ML-Notebooks/Attrition_Rate.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import datasets\n",
        "from sklearn.metrics import accuracy_score\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attrdata = pd.read_csv(\"Table_1.csv\")"
      ],
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        "id": "YaIKggJl1Oiz"
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      "execution_count": 2,
      "outputs": []
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        "attrdata.head()"
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              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "0         1  sid    9876544345       Pune         B2  Operation    Male   \n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM) Marital Status  Age in YY.  \\\n",
              "0    0.00        < =1                6.08         Single       27.12   \n",
              "1    0.00        < =1               13.00          Marr.       38.08   \n",
              "2    0.01        < =1               16.05          Marr.       36.04   \n",
              "3    0.01        < =1                6.06          Marr.       32.07   \n",
              "4    0.00        < =1                7.00          Marr.       32.05   \n",
              "\n",
              "  Hiring Source Promoted/Non Promoted Job Role Match Stay/Left  \n",
              "0        Direct          Non Promoted            Yes      Left  \n",
              "1        Direct              Promoted             No      Stay  \n",
              "2        Direct              Promoted            Yes      Stay  \n",
              "3        Direct              Promoted            Yes      Stay  \n",
              "4        Direct          Non Promoted            Yes      Stay  "
            ],
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              "      <th></th>\n",
              "      <th>table id</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Pune</td>\n",
              "      <td>B2</td>\n",
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              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>6.08</td>\n",
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              "      <td>Direct</td>\n",
              "      <td>Non Promoted</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B7</td>\n",
              "      <td>Support</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>13.00</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>38.08</td>\n",
              "      <td>Direct</td>\n",
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              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>16.05</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>36.04</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>6.06</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>32.07</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Lucknow</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>7.00</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>32.05</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Non Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
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        "attrdata.drop(0,inplace=True)"
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "attrdata.isnull().sum()"
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      "execution_count": 5,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "table id                 0\n",
              "name                     0\n",
              "phone number             0\n",
              "Location                 0\n",
              "Emp. Group               0\n",
              "Function                 0\n",
              "Gender                   0\n",
              "Tenure                   0\n",
              "Tenure Grp.              0\n",
              "Experience (YY.MM)       4\n",
              "Marital Status           0\n",
              "Age in YY.               0\n",
              "Hiring Source            0\n",
              "Promoted/Non Promoted    0\n",
              "Job Role Match           2\n",
              "Stay/Left                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
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      ],
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          "data": {
            "text/plain": [
              "(895, 16)"
            ]
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          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender_dict = attrdata[\"Gender \"].value_counts()\n",
        "gender_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GevxKgu52eWo",
        "outputId": "d70a8978-abaf-4bb9-fa5f-1a31bc5e04df"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      655\n",
              "Female    234\n",
              "other       6\n",
              "Name: Gender , dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attrdata['Gender '].value_counts().plot(kind='bar',color=['salmon','lightblue'],title=\"Count of different gender\")"
      ],
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde0e52f400>"
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a plot for crosstab\n",
        "\n",
        "pd.crosstab(attrdata['Gender '],attrdata['Stay/Left']).plot(kind=\"bar\",figsize=(10,6))\n",
        "plt.title(\"Stay/Left vs Gender\")\n",
        "plt.xlabel(\"Stay/Left\")\n",
        "plt.ylabel(\"No of people who left based on gender\")\n",
        "plt.legend([\"Left\",\"Stay\"])\n",
        "plt.xticks(rotation=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "Fl6ahEFJ9vaB",
        "outputId": "bd68234a-bc1f-4aef-889f-8e88b4e8d908"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2]), <a list of 3 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "promoted_dict = attrdata[\"Promoted/Non Promoted\"].value_counts()\n",
        "promoted_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ey-hnrz9yVX",
        "outputId": "daffa230-3a44-4873-bbbe-b17ea45ba4c1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Promoted        457\n",
              "Non Promoted    438\n",
