nmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwn+Ui4qURcWdEbIiIYyLiiog4bhr3/7aIeKjsf9fp2u+gi4i9ymue0+9aujHdnwMNvvA+896LiLXAIuAp4JfAFcDfppQ2dLGvq4DLUkpnjrPseODElNLLuqzzGcAvgBenlG6KiOXAF1JKe3SzP7UnIq4h9/On2lz/NGBZSunNM1mXBptn5v3z2pTSjsBBwMHA+1tXiIihNvazN/DDaa5t1CJg7nTtv83XI6kbKSUfPX4Aa4HDG8/PAC4v0wl4O3AncE+Z95fAMPAIcBmwuMy/C3ga2AhsALYDrgFOBJ4D/Ip89r8BeHSCWt4C3AY8DtwN/HWZvz/5p4ZUtr+6tPN0eb4BWEw+IXhvqWU9cDGwoOxjadn+rcBPgO+O0/5y4H7gPcDPgAeAY4CjgB+X1/y+xvoTtleWXwI8CDwGfBc4oLHsPOATwH+V13sdsN8E/TJa+1B5fg3wL8D/ltf+n8CuwAXkn17+D1ja2D4B7yh9+nB5j7cpy04jn3lv1hbwofKe/aq08/GyzpnAfaWt1cAhZf4RwG+A35b1b2rUe2Kjz94P3Fv6+Hxgfkvbx5X36GHg1H4fIz66yJV+F7A1PmiEObAn+cz3n8vzBHwbWADMAw4rB9hB5LBe1QxFNv9iaB7ExwPfm6KWVwP7AQH8MfAEcFBZ1hpoy4H7W7ZfCXwf2KPUdw5wYcv25wM7APPGaX858CTwAeAZ5C+udcAXgZ2AA8hfIvtM1V5ZfkLZbjvgY8CaxrLzyF8ALyQH5wXAlybol9bXfg35C3U/YD7wI/KXzeFlX+cDn21sn8hfgAuAvcq6o+/LaUwQ5q3vYWOdN5O/PIaAd5G/sOaOt79xPgcnlNr3BXYELgU+39L2f5A/b88Dfg08p9/HiY/OHn0vYGt8kAN4A/Ao+Wzpk6NBVw6swxrrfho4vfF8R/JZ2NLGvroO83Fq+xqwsky3hsxyNg/z24BXNJ7vXuobamy/7yTtLSeH9ZzyfKeyzYsa66wGjpmqvXH2vXPZ1+hZ6HnApxrLjwJun6Cu8QL21MbyfwWuaDx/LWO/OBJwROP5ScBVZXpM+E7Q1onj1dXY5ufA88bb3zifg6uAkxrLnj3Oe7RHY/n1wLH9Pk58dPZwDLN/jkkpfWeCZfc1phcDN44+SSltiIj1wBJykG+RiDgS+EfysMo2wPbALR3sYm/gqxHxdGPeU+Tx9lH3Mbn1KaWnyvTG8u9DjeUbyV9ik7YXEQ+Shyn+DNiNPCQEsJA87AL5jHbUE439tqO1polqHNV83feS38uuRMTfk4erFpPD95nk19WOxaX9Zi1DjH2PtqRfNAC8ADqYmrcY/ZQcYABExA7kH7dHOtzPZiJiO+ArwEeBRSmlnYFvkIdc2t3ffcCRKaWdG4+5KaWRKbbr1mTtvRE4mjz0MZ981gkTv56Ztmdjei/yewn5WsT2jWXPatluTH9FxCHkawp/DuxS3qfH2PS6purfMZ+hUsuTjP0y0ixnmA++C4G3RMSBJXw/DFyXUlrbxrYPAXtExLYTLN+WPLa8DniynKW/cor97RoR8xvzzgY+FBF7A0TEbhFxdBu1dWuy9nYij/euJ4flh2ewjna8OyJ2iYg9yWP9F5X5a4CXl3vZ5wOntGz3EHl8e9RO5PBdBwxFxAfIZ+bN9ZdGxETH84XAyRGxT0TsSO6Xi1JKT27Ji9NgMcwHXBmK+QfyGfQD5Atwx7a5+X+TL64+GBEPj7Pvx8l3XFxMHoN9I/lumYlquZ0cDHdHxKMRsZh8l8VlwLci4nHyxckXtVlfNyZr73zyEMII+QLl92ewjnZ8nTzev4Z8B82nAVJK3yYH+81l+eUt250JvD4ifh4RZwFXAt8kX0S9l3ynS3MI55Ly7/qIuJHNfQb4PPnunnvK9iu29MVpsPhLQ9IMiIgE/H5KabjftWjr4Jm5JFXAMJekCjjMIkkV8MxckirQ0S8NLVy4MC1dunSGSpGkOq1evfrhlNJuM9lGR2G+dOlSbrjhhpmqRZKqFBH3Tr3WlnGYRZIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSapAR/8HqLYuq1atYnh4uG/tj4yMALBkyZK+1dBry5YtY8WKFf0uQ7OQYa4JDQ8Ps+bW23hq+wV9aX/OE48B8OCvt46P6ZwnHul3CZrFto6jRF17avsFbPyDo/rS9rzbvwHQt/Z7bfT1St1wzFySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIq0JMwX7VqFatWrepFU5I0rWZLfg31opHh4eFeNCNJ02625JfDLJJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklSBoV40MjIywsaNG1m5cmUvmtM0GR4eZpvfpH6XsdXY5le/YHj4cY+TATM8PMy8efP6XcaUpjwzj4i/iogbIuKGdevW9aImSVKHpjwzTymdC5wLcPDBB3d1mrZkyRIAzjzzzG42V5+sXLmS1Xc/1O8ythpPz30my/Zd5HEyYGbLT0qOmUtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqsBQLxpZtmxZL5qRpGk3W/KrJ2G+YsWKXjQjSdNutuSXwyySVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoM9bsADbY5TzzCvNu/0ae21wP0rf1em/PEI8CifpehWcow14SWLVvW1/ZHRp4EYMmSrSXgFvW9zzV7Geaa0IoVK/pdgqQ2OWYuSRUwzCWpAoa5JFXAMJekChjmklQBw1ySKmCYS1IFDHNJqoBhLkkVMMwlqQKGuSRVwDCXpAoY5pJUAcNckipgmEtSBQxzSaqAYS5JFTDMJakChrkkVcAwl6QKREqp/ZUj1gH3zlAtC4GHZ2jfW8raumNt3bG2zg1qXZBr2yGltNtMNtJRmM+kiLghpXRwv+sYj7V1x9q6Y22dG9S6oHe1OcwiSRUwzCWpAoMU5uf2u4BJWFt3rK071ta5Qa0LelTbwIyZS5K6N0hn5pKkLhnmklSDlNK0P4A5wA+Ay8vzfYDrgGHgImDbMn+78ny4LF/a2McpZf4dwKsa848o84aB93ZY187Al4HbgduAlwALgG8Dd5Z/dynrBnBWaedm4KDGfo4r698JHNeY/wLglrLNWZRhrDZrOxn4IXArcCEwt1/9BnwG+Blwa2PejPfTRG20UdsZ5T29GfgqsHO3/dFNn09WW2PZu4AELByUfivzV5S++yFw+qD0G3Ag8H1gDXAD8MJe9xuwJ3A18KPSPysH6VjYrB87CcMOgunvgC+yKcwvBo4t02cDbyvTJwFnl+ljgYvK9HOBm8oHYR/gLvIXxJwyvS+wbVnnuR3U9TngxDK9LTncTx/9UALvBT5Spo8Crihv0IuB6xqdfHf5d5cyPfpmXl/WjbLtkW3WtQS4B5jX6K/j+9VvwMuBgxh7cM14P03URhu1vRIYKtMfadTWcX902udT1dYIhSvJv3C3cID67VDgO8B25fnvDUq/Ad9qvL6jgGt63W/A7pRABnYCflz6ZiCOhc36sdvAniQI9gCuAg4DLi9FPsymg+0lwJVl+krgJWV6qKwX5LOCUxr7vLJs97tty/wx601R13xyYEbL/DuA3Rtv3h1