              "Name: Promoted/Non Promoted, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attrdata['Promoted/Non Promoted'].value_counts().plot(kind='bar',color=['salmon','lightblue'],title=\"Promoted and Non Promoted\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "73DNH5_V91I3",
        "outputId": "d0b315ca-8f7e-4552-c0a6-c96a71222f21"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde0df66310>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": [
        "#Create a plot for crosstab\n",
        "\n",
        "pd.crosstab(attrdata['Promoted/Non Promoted'],attrdata['Stay/Left']).plot(kind=\"bar\",figsize=(10,6))\n",
        "plt.title(\"Stay/Left vs Promoted/Non Promoted\")\n",
        "plt.xlabel(\"Stay/Left\")\n",
        "plt.ylabel(\"No. of people who left/stay based on promotion\")\n",
        "plt.legend([\"Left\",\"Stay\"])\n",
        "plt.xticks(rotation=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "Mz5hp3M-94In",
        "outputId": "6c0715f2-37c4-4f98-d29b-1ab6f7deae5e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "func_dict = attrdata[\"Function\"].value_counts()\n",
        "func_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70PzcZS097M4",
        "outputId": "3aa1a191-731c-4bb8-e0b5-8d03e0840ee6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Operation    831\n",
              "Support       52\n",
              "Sales         12\n",
              "Name: Function, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attrdata['Function'].value_counts().plot(kind='bar',color=['salmon','lightblue'],title=\"Functions in organization\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "36OQXQBZ9_NB",
        "outputId": "ec1a67f0-fed2-4de0-fbfd-b0cd424a94eb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde0df1f3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": [
        "#Create a plot for crosstab\n",
        "\n",
        "pd.crosstab(attrdata['Function'],attrdata['Stay/Left']).plot(kind=\"bar\",figsize=(10,6))\n",
        "plt.title(\"Stay/Left vs Function\")\n",
        "plt.xlabel(\"Stay/Left\")\n",
        "plt.ylabel(\"No. of people who left/stay based on function of organization\")\n",
        "plt.legend([\"Left\",\"Stay\"])\n",
        "plt.xticks(rotation=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "kPsmbc03-BqJ",
        "outputId": "f572b104-6b26-435f-8120-f503f872456b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2]), <a list of 3 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Hiring_dict = attrdata[\"Hiring Source\"].value_counts()\n",
        "Hiring_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnWlI1OO-GAZ",
        "outputId": "ce2a62df-666b-4d05-ba4b-f8c68e510917"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Direct               708\n",
              "Agency               116\n",
              "Employee Referral     71\n",
              "Name: Hiring Source, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Marital_dict = attrdata[\"Marital Status\"].value_counts()\n",
        "print(Marital_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKgGFMuz-JGx",
        "outputId": "fdbfe000-ae09-4007-d830-075a751a433b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Single    533\n",
            "Marr.     356\n",
            "Div.        2\n",
            "NTBD        2\n",
            "Sep.        2\n",
            "Name: Marital Status, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Emp_dict = attrdata[\"Emp. Group\"].value_counts()\n",
        "Emp_dict['other group'] = 1\n",
        "print(Emp_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJUsvZ_F-Lzj",
        "outputId": "25876503-0c1b-4c6c-aed9-2e541e5084ca"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "B1             537\n",
            "B2             275\n",
            "B3              59\n",
            "B0               8\n",
            "B4               7\n",
            "B5               4\n",
            "B7               2\n",
            "B6               1\n",
            "C3               1\n",
            "D2               1\n",
            "other group      1\n",
            "Name: Emp. Group, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "job_dict = attrdata[\"Job Role Match\"].value_counts()\n",
        "job_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wILjkp59-PcS",
        "outputId": "4fe3e269-ded7-46e6-9080-d6563ba485bc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    480\n",
              "No     415\n",
              "Name: Job Role Match, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attrdata['Job Role Match'].value_counts().plot(kind='bar',color=['salmon','lightblue'],title=\"Job Role Match\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "HrEo51l--TpR",
        "outputId": "672180dd-f9cf-45ee-928d-e717616f61b2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde0dde0a60>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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": [
        "#Create a plot for crosstab\n",
        "\n",
        "pd.crosstab(attrdata['Job Role Match'],attrdata['Stay/Left']).plot(kind=\"bar\",figsize=(10,6))\n",
        "plt.title(\"Stay/Left vs Job Role Match\")\n",
        "plt.xlabel(\"Stay/Left\")\n",
        "plt.legend([\"Left\",\"Stay\"])\n",
        "plt.xticks(rotation=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "7dRwO8er-V0q",
        "outputId": "85f53cd3-d254-459e-8468-7bb0afaf9f46"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tenure_dict = attrdata[\"Tenure Grp.\"].value_counts()\n",
        "print(tenure_dict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTHbFYbQ-ai9",
        "outputId": "f6818267-2b4d-47b1-f380-984b5c97ce8b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> 1 & < =3    626\n",
            "< =1          269\n",
            "Name: Tenure Grp., dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Its Age vs stay/left\n",
        "sns.jointplot(x='Stay/Left',y='Age in YY.',data=attrdata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "P7SjwiHT-eBy",
        "outputId": "9525ed7f-a820-4dec-c7e9-ad949be3f0f7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fde0dd84d60>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(x='Stay/Left',y='Experience (YY.MM)',data=attrdata)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "2QqhDD1p-hVN",