l+hzgDa3rAW8AzmnMP6fM2x24vTF/zHpT1LYEuK+82UOl317Vz34DljL24Jrxfpqojalqa1n2p8AF473Oqfqjm89qO7WRfxp8HrCWTWHe934jB/Dh46zX934r2/1F47V+sV/91tj268CfTLRdP2tLKc3ImPnHgPcAT5fnuwKPppSeLM/vJ4cXbAoxyvLHyvq/m9+yzUTz27EPsA74bET8ICI+FRE7AItSSg+UdR4EFrXW1mYNS8p0x7WllEaAjwI/AR4g98NqBqPfRvWinyZqoxMnkM9wuqmtm8/qpCLiaGAkpXRTy6JB6Lf9gUMi4rqI+J+I+KMua5v2fgPeCZwREfeRj41TuqxtWvotIpYCzycPFQ3ksTCtYR4RrwF+llJaPZ37nSZD5B/l/j2l9Hzgl+QfX34n5a/B1OvCImIX4GjyF85iYAfy2ORA6kU/ddNGRJwKPAlcMCNFdSgitgfeB3ygV2122G9D5J8GXwy8G7g4ImKmauvQ24CTU0p7kq8nfXomG5us3yJiR+ArwDtTSr9od7te1NY03WfmLwVeFxFrgS+Rh1rOBHaOiKGyzh7ASJkeIY8nUpbPB9Y357dsM9H8dtwP3J9Suq48/zI53B+KiN1LDbuTL8SMqa3NGkbKdDe1HQ7ck1Jal1L6LXApuS8Hod9G9aKfJmpjShFxPPAa4E3lw99NbevpvM8nsx/5C/qmckzsAdwYEc/qoraZ6Lf7gUtTdj35p+mFXdQ23f0G+YLhpWX6EuCFrftrs7Yt6reIeAY5yC9IKV06xXb9fU+nGofp9gEsZ9MF0EsYe3HkpDL9dsZeHLm4TB/A2Aswd5MvvgyV6X3YdAHmgA5quhZ4dpk+jXwXxBmMvdBwepl+NWMvZlxf5i8gj73vUh73AAvKstaLGUe1WdeLyFfLty/bfo58l0Hf+o3NxzBnvJ8maqON2o4g33GwW8t6HfdHp30+VW0ty9ayacx8EPrtb4APlun9yUMBMQj9Rr7bbHmZfgWwutf9VtY/H/hYy/yBORbG1NVuEHb6YGyY71uKHi5v+ujV87nl+XBZvm9j+1PJV87voHFXCPmK8Y/LslM7rOlA8m1ONwNfKx27K/mC7Z3kK/ujnRzAJ0o7twAHN/ZzQql5GHhLY/7B5FsL7wI+Tme3Jv4T+RaxW4HPkw+kvvQb+dbIB4Dfks/e3tqLfpqojTZqGyYH0ZryOLvb/uimzyerrWX5WsbemtjvftsW+ELZ543AYYPSb8DLyNeNbiKPU7+g1/1WakjkvBj9bB010Xa9fk9bH/46vyRVwN8AlaQKGOaSVAHDXJIqYJhLUgUMc0mqgGGugRQRT0XEmoi4NSIuKb9N2cn2F0bEzRFxckR8MCIOL/Pf2em+pNnAWxM1kCJiQ0ppxzJ9AfmXRv6tsXwobfpbIK3bPgv4Xkpp2TjL1pLv/314ZiqX+sMzc80G1wLLImJ5RFwbEZcBP4qIuRHx2Yi4pfzxtEPL+t8ClpQz+0Mi4ryIeH1EvIP8t2+ujoir+/VipJlgmGuglb/ncST5N+og/z2dlSml/cm/Kp5SSn9I/vOhn4uIucDrgLtSSgemlK4d3VdK6Szgp8ChKaVDkSpimGtQzYuI0f9l5ids+qt516eU7inTLyP/OjoppdvJ//nD/r0uVBoEQ1OvIvXFxpTSgc0Z5a+z/rI/5UiDzTNzzWbXAm8CiIj9gb3IfxhqMo+T/wswqSqGuWazTwLbRMQt5P80+PiU0q+n2OZc4JteAFVtvDVRkirgmbkkVcAwl6QKGOaSVAHDXJIqYJhLUgUMc0mqgGEuSRX4f6kxzYw1XaWsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compare mean imputation model with first model\n",
        "model2 = smf.ols('Profit~RD+Administration+Marketing', data = df3).fit()\n",
        "(model2.rsquared, model2.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9fvXg5twlHg",
        "outputId": "7d1e9150-e7f0-472d-d773-af3f3e91626a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9119537155310937, 0.9062115665439912)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model has not improved with mean imputation as well. It has got little worse than the medain imputation. \n",
        "\n",
        "The best thing we can do is to remove the outlier and check how the model performs."
      ],
      "metadata": {
        "id": "zTGTcyV7w-xe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4= df1.drop([49])"
      ],
      "metadata": {
        "id": "UEAExVnMwwSi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4"
      ],
      "metadata": {
        "id": "VtAdcSxEx0yo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df4.Profit)\n",
        "plt.title('Profit after removing outliers')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "f176KoD_zaP1",
        "outputId": "c1c22214-0101-401b-f275-bd186888e78d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Profit after removing outliers')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#compare outlier removed model with original model\n",
        "model3 =smf.ols('Profit~RD+Administration+Marketing', data = df4).fit()\n",
        "(model3.rsquared, model3.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruqthXjzyzRE",
        "outputId": "c259527a-2296-47f8-96f3-b49ce65d0803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9613162435129848, 0.9587373264138505)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see here, the model has imporved slightly after removing the outlier. We will continue with this model."
      ],
      "metadata": {
        "id": "ltkN8JkTziG9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4.State.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N2uZuW1fzS7e",
        "outputId": "ade4bfca-6f21-40f7-b9ae-1680ae023352"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "New York      17\n",
              "California    16\n",
              "Florida       16\n",
              "Name: State, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4.State.value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "TaZUeaQA1_OD",
        "outputId": "6cd3f2b9-a68d-4673-cee9-65394672a2b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4d4989090>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The categorical object type column, is quite balanced and is not afftecing a lot to our dependent vairable  - Profit.\n",
        "\n",
        "So we will drop this column for building our model."
      ],
      "metadata": {
        "id": "qzGg4q-c2G5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_f = df4.drop('State', axis=1)"
      ],
      "metadata": {
        "id": "Oa5hJOEU12Pl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_f.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iuwNWjt32jXx",
        "outputId": "0d73d87b-5d9f-4a35-cc6c-5a38e7cb0b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          RD  Administration  Marketing     Profit\n",
              "0  165349.20       136897.80  471784.10  192261.83\n",
              "1  162597.70       151377.59  443898.53  191792.06\n",
              "2  153441.51       101145.55  407934.54  191050.39\n",
              "3  144372.41       118671.85  383199.62  182901.99\n",
              "4  142107.34        91391.77  366168.42  166187.94"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RD</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing</th>\n",
              "      <th>Profit</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</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>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>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>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>166187.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab47e125-8a70-42be-82cf-5e5fe568a6b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-ab47e125-8a70-42be-82cf-5e5fe568a6b1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ab47e125-8a70-42be-82cf-5e5fe568a6b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " df_f.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RI-mdMAZ2laV",
        "outputId": "02a55790-8644-4a8c-f56e-14d0c6a806f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(49, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation coef\n",
        "df_f.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "rKn7IVp33qMt",
        "outputId": "fc7ac547-65c4-4875-8306-ada53a0fa4e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      RD  Administration  Marketing    Profit\n",
              "RD              1.000000        0.243438   0.711654  0.978437\n",
              "Administration  0.243438        1.000000  -0.037280  0.205841\n",
              "Marketing       0.711654       -0.037280   1.000000  0.739307\n",
              "Profit          0.978437        0.205841   0.739307  1.000000"
            ],
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              "      <th></th>\n",
              "      <th>RD</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RD</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.243438</td>\n",
              "      <td>0.711654</td>\n",
              "      <td>0.978437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Administration</th>\n",
              "      <td>0.243438</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.037280</td>\n",
              "      <td>0.205841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marketing</th>\n",
              "      <td>0.711654</td>\n",
              "      <td>-0.037280</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.739307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Profit</th>\n",
              "      <td>0.978437</td>\n",
              "      <td>0.205841</td>\n",
              "      <td>0.739307</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b94bb6d5-f9e5-4441-81cb-867b2a37c652')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b94bb6d5-f9e5-4441-81cb-867b2a37c652 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b94bb6d5-f9e5-4441-81cb-867b2a37c652');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df_f)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "PKVQOJQi4TAu",
        "outputId": "d76209d6-78df-4574-a06e-46513789bd59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fb4d4591350>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Building the model***"
      ],
      "metadata": {
        "id": "9WStMNnt26X7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ml_f = smf.ols('Profit~RD+Administration+Marketing', data = df_f).fit()"
      ],
      "metadata": {
        "id": "JI1lSiaZ2tTW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters\n",
        "ml_f.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwoLjYdu3Pye",
        "outputId": "0fcaaefc-f36d-4d9e-c75d-05fddf3d41fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept         52382.439251\n",
              "RD                    0.783002\n",
              "Administration       -0.022201\n",
              "Marketing             0.025234\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#t-values & p-values\n",
        "print(ml_f.tvalues, ' \\n', ml_f.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jMiXbux3UJG",
        "outputId": "80c09215-4cda-43ac-da2e-5df6a831080c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept          9.450782\n",
            "RD                20.469501\n",
            "Administration    -0.517889\n",
            "Marketing          1.825383\n",
            "dtype: float64  \n",
            " Intercept         2.960000e-12\n",
            "RD                1.971077e-24\n",
            "Administration    6.070728e-01\n",
            "Marketing         7.458443e-02\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As p values for Administration and Marketing are > 0.05. we will check for colinearity problem"
      ],
      "metadata": {
        "id": "DhVp8aXs5FJk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will perform simple linear regression on Administration then on Marketing. And then on both Administration and Marketing."
      ],
      "metadata": {
        "id": "sEzdNw705qoH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ml_a = smf.ols('Profit~Administration', data=df_f).fit()"
      ],
      "metadata": {
        "id": "F_KYR3KV3jaF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ml_a.tvalues, '\\n', ml_a.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PS9M5R0V6GTs",
        "outputId": "e3fde8ca-cb68-40a8-efc1-d46ea8971f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept         3.346236\n",
            "Administration    1.442053\n",
            "dtype: float64 \n",
            " Intercept         0.001618\n",
            "Administration    0.155918\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(ml_a.rsquared, ml_a.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNU1sBcZ6Qa0",
        "outputId": "94f1f14f-1d61-4516-c29c-6426d3751af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.04237035844418091, 0.02199525968767424)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ml_m = smf.ols('Profit~Marketing', data=df_f).fit()"
      ],
      "metadata": {
        "id": "sIUa4-yQ7do8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ml_m.tvalues, '\\n', ml_m.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKkVQuxE7kYw",
        "outputId": "bfa8bc52-a1ca-4131-ed9c-89747d3078c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept    8.427541\n",
            "Marketing    7.526983\n",
            "dtype: float64 \n",
            " Intercept    5.905020e-11\n",
            "Marketing    1.304853e-09\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(ml_m.rsquared, ml_m.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qVJFPIl7oPv",
        "outputId": "8e3879d3-619e-43c2-dc05-dde14abfa44a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.5465748398585546, 0.536927496025758)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ml_a_m = smf.ols('Profit~Administration+Marketing', data=df_f).fit()"
      ],
      "metadata": {
        "id": "9tCMZdtp7rr3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(ml_a_m.tvalues, '\\n', ml_a_m.pvalues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZ7_x_sp71pv",
        "outputId": "a0222ddb-6de6-48ee-8f50-e51ff0ed537f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept         1.474103\n",
            "Administration    2.508234\n",
            "Marketing         8.027366\n",
            "dtype: float64 \n",
            " Intercept         1.472650e-01\n",
            "Administration    1.572324e-02\n",
            "Marketing         2.695219e-10\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(ml_a_m.rsquared, ml_a_m.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63NIYXCq75uF",
        "outputId": "72981720-a818-4bd6-dd62-e6b5cc4b78ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6011271008483876, 0.583784800885274)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Let us try with VIF.***"
      ],
      "metadata": {
        "id": "Gda--jHv8WaS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#calculate VIF to detect colinearity\n",
        "rsq_rd = smf.ols('RD~Administration+Marketing', data = df_f).fit().rsquared\n",
        "vif_rd = 1/(1-rsq_rd)\n",
        "\n",
        "rsq_admin = smf.ols('Administration~RD+Marketing', data = df_f).fit().rsquared\n",
        "vif_admin = 1/(1-rsq_admin)\n",
        "\n",
        "rsq_mark = smf.ols('Marketing~RD+Administration', data = df_f).fit().rsquared\n",
        "vif_mark = 1/(1-rsq_mark)\n",
        "\n",
        "#Storing vif values in dataframe\n",
        "d1 = {'Variables':['RD', 'Administration', 'Marketing'], 'VIF':[vif_rd, vif_admin, vif_mark]}\n",
        "vif_frame = pd.DataFrame(d1)\n",
        "vif_frame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Jm6OTwss7_40",
        "outputId": "5a8c87da-3a1d-4422-98ad-51dd257a0f44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Variables       VIF\n",
              "0              RD  2.377754\n",
              "1  Administration  1.175172\n",
              "2       Marketing  2.239957"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<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>RD</td>\n",
              "      <td>2.377754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Administration</td>\n",
              "      <td>1.175172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Marketing</td>\n",
              "      <td>2.239957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10cc7e27-111b-4ead-ad9d-e7db31aa5075 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-10cc7e27-111b-4ead-ad9d-e7db31aa5075');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No variable has VIF>20. So no colinearity."