        "outputId": "9719568c-fff3-4d16-8e36-6a98654e88cc"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fde0ced27f0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's make our correlation matrix visual\n",
        "corr_matrix=attrdata.corr()\n",
        "fig,ax=plt.subplots(figsize=(15,10))\n",
        "ax=sns.heatmap(corr_matrix,\n",
        "               annot=True,\n",
        "               linewidths=0.5,\n",
        "               fmt=\".2f\"\n",
        "              )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "pZoA0Z4H-0jO",
        "outputId": "3f9971cd-fa9e-4c54-b9ca-c43d58889aa5"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "location_dict = attrdata[\"Location\"].value_counts()\n",
        "print(location_dict)\n",
        "\n",
        "location_dict_new = {\n",
        "    'Chennai':       7,\n",
        "    'Noida':         6,\n",
        "    'Bangalore':     5,\n",
        "    'Hyderabad':     4,\n",
        "    'Pune':          3,\n",
        "    'Madurai':       2,\n",
        "    'Lucknow':       1,\n",
        "    'other place':   0,\n",
        "}\n",
        "\n",
        "print(location_dict_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfIZhMPf-3jF",
        "outputId": "3083199c-efd8-409d-b9e2-09566599446a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chennai       255\n",
            "Noida         236\n",
            "Bangalore     210\n",
            "Hyderabad      62\n",
            "Pune           55\n",
            "Madurai        29\n",
            "Lucknow        20\n",
            "Nagpur         14\n",
            "Vijayawada      6\n",
            "Mumbai          4\n",
            "Gurgaon         3\n",
            "Kolkata         1\n",
            "Name: Location, dtype: int64\n",
            "{'Chennai': 7, 'Noida': 6, 'Bangalore': 5, 'Hyderabad': 4, 'Pune': 3, 'Madurai': 2, 'Lucknow': 1, 'other place': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def location(x):\n",
        "    if str(x) in location_dict_new.keys():\n",
        "        return location_dict_new[str(x)]\n",
        "    else:\n",
        "        return location_dict_new['other place']\n",
        "    \n",
        "data_l = attrdata[\"Location\"].apply(location)\n",
        "attrdata['New Location'] = data_l\n",
        "attrdata.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "BfeA2sle-6r_",
        "outputId": "d82bfad7-8e95-4f9f-9379-f7e5d8c2b7fc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM) Marital Status  Age in YY.  \\\n",
              "1    0.00        < =1               13.00          Marr.       38.08   \n",
              "2    0.01        < =1               16.05          Marr.       36.04   \n",
              "3    0.01        < =1                6.06          Marr.       32.07   \n",
              "4    0.00        < =1                7.00          Marr.       32.05   \n",
              "5    0.01        < =1                9.01          Marr.       39.09   \n",
              "\n",
              "  Hiring Source Promoted/Non Promoted Job Role Match Stay/Left  New Location  \n",
              "1        Direct              Promoted             No      Stay             6  \n",
              "2        Direct              Promoted            Yes      Stay             5  \n",
              "3        Direct              Promoted            Yes      Stay             6  \n",
              "4        Direct          Non Promoted            Yes      Stay             1  \n",
              "5        Direct              Promoted             No      Stay             5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-67764956-1466-44c5-ae82-04f024e1cbb3\">\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>table id</th>\n",
              "      <th>name</th>\n",
              "      <th>phone number</th>\n",
              "      <th>Location</th>\n",
              "      <th>Emp. Group</th>\n",
              "      <th>Function</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Tenure Grp.</th>\n",
              "      <th>Experience (YY.MM)</th>\n",
              "      <th>Marital Status</th>\n",
              "      <th>Age in YY.</th>\n",
              "      <th>Hiring Source</th>\n",
              "      <th>Promoted/Non Promoted</th>\n",
              "      <th>Job Role Match</th>\n",
              "      <th>Stay/Left</th>\n",
              "      <th>New Location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B7</td>\n",
              "      <td>Support</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>13.00</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>38.08</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>No</td>\n",
              "      <td>Stay</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>16.05</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>36.04</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>6.06</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>32.07</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Lucknow</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>7.00</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>32.05</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Non Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>9.01</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>39.09</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>No</td>\n",
              "      <td>Stay</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67764956-1466-44c5-ae82-04f024e1cbb3')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-67764956-1466-44c5-ae82-04f024e1cbb3');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gen = pd.get_dummies(attrdata[\"Function\"])\n",
        "gen.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "o0zSF7xI--4d",
        "outputId": "ae67c22f-b7dc-4f4b-a091-237cebfa9a48"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Operation  Sales  Support\n",
              "1          0      0        1\n",
              "2          1      0        0\n",
              "3          1      0        0\n",
              "4          1      0        0\n",