      ],
      "metadata": {
        "id": "IyJuGKik9xIL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Residual Analysis***"
      ],
      "metadata": {
        "id": "p4tX4rA9CbTG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test for Normality of Residuals - QQ plots"
      ],
      "metadata": {
        "id": "mWJP4wMRC-Aq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "TTCAo1-K9Ua7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#q line as the 45 degree line\n",
        "qqplot = sm.qqplot(ml_f.resid, line = 'q')\n",
        "plt.title('Normality of Residuals - Q-Q Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "n6oL9dIrDBJQ",
        "outputId": "cfd3e487-3ca3-403c-e852-fe2a19a102cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can conclude their no outliers."
      ],
      "metadata": {
        "id": "DTjKKZxcDYGS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Residual plot for Homoscedasticity"
      ],
      "metadata": {
        "id": "906spq0LDd11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get standardized values\n",
        "def get_standardized_values(vals):\n",
        "  return (vals - vals.mean())/vals.std()\n",
        "\n",
        "plt.scatter(get_standardized_values(ml_f.fittedvalues), get_standardized_values(ml_f.resid))\n",
        "plt.ylabel('Standardize Residual values')\n",
        "plt.xlabel('Standardize Fitted values')\n",
        "plt.title('Residaul Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "89PmB9zODJv4",
        "outputId": "d4414700-e01f-44dd-e878-8373def8b5e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Residual vs Regressors"
      ],
      "metadata": {
        "id": "5n_SCASCDroJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(ml_f, 'RD', fig=fig)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "ur26DtFRDoEf",
        "outputId": "6ff7aae4-4e55-4d1b-f99e-6129adc26b8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(ml_f, 'Administration', fig=fig)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "iK_XoOsPDyOQ",
        "outputId": "9fee48f4-381d-425f-91d4-974d668059b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,8))\n",
        "fig = sm.graphics.plot_regress_exog(ml_f, 'Marketing', fig=fig)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "GqvjqzTFD3Ao",
        "outputId": "6f6c80e1-6614-4e19-aad5-68595c909ff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "***Model Deletion***\n",
        "\n",
        "By using Cook's Distance and Leverage cut_off"
      ],
      "metadata": {
        "id": "Ln6VNx6XEFfc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_influence = ml_f.get_influence()\n",
        "(c, _) = model_influence.cooks_distance"
      ],
      "metadata": {
        "id": "WyBvyJNlD6dQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the influencers values using stem plot\n",
        "fig = plt.subplots(figsize=(20,8))\n",
        "plt.stem(np.arange(len(df_f)), np.round(c,3))\n",
        "plt.xlabel('Row Index')\n",
        "plt.ylabel('Cooks Distance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "a4UAgJMiENPw",
        "outputId": "dcae88e7-722a-498e-8874-d30195efe38b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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AAMBsW0qolO7em2RNdx/o7v8jycXjlgUAAADALFvKQN2fq6rTktxZVb+U5KNZYhgFAAAAwMq0lHDoRyfLXZ7ks0nOSvLSMYsCAAAAYLYtJVTa1t3/0N2f6e5/1d2vS/L9YxcGAAAAwOxaSqj0yiO0/diU6wAAAABgjhx1TKWqekWSH0pyblXtWDTry5N8cuzCAAAAAJhdxxqo+y9ycFDu05P820Xt/z3JXWMWBQAAAMBsO2qo1N1/m+Rvq+pFSfZ392NV9XVJvj7J3SerQAAAAABmz1LGVPqzJE+pqg1J/igH7wb3W2MWBQAAAMBsW0qoVN39uSQvTfLr3f3yJM9aysar6uKq2lNVe6vqyiPMf11V3VtVd1XVn1TV2YvmvbKqPjj5OtJg4QAAAAAskyWFSlX1/CQ/nORdk7Y1S1hpTZJrk3xPkvOTvKKqzj9ssd1JtnT3NyW5KckvTdZ9epI3JnlekguTvLGqnraEWgEAAAA4CZYSKr02yVVJ3tnd91TVM5O8ZwnrXZhkb3ff190PJ7kxySWLF+ju90yugkqS9yU5c/J4a5J3d/cnu/tTSd6d5OIl/EwAAAAAToJj3f0tSdLd703y3kXT9yX56SVse0OSjyyavj8Hrzw6mh9P8ofHWHfDEn4mAAAAACfBUUOlqvrV7n5tVf1Bkj58fne/ZFpFVNWPJNmS5DsGrndZksuSZOPGjdMqBwAAAIAncKwrlX5n8v3fHOe2F5KctWj6zEnb41TVi5L8XJLv6O6HFq37nYet+6eHr9vd1yW5Lkm2bNnyRcEXAAAAAOM4aqjU3XdMvr+3qtZPHj84YNu3Jzmvqs7NwZDo0iQ/tHiBqtqc5K1JLu7ujy2atTPJv140OPd35+C4TgAAAADMgGMO1F1VP19VH0+yJ8n/V1UPVtUblrLh7n40yeU5GBB9IMk7JgN9X11Vh7rOXZPkS5P8blXdWVU7Jut+Mskv5GAwdXuSqydtAAAAAMyAY42p9LokFyV5bnf/9aTtmUn+t6r6X7r7V55o4919S5JbDmt7w6LHLzrGutcnuf4JfwMAAAAATrpjXan0o0lecShQSj5/57cfSfLPxi4MAAAAgNl1rFDp1O7++OGNk3GVTh2vJAAAAABm3bFCpYePcx4AAAAAK9xRx1RK8s1V9ZkjtFeSp4xUDwAAAABz4KihUnevOZmFAAAAADA/jtX9DQAAAACOSKgEAAAAwGBCJQAAAAAGe8JQqaqeWlVPmjz+uqp6SVWdOn5pAAAAAMyqpVyp9GdJnlJVG5L8UZIfTfJbYxYFAAAAwGxbSqhU3f25JC9N8uvd/fIkzxq3LAAAAABm2ZJCpap6fpIfTvKuSdua8UoCAAAAYNYtJVR6TZKrkryzu++pqmcmec+4ZQEAAAAwy05ZwjIf6e6XHJro7vuq6ndGrAkAAACAGbeUK5VumgzSnSSpqu9Icv14JQEAAAAw65YSKv1Uku1V9Y+q6nuT/Lsk3ztuWQAAAADMsifs/tbdt1fVTyf5oyT/kORF3f3g6JUBAAAAMLOOGipV1R8k6UVNX5Lk00l+s6qyeJwlAAAAAFaXY12p9G9OWhUAAAAAzJWjhkrd/d5Dj6vqGUmeO5m8rbs/NnZhAAAAAMyuJxyou6p+IMltSV6e5AeSvL+qXjZ2YQAAAADMriccqDvJzyV57qGrk6pqfZI/TnLTmIUBAAAAMLue8EqlJE86rLvbJ5a4HgAAAAAr1FKuVPp/qmpnkhsm0z+Y5JbxSgIAAABg1j1hqNTdV1TVS5O8YNJ0XXe/c9yyAAAAAJhlS7lSKUn+PMkjSToHB+0GAAAAYBUbcve3l8Xd3wAAAACIu78BAAAAcBzc/Q0AAACAwY737m9/OF5JAAAAAMw6d38DAAAAYLCjhkpV9bVJntHdf97dNye5edL+gqr6mu7+0MkqEgAAAIDZcqyxkX41yWeO0P7pyTwAAAAAVqljhUrP6O67D2+ctJ0zWkUAAAAAzLxjhUrrjjFv7bQLAQAAAGB+HCtU2lVVP3l4Y1X9RJI7xisJAAAAgFl3rLu/vTbJO6vqh/OFEGlLktOS/NOxCwMAAABgdh01VOruv0vyj6vqnyT5xknzu7r71pNSGQAAAAAz61hXKiVJuvs9Sd5zEmoBAAAAYE4ca0wlAAAAADgioRIAAAAAg40aKlXVxVW1p6r2VtWVR5j/7VX1l1X1aFW97LB5B6rqzsnXjjHrBAAAAGCYJxxT6XhV1Zok1yZ5cZL7k9xeVTu6+95Fi304yY8l+edH2MT+7r5grPoAAAAAOH6jhUpJLkyyt7vvS5KqujHJJUk+Hyp1999M5j02Yh0AAAAATNmY3d82JPnIoun7J21L9ZSq2lVV76uqbdMtDQAAAIATMeaVSifq7O5eqKpnJrm1qu7u7g8tXqCqLktyWZJs3LhxOWoEAAAAWJXGvFJpIclZi6bPnLQtSXcvTL7fl+RPk2w+wjLXdfeW7t6yfv36E6sWAAAAgCUbM1S6Pcl5VXVuVZ2W5NIkS7qLW1U9raqePHl8epKLsmgsJgAAAACW12ihUnc/muTyJDuTfCDJO7r7nqq6uqpekiRV9dyquj/Jy5O8tarumaz+DUl2VdV/TfKeJG857K5xAAAAACyjUcdU6u5bktxyWNsbFj2+PQe7xR2+3l8kefaYtQEAAABw/Mbs/gYAAADACiVUAgAAAGAwoRIAAAAAg406phIAAADAidq+eyHX7NyTB/btzxnr1uaKrZuybfOG5S5r1RMqAQAAADNr++6FXHXz3dn/yIEkycK+/bnq5ruTRLC0zHR/AwAAAGbWNTv3fD5QOmT/Iwdyzc49y1QRhwiVAAAAgJn1wL79g9o5eYRKAAAAwMw6Y93aQe2cPEIlAAAAYGZdsXVT1p665nFta09dkyu2blqmijjEQN0AAADAzDo0GPfrb7orDx94LBvc/W1mCJUAAACAmbZt84bccNuHkyRvf9Xzl7kaDtH9DQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAAABgMKESAAAAAIMJlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYbNVSqqourak9V7a2qK48w/9ur6i+r6tGqetlh815ZVR+cfL1yzDoBAAAAGGa0UKmq1iS5Nsn3JDk/ySuq6vzDFvtwkh9L8rbD1n16kjcmeV6SC5O8saqeNlatAAAAAAxzyojbvjDJ3u6+L0mq6sYklyS599AC3f03k3mPHbbu1iTv7u5PTua/O8nFSW4YsV4AAADgBG3fvZBrdu7JA/v254x1a3PF1k3ZtnnDcpfFCMbs/rYhyUcWTd8/aZvaulV1WVXtqqpdDz744HEXCgAAAJy47bsXctXNd2dh3/50koV9+3PVzXdn++6F5S6NEcz1QN3dfV13b+nuLevXr1/ucgAAAGBVu2bnnux/5MDj2vY/ciDX7NyzTBUxpjFDpYUkZy2aPnPSNva6AAAAwDJ4YN/+Qe3MtzFDpduTnFdV51bVaUkuTbJjievuTPLdVfW0yQDd3z1pAwAAAGbUGevWDmpnvo0WKnX3o0kuz8Ew6ANJ3tHd91TV1VX1kiSpqudW1f1JXp7krVV1z2TdTyb5hRwMpm5PcvWhQbsBAACA2XTF1k1Ze+qax7WtPXVNrti6aZkqYkxj3v0t3X1LklsOa3vDose352DXtiOte32S68esDwAAAJieQ3d5e/1Nd+XhA49lg7u/rWijhkoAAADA6rJt84bccNuHkyRvf9Xzl7kaxjTXd38DAAAAYHkIlQAAAAAYTKgEAAAAwGBCJQAAAAAGEyoBAAAAMJhQCQAAAIDBhEoAAAAADHbKchcAwOzbvnsh1+zckwf27c8Z69bmiq2bsm3zhuUuCwAAWEZCJQCOafvuhVx1893Z/8iBJMnCvv256ua7k0SwBAAAq5jubwAc0zU793w+UDpk/yMHcs3OPctUEQAAMAuESgAc0wP79g9qBwAAVgehEgDHdMa6tYPaAQCA1UGoBMAxXbF1U9aeuuZxbWtPXZMrtm5apooAAIBZYKBuAI7p0GDcr7/prjx84LFscPc3AAAgQiUAlmDb5g254bYPJ0ne/qrnL3M1AADALND9DQAAAIDBhEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYLBTlrsA5s/23Qu5ZueePLBvf85YtzZXbN2UbZs3LHdZAAAAwEkkVGKQ7bsXctXNd2f/IweSJAv79ueqm+9OEsESAAAcxglZYCXT/Y1Brtm55/OB0iH7HzmQa3buWaaKAABgNh06Ibuwb386Xzghu333wnKXBjAVQiUGeWDf/kHtAACwWjkhC6x0QiUGOWPd2kHtAACwWjkhC6x0QiUGuWLrpqw9dc3j2taeuiZXbN20TBUBAMBsckIWWOmESgyybfOGvPmlz85paw4+dTasW5s3v/TZBhsEAIDDzNMJ2e27F3LRW27NuVe+Kxe95VbjPgFL4u5vDLZt84bccNuHkyRvf9Xzl7kaAACYTYdOvL7+prvy8IHHsmFG7/7mDs/A8RIqAQAAjGQeTsgea0BxoRJwLLq/AQAArGIGFAeOl1AJAABgFTOgOHC8hEoAAACr2DwNKA7MFmMqAQAArGLzMqA4MHuESgAAAKvcPAwoDsyeUbu/VdXFVbWnqvZW1ZVHmP/kqnr7ZP77q+qcSfs5VbW/qu6cfP3GmHUCAAAAMMxoVypV1Zok1yZ5cZL7k9xeVTu6+95Fi/14kk9199dW1aVJfjHJD07mfai7LxirPgAAAACO35hXKl2YZG9339fdDye5Mcklhy1zSZLfnjy+Kcl3VVWNWBMAAAAAUzBmqLQhyUcWTd8/aTviMt39aJJPJ/nKybxzq2p3Vb23qr5txDoBAAAAGGhWB+r+aJKN3f2JqnpOku1V9azu/sziharqsiSXJcnGjRuXoUwAjtf23Qu5ZueePLBvf85wlxlYFRz3ALCyjHml0kKSsxZNnzlpO+IyVXVKkq9I8onufqi7P5Ek3X1Hkg8l+brDf0B3X9fdW7p7y/r160f4FQAYw/bdC7nq5ruzsG9/OsnCvv256ua7s3334S8TwErhuAeAlWfMUOn2JOdV1blVdVqSS5PsOGyZHUleOXn8siS3dndX1frJQN+pqmcmOS/JfSPWCsBJdM3OPdn/yIHHte1/5ECu2blnmSoCxua4B4CVZ7Tub939aFVdnmRnkjVJru/ue6rq6iS7untHkt9M8jtVtTfJJ3MweEqSb09ydVU9kuSxJD/V3Z8cq1YATq4H9u0f1A7MP8c9AKw8o46p1N23JLnlsLY3LHr8D0lefoT1fi/J741ZGwDL54x1a7NwhA+SZ6xbuwzVrBzGq2GWOe4BYOUZs/sbABzRFVs3Ze2pax7XtvbUNbli66Zlqmj+Ga+GWee4B4CVR6gEwEm3bfOGvPmlz85paw6+DG1YtzZvfumzXVVzAoxXw6xz3APAyjNq9zcYQrcNWF22bd6QG277cJLk7a96/jJXM/+MV8M8cNwDwMriSiVmgm4bACfmaOPSGK8GAICxCJWYCbptAJwY49UAAHCy6f7GTNBtA+DEHOou/Pqb7srDBx7LBt2IAQAYmVCJmeA2wwAnzng1AACcTLq/MRN02wAAAID54kolZoJuGwAAADBfhErMDN02AAAAYH7o/gYAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDnbLcBQAAAAAsh+27F3LNzj15YN/+nLFuba7YuinbNm9Y7rLmhlAJYIm84AAAwMqxffdCrrr57ux/5ECSZGHf/lx1891J4n3+Eun+BrAEh3FHPvAAAA+6SURBVF5wFvbtT+cLLzjbdy8sd2kAAMBxuGbnns8HSofsf+RArtm5Z5kqmj9CJYAl8IIDAAArywP79g9q54vp/gasSNPuquYFBwAAVpYz1q3NwhHez5+xbu0yVDOfXKkErDhjdFU72guLFxwAWB7bdy/korfcmnOvfFcuesutuqQDg12xdVPWnrrmcW1rT12TK7ZuWqaK5o9QCVhxxuiq5gUHpscHQeBEGesQmIZtmzfkzS99dk5bczAa2bBubd780mcbpHsA3d+AFWeMrmqHXlhef9NdefjAY9ng7m9wXNxlBZiGY51A8r+ElczdiKdv2+YNueG2DydJ3v6q5y9zNfNHqASsOGP1jfaCAyfOB0FgGox1yGrkxAyzSPc3YMWZp65qugGx2vggCEyDsQ5ZjdyNmFkkVAJWnHnpG208CFYjHwSBaZinE0gwLU7MMIuESsCKtG3zhmzeuC7PO/fp+fMrXzhzgVLibBOrkw+CwDTMywkkmCYnZphFxlRiRTOQHbPM2SZWI4PeA9NirENWmyu2bnrcmEqJEzMsP6HSCreaQ5WxBrJbzfuU6RprQHGYdT4IAsBwTswwi4RKK9hqvzvAGHcYWu37lOlytmn6hL4AwErmxAyzxphKK9hqH69ljK5Fq32fMl3Gg5guA58DAMDJ5UqlFWy1j9cyRtei1b5PmT5nm6ZnjKsTYdpcTQcArCSuVFrBVvvdAca4w9Bq36cwy4S+zDpX0wEAK41QaQVb7bdtHqNr0WrfpzDLhL7MunnrQr1990IuesutOffKd+Wit9wq/AIAvojubyuYuwNMv2uRfQqzy8DnzLp5uprOjSlYrXRRBRhGqLTCGa9l+uzT6fLmbfpW6z4dK/Rdrftz3szD32mMsf7GYowyViNh6vTNw/9m4MQIlWCFmocXcW/epm+179Nph76rfX/Oi3n5O83T1XTzdFUVTIswdbrm5X8zcGKMqQQzYNrjVszLYLDzNr7IPLBPp2vM/Wm8mumZl+f9GGP9HTLt55Mxyla31fr/SZg6XfPyvxk4Ma5UmiHzcGUJ0zfGWZx5OdPmzdv02afTNdb+dPZ2uubpeT9GF+oxnk/zdFUV07Wa/z/NUxfVeTBP/5tXO59DORGjXqlUVRdX1Z6q2ltVVx5h/pOr6u2T+e+vqnMWzbtq0r6nqraOWecsmJcrS5i+Mc7izMuLuDPh02efTtdY+9PZ2+ka6+80L1drjPF8GvOqqnkwL3/7Meoc6//TPOzTebrL7zzszzHfk4zx+8/DPh2Dz6GcqNFCpapak+TaJN+T5Pwkr6iq8w9b7MeTfKq7vzbJryT5xcm65ye5NMmzklyc5Ncn21uxfMBYvcYIgOblA9Y8vXmbF/bpdI21P+cl+E3m4032GH+neXqTPdbzadvmDdm8cV2ed+7T8+dXvnBmA6V56UI+L3WO8Xyal+NpXsLUedmfY72GjvH7z8txPwafQzlRY16pdGGSvd19X3c/nOTGJJcctswlSX578vimJN9VVTVpv7G7H+ruv06yd7K9FWuePmAwXWMEQPPyAWte3rzNE/t0usban/NyRdm8fHAZ4+80T2+y5+X5NIYxnqNj/O3npc5knOfTPB1P8xCmzsv+HOs1dIzff16O+zH4HMqJqu4eZ8NVL0tycXf/xGT6R5M8r7svX7TMX02WuX8y/aEkz0vy80ne193/56T9N5P8YXffdLSft2XLlt61a9cov8vJcNFbbs3Cvv151V2/n2d++gv/aJ58ypps3rjuhLZ970c/kyQ5/6u//IS2M/Y2x9rurG/z43//UO77+Gfz2GNfOBaf9KTKM09/ak7/0ief0HY/9OBn09158ilrctbT157Q9nZ/eF8eevTAF7XP6nN0rO3apm1Ow1jHfTLdWuftuJ/mNt933yeOOu9bn/mVJ7Tt1fp8GmObYzxHx/jbz0udyTjPp3k6nuZhm6t9f47x+8/LcX/IrP8fPWQenk9jbvfej34m/239Wfkfb/j3U9vmcqmqO7p7y5HmzfVA3VV1WZLLkmTjxo3LXM2JOTQg5mJPelLlrKef+FnGLzlt+j0Hx9jmWNud9W0eeoP21x//bA48Np0A6NB2//6hR5Mk53zlU0+4ziO92ByrfQjPJ9tcbdsc67hPplvrvB3309zmk09Zc9Q32SdqtT6fxtjmGM/RMf7281JnMs7zaZ6Op3nY5mrfn2P8/vNy3B8yzX161tPXHjFI9jl0Ott8+lNP/LV41o0ZKi0kOWvR9JmTtiMtc39VnZLkK5J8YonrpruvS3JdcvBKpalVvgwOXQZ6zVNPe9yo+8+ZwiW3Z5/wFk7ONsfa7jxs8+wkz5nyNg9td1p+aHI13eE2rFubH7zyhSe0bc8n21yt23TcT9c0t7l790J+/gh3P3vzS5+ds0/wtXm1Pp/G2OYYz9Ex/vbzUuch034+zePxNMvbXO37c4zff16O+0OmuU/PTvKRI9z9zefQ2dzmLBozVLo9yXlVdW4OBkKXJvmhw5bZkeSVSf5LkpclubW7u6p2JHlbVf1ykjOSnJfkthFrnQnbNm+YyX7bkLi9NKxGq/m4//zJHrdYnmljPEfH+NvPS51jmada58Fq359j/P7zctyPxedQTsRoYyolSVV9b5JfTbImyfXd/aaqujrJru7eUVVPSfI7STYn+WSSS7v7vsm6P5fkf0ryaJLXdvcfHutnzfuYSjAPth/hLIYXIFjZHPfMunl5js5LncD0OO5ZKY41ptKoodLJJFQCAAAAmK5jhUpPOtnFAAAAADD/hEoAAAAADCZUAgAAAGAwoRIAAAAAgwmVAAAAABhMqAQAAADAYEIlAAAAAAYTKgEAAAAwmFAJAAAAgMGESgAAAAAMJlQCAAAAYDChEgAAAACDCZUAAAAAGEyoBAAAAMBgQiUAAAAABqvuXu4apqKqHkzyt8tdx5ScnuTjy10ErBCOJ5gOxxJMh2MJpsfxBNPxRMfS2d29/kgzVkyotJJU1a7u3rLcdcBK4HiC6XAswXQ4lmB6HE8wHSdyLOn+BgAAAMBgQiUAAAAABhMqzabrlrsAWEEcTzAdjiWYDscSTI/jCabjuI8lYyoBAAAAMJgrlQAAAAAYTKg0Y6rq4qraU1V7q+rK5a4H5kVVXV9VH6uqv1rU9vSqendVfXDy/WnLWSPMg6o6q6reU1X3VtU9VfWaSbvjCQaqqqdU1W1V9V8nx9O/mrSfW1Xvn7zfe3tVnbbctcI8qKo1VbW7qv7vybRjCQaqqr+pqrur6s6q2jVpO+73eUKlGVJVa5Jcm+R7kpyf5BVVdf7yVgVz47eSXHxY25VJ/qS7z0vyJ5Np4NgeTfIz3X1+km9N8urJa5HjCYZ7KMkLu/ubk1yQ5OKq+tYkv5jkV7r7a5N8KsmPL2ONME9ek+QDi6YdS3B8/kl3X9DdWybTx/0+T6g0Wy5Msre77+vuh5PcmOSSZa4J5kJ3/1mSTx7WfEmS3548/u0k205qUTCHuvuj3f2Xk8f/PQffvG+I4wkG64P+fjJ56uSrk7wwyU2TdscTLEFVnZnk+5L875PpimMJpuW43+cJlWbLhiQfWTR9/6QNOD7P6O6PTh7/tyTPWM5iYN5U1TlJNid5fxxPcFwm3XXuTPKxJO9O8qEk+7r70cki3u/B0vxqktcneWwy/ZVxLMHx6CR/VFV3VNVlk7bjfp93yrSrA5hF3d1V5XaXsERV9aVJfi/Ja7v7MwdPCB/keIKl6+4DSS6oqnVJ3pnk65e5JJg7VfX9ST7W3XdU1Xcudz0w517Q3QtV9VVJ3l1V/+/imUPf57lSabYsJDlr0fSZkzbg+PxdVX11kky+f2yZ64G5UFWn5mCg9H91982TZscTnIDu3pfkPUmen2RdVR06uev9Hjyxi5K8pKr+JgeHCHlhkv81jiUYrLsXJt8/loMnOy7MCbzPEyrNltuTnDe5i8FpSS5NsmOZa4J5tiPJKyePX5nk95exFpgLkzEqfjPJB7r7lxfNcjzBQFW1fnKFUqpqbZIX5+A4Ze9J8rLJYo4neALdfVV3n9nd5+TgZ6Rbu/uH41iCQarqqVX1ZYceJ/nuJH+VE3ifV92uXp8lVfW9OdhfeE2S67v7TctcEsyFqrohyXcmOT3J3yV5Y5LtSd6RZGOSv03yA919+GDewCJV9YIk/ynJ3fnCuBX/IgfHVXI8wQBV9U05OODpmhw8mfuO7r66qp6Zg1dbPD3J7iQ/0t0PLV+lMD8m3d/+eXd/v2MJhpkcM++cTJ6S5G3d/aaq+soc5/s8oRIAAAAAg+n+BgAAAMBgQiUAAAAABhMqAQAAADCYUAkAAACAwYRKAAAAAAwmVAIAOExVHaiqO6vqr6rqD6pq3RS3/WNV9WsD1/mtqnrZtGoAAJgGoRIAwBfb390XdPc3Jvlkklcvd0EAALNGqAQAcGz/JcmGJKmqC6rqfVV1V1W9s6qeVlVfVVV3TOZ/c1V1VW2cTH+oqr7kaBueXIH076rqL6rqvkNXI9VBv1ZVe6rqj5N81aJ1nlNV762qO6pqZ1V9dVV9xWTZTZNlbqiqnxxvlwAACJUAAI6qqtYk+a4kOyZN/zHJz3b3NyW5O8kbu/tjSZ5SVV+e5NuS7ErybVV1dpKPdffnnuDHfHWSFyT5/iRvmbT90ySbkpyf5J8l+ceTek5N8u+TvKy7n5Pk+iRv6u5PJ7k8yW9V1aVJntbd/+GEdwAAwDGcstwFAADMoLVVdWcOXqH0gSTvrqqvSLKuu987Wea3k/zu5PFfJLkoybcn+ddJLk5SSf7TEn7W9u5+LMm9VfWMSdu3J7mhuw8keaCqbp20b0ryjZN6kmRNko8mSXe/u6penuTaJN98fL82AMDSuVIJAOCL7e/uC5KcnYPh0BONqfRnOXiV0tlJfj8HQ50XZGmh0kOLHtcTLFtJ7pmM93RBdz+7u787SarqSUm+IcnnkjxtCT8XAOCECJUAAI5i0nXtp5P8TJLPJv9/O3eQslMYhnH8f8cWDJQdmCsDA4Y2ICIpGzBXtiAjM0uwh9c3VwZKSrIA38BM6jF4jymdtyT1+w3Pc07PGV/d1935zNzYjh9Uv6aW3lT3q4/b1NHX6nZ1duLVh+rOzFyYmcvVze35h+rSzFyvYx1uZq5uZ086TlXdq15tVTkAgL9G/Q0A4DfWWm9n5l11t3pYvdyWb3+qHm3vfJ5jH+2wfXZWXVlrnZ947evqVvW++tJxWXhrre/bMu8XWx3vYvV8Zn5Uj6tra61vM3OonlbPTrwfAOCPZq31r/8BAAAAgP+M+hsAAAAAuwmVAAAAANhNqAQAAADAbkIlAAAAAHYTKgEAAACwm1AJAAAAgN2ESgAAAADsJlQCAAAAYLefGdSukHfe2eUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#index and values of influencers where c is more than .5\n",
        "(np.argmax(c), np.max(c))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oY75RPQETDw",
        "outputId": "963c1a09-7d28-4836-bc7c-317fa51e8259"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48, 0.39274420556321504)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the c is not more than 1. We can conclude there are no outliers or influencers."
      ],
      "metadata": {
        "id": "NwATpozcEYUZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "High Influence Points"
      ],
      "metadata": {
        "id": "_AewoERWEgPT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.regressionplots import influence_plot"
      ],
      "metadata": {
        "id": "MmsZMM8kEXN0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "influence_plot(ml_f)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "DEKuTWGwEifV",
        "outputId": "6c5e7c53-b220-4f2a-e5f3-b969428689af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = df_f.shape[1]\n",
        "n = df_f.shape[0]\n",
        "leverage_cutoff = 3*(k+1)/n"
      ],
      "metadata": {
        "id": "oBiKetSFElnV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "leverage_cutoff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbQkgWseEpBE",
        "outputId": "4e38fca1-e04b-46cb-dde9-6a3f162a5155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.30612244897959184"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see in plot no points are above leverage cutoof value 0.306, we can conclude no influencers. \n",
        "\n",
        "So no deletion of datapoints required."
      ],
      "metadata": {
        "id": "ZPD6rJ0MEvb1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Predict Model***"
      ],