              "5          1      0        0"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Operation</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-245dc5b5-d11c-4f99-9875-524565a78395')\"\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-245dc5b5-d11c-4f99-9875-524565a78395 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-245dc5b5-d11c-4f99-9875-524565a78395');\n",
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              "            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",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hr = pd.get_dummies(attrdata[\"Hiring Source\"])\n",
        "hr.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N8YvLmQ__CMe",
        "outputId": "15d5ec24-5e8e-4a39-d3ef-4b2253386547"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Agency  Direct  Employee Referral\n",
              "1       0       1                  0\n",
              "2       0       1                  0\n",
              "3       0       1                  0\n",
              "4       0       1                  0\n",
              "5       0       1                  0"
            ],
            "text/html": [
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              "\n",
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              "\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Agency</th>\n",
              "      <th>Direct</th>\n",
              "      <th>Employee Referral</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "</div>\n",
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              "\n",
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              "          const element = document.querySelector('#df-c20463c2-3f31-4656-9e59-3925d3b682d2');\n",
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              "            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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Mar(x):\n",
        "    if str(x) in Marital_dict.keys() and Marital_dict[str(x)] > 100:\n",
        "        return str(x)\n",
        "    else:\n",
        "        return 'other status'\n",
        "    \n",
        "data_l = attrdata[\"Marital Status\"].apply(Mar)\n",
        "attrdata['New Marital'] = data_l\n",
        "attrdata.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "fmP5d74__Ejv",
        "outputId": "27dcd8a2-6655-4ec9-8895-9a68aaaf5b40"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM) Marital Status  Age in YY.  \\\n",
              "1    0.00        < =1               13.00          Marr.       38.08   \n",
              "2    0.01        < =1               16.05          Marr.       36.04   \n",
              "3    0.01        < =1                6.06          Marr.       32.07   \n",
              "4    0.00        < =1                7.00          Marr.       32.05   \n",
              "5    0.01        < =1                9.01          Marr.       39.09   \n",
              "\n",
              "  Hiring Source Promoted/Non Promoted Job Role Match Stay/Left  New Location  \\\n",
              "1        Direct              Promoted             No      Stay             6   \n",
              "2        Direct              Promoted            Yes      Stay             5   \n",
              "3        Direct              Promoted            Yes      Stay             6   \n",
              "4        Direct          Non Promoted            Yes      Stay             1   \n",
              "5        Direct              Promoted             No      Stay             5   \n",
              "\n",
              "  New Marital  \n",
              "1       Marr.  \n",
              "2       Marr.  \n",
              "3       Marr.  \n",
              "4       Marr.  \n",
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    {
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        "Mr = pd.get_dummies(attrdata[\"New Marital\"])\n",
        "Mr.head()"
      ],
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      "execution_count": 34,
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          "data": {
            "text/plain": [
              "   Marr.  Single  other status\n",
              "1      1       0             0\n",
              "2      1       0             0\n",
              "3      1       0             0\n",
              "4      1       0             0\n",
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    {
      "cell_type": "code",
      "source": [
        "def Promoted(x):\n",
        "    if x == 'Promoted':\n",
        "        return int(1)\n",
        "    else:\n",
        "        return int(0)\n",
        "\n",
        "data_l = attrdata[\"Promoted/Non Promoted\"].apply(Promoted)\n",
        "attrdata['New Promotion'] = data_l\n",
        "attrdata.head()"
      ],
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        "id": "9jFy1p6Z_KFf",
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      },
      "execution_count": 35,
      "outputs": [
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          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM) Marital Status  Age in YY.  \\\n",
              "1    0.00        < =1               13.00          Marr.       38.08   \n",
              "2    0.01        < =1               16.05          Marr.       36.04   \n",
              "3    0.01        < =1                6.06          Marr.       32.07   \n",
              "4    0.00        < =1                7.00          Marr.       32.05   \n",
              "5    0.01        < =1                9.01          Marr.       39.09   \n",
              "\n",
              "  Hiring Source Promoted/Non Promoted Job Role Match Stay/Left  New Location  \\\n",
              "1        Direct              Promoted             No      Stay             6   \n",
              "2        Direct              Promoted            Yes      Stay             5   \n",
              "3        Direct              Promoted            Yes      Stay             6   \n",
              "4        Direct          Non Promoted            Yes      Stay             1   \n",
              "5        Direct              Promoted             No      Stay             5   \n",
              "\n",
              "  New Marital  New Promotion  \n",
              "1       Marr.              1  \n",
              "2       Marr.              1  \n",