      "metadata": {
        "id": "sMI2ta74FGzO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#dataset3['research'].describe()\n",
        "#dataset3['Admin'].describe()\n",
        "#dataset3['Marketing'].describe()\n",
        "df_f.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "uPXeeNPCEuTo",
        "outputId": "f9dd0e82-523d-4e02-e11b-1ff21120ed52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  RD  Administration      Marketing         Profit\n",
              "count      49.000000       49.000000      49.000000      49.000000\n",
              "mean    75226.138367   121433.636327  214409.833265  113998.991020\n",
              "std     45115.141560    28301.008988  121168.170072   38171.246893\n",
              "min         0.000000    51283.140000       0.000000   35673.410000\n",
              "25%     44069.950000   103057.490000  134050.070000   90708.190000\n",
              "50%     73994.560000   122782.750000  214634.810000  108552.040000\n",
              "75%    101913.080000   145077.580000  299737.290000  141585.520000\n",
              "max    165349.200000   182645.560000  471784.100000  192261.830000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef603a39-fb5c-4c7f-ada8-154c1c155f50\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>RD</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75226.138367</td>\n",
              "      <td>121433.636327</td>\n",
              "      <td>214409.833265</td>\n",
              "      <td>113998.991020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>45115.141560</td>\n",
              "      <td>28301.008988</td>\n",
              "      <td>121168.170072</td>\n",
              "      <td>38171.246893</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>35673.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>44069.950000</td>\n",
              "      <td>103057.490000</td>\n",
              "      <td>134050.070000</td>\n",
              "      <td>90708.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>73994.560000</td>\n",
              "      <td>122782.750000</td>\n",
              "      <td>214634.810000</td>\n",
              "      <td>108552.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>101913.080000</td>\n",
              "      <td>145077.580000</td>\n",
              "      <td>299737.290000</td>\n",
              "      <td>141585.520000</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef603a39-fb5c-4c7f-ada8-154c1c155f50')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ef603a39-fb5c-4c7f-ada8-154c1c155f50 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ef603a39-fb5c-4c7f-ada8-154c1c155f50');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#New data for prediction\n",
        "new_data=pd.DataFrame({'RD':4000,\"Administration\":70000,\"Marketing\":20000},index=[0])"
      ],
      "metadata": {
        "id": "Wp2wSpSdFRQJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "XrV2uPd9FvYr",
        "outputId": "ff56d814-03b5-4334-b502-4bdb92b2fc97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     RD  Administration  Marketing\n",
              "0  4000           70000      20000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8578aee-c38d-47e1-af9e-1fbeaf34e5e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>RD</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4000</td>\n",
              "      <td>70000</td>\n",
              "      <td>20000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8578aee-c38d-47e1-af9e-1fbeaf34e5e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8578aee-c38d-47e1-af9e-1fbeaf34e5e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e8578aee-c38d-47e1-af9e-1fbeaf34e5e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ml_f.predict(new_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwUger_xFbmJ",
        "outputId": "ec724d99-5ee3-42cf-cf5a-578def7654fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    54465.02362\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AhuSXSZJFo_R"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}