              "3       Marr.              1  \n",
              "4       Marr.              0  \n",
              "5       Marr.              1  "
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              "      <th>Function</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Tenure Grp.</th>\n",
              "      <th>Experience (YY.MM)</th>\n",
              "      <th>Marital Status</th>\n",
              "      <th>Age in YY.</th>\n",
              "      <th>Hiring Source</th>\n",
              "      <th>Promoted/Non Promoted</th>\n",
              "      <th>Job Role Match</th>\n",
              "      <th>Stay/Left</th>\n",
              "      <th>New Location</th>\n",
              "      <th>New Marital</th>\n",
              "      <th>New Promotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B7</td>\n",
              "      <td>Support</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>13.00</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>38.08</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>No</td>\n",
              "      <td>Stay</td>\n",
              "      <td>6</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>16.05</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>36.04</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>5</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>6.06</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>32.07</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>6</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Lucknow</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>7.00</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>32.05</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Non Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>1</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>9.01</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>39.09</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>No</td>\n",
              "      <td>Stay</td>\n",
              "      <td>5</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82f643f9-51f1-4f3d-abd9-6b96518c71c1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
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              "      gap: 12px;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\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",
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              "\n",
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              "    }\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-82f643f9-51f1-4f3d-abd9-6b96518c71c1 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-82f643f9-51f1-4f3d-abd9-6b96518c71c1');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Emp_dict_new = {\n",
        "    'B1': 4,\n",
        "    'B2': 3,\n",
        "    'B3': 2,\n",
        "    'other group': 1,\n",
        "}"
      ],
      "metadata": {
        "id": "AJ-jMbHR_M_A"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def emp(x):\n",
        "    if str(x) in Emp_dict_new.keys():\n",
        "        return str(x)\n",
        "    else:\n",
        "        return 'other group'\n",
        " \n",
        "data_l = attrdata[\"Emp. Group\"].apply(emp)\n",
        "attrdata['New EMP'] = data_l\n",
        "\n",
        "emp = pd.get_dummies(attrdata[\"New EMP\"])\n",
        "attrdata.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "FJQKI_Ns_RXH",
        "outputId": "f8706756-ea8a-49ff-f1fb-9be197badcab"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM) Marital Status  Age in YY.  \\\n",
              "1    0.00        < =1               13.00          Marr.       38.08   \n",
              "2    0.01        < =1               16.05          Marr.       36.04   \n",
              "3    0.01        < =1                6.06          Marr.       32.07   \n",
              "4    0.00        < =1                7.00          Marr.       32.05   \n",
              "5    0.01        < =1                9.01          Marr.       39.09   \n",
              "\n",
              "  Hiring Source Promoted/Non Promoted Job Role Match Stay/Left  New Location  \\\n",
              "1        Direct              Promoted             No      Stay             6   \n",
              "2        Direct              Promoted            Yes      Stay             5   \n",
              "3        Direct              Promoted            Yes      Stay             6   \n",
              "4        Direct          Non Promoted            Yes      Stay             1   \n",
              "5        Direct              Promoted             No      Stay             5   \n",
              "\n",
              "  New Marital  New Promotion      New EMP  \n",
              "1       Marr.              1  other group  \n",
              "2       Marr.              1           B3  \n",
              "3       Marr.              1           B2  \n",
              "4       Marr.              0           B2  \n",
              "5       Marr.              1           B3  "
            ],
            "text/html": [
              "\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
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              "      <td>Noida</td>\n",
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              "      <td>Support</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
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              "          const element = document.querySelector('#df-ee537023-0513-420d-888b-b24e811bbab1');\n",
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              "            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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Job(x):\n",
        "    if x == 'Yes':\n",
        "        return int(1)\n",
        "    else:\n",
        "        return int(0)\n",
        "    \n",
        "data_l = attrdata[\"Job Role Match\"].apply(Job)\n",
        "attrdata['New Job Role Match'] = data_l\n",
        "attrdata.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "sHtj4TQy_T7C",
        "outputId": "4c1dc494-2234-4f51-dc50-a7b46e71f03e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM)  ... Age in YY.  Hiring Source  \\\n",
              "1    0.00        < =1               13.00  ...      38.08         Direct   \n",
              "2    0.01        < =1               16.05  ...      36.04         Direct   \n",
              "3    0.01        < =1                6.06  ...      32.07         Direct   \n",
              "4    0.00        < =1                7.00  ...      32.05         Direct   \n",
              "5    0.01        < =1                9.01  ...      39.09         Direct   \n",
              "\n",
              "  Promoted/Non Promoted Job Role Match Stay/Left New Location  New Marital  \\\n",
              "1              Promoted             No      Stay            6        Marr.   \n",
              "2              Promoted            Yes      Stay            5        Marr.   \n",
              "3              Promoted            Yes      Stay            6        Marr.   \n",
              "4          Non Promoted            Yes      Stay            1        Marr.   \n",
              "5              Promoted             No      Stay            5        Marr.   \n",
              "\n",
              "  New Promotion      New EMP New Job Role Match  \n",
              "1             1  other group                  0  \n",
              "2             1           B3                  1  \n",
              "3             1           B2                  1  \n",
              "4             0           B2                  1  \n",
              "5             1           B3                  0  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <td>&lt; =1</td>\n",
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              "      <td>Noida</td>\n",
              "      <td>B2</td>\n",
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              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>6.06</td>\n",
              "      <td>...</td>\n",
              "      <td>32.07</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>6</td>\n",
              "      <td>Marr.</td>\n",
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              "      <td>B2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>9876544345</td>\n",
              "      <td>Lucknow</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>7.00</td>\n",
              "      <td>...</td>\n",
              "      <td>32.05</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Non Promoted</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Stay</td>\n",
              "      <td>1</td>\n",
              "      <td>Marr.</td>\n",
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              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>9.01</td>\n",
              "      <td>...</td>\n",
              "      <td>39.09</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>No</td>\n",
              "      <td>Stay</td>\n",
              "      <td>5</td>\n",
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              "      <td>B3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abea5c75-94de-4277-b1e9-23704e470a39')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-abea5c75-94de-4277-b1e9-23704e470a39 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-abea5c75-94de-4277-b1e9-23704e470a39');\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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Gen(x):\n",
        "    if x in gender_dict.keys():\n",
        "        return str(x)\n",
        "    else:\n",
        "        return 'other'\n",
        "\n",
        "data_l = attrdata[\"Gender \"].apply(Gen)\n",
        "attrdata['New Gender'] = data_l\n",
        "attrdata.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "oyZ3w2xr_WyJ",
        "outputId": "a1031e04-05fc-4c9b-9f3d-64b3f58de515"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM)  ... Hiring Source  \\\n",
              "1    0.00        < =1               13.00  ...        Direct   \n",
              "2    0.01        < =1               16.05  ...        Direct   \n",
              "3    0.01        < =1                6.06  ...        Direct   \n",
              "4    0.00        < =1                7.00  ...        Direct   \n",
              "5    0.01        < =1                9.01  ...        Direct   \n",
              "\n",
              "   Promoted/Non Promoted Job Role Match Stay/Left New Location New Marital  \\\n",
              "1               Promoted             No      Stay            6       Marr.   \n",
              "2               Promoted            Yes      Stay            5       Marr.   \n",
              "3               Promoted            Yes      Stay            6       Marr.   \n",
              "4           Non Promoted            Yes      Stay            1       Marr.   \n",
              "5               Promoted             No      Stay            5       Marr.   \n",
              "\n",
              "   New Promotion      New EMP  New Job Role Match New Gender  \n",
              "1              1  other group                   0       Male  \n",
              "2              1           B3                   1       Male  \n",
              "3              1           B2                   1       Male  \n",
              "4              0           B2                   1       Male  \n",
              "5              1           B3                   0       Male  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
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              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>9.01</td>\n",
              "      <td>...</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Promoted</td>\n",
              "      <td>No</td>\n",
              "      <td>Stay</td>\n",
              "      <td>5</td>\n",
              "      <td>Marr.</td>\n",
              "      <td>1</td>\n",
              "      <td>B3</td>\n",
              "      <td>0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
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    {
      "cell_type": "code",
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        "gend = pd.get_dummies(attrdata[\"New Gender\"])\n",
        "gend.head()"
      ],
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      "execution_count": 40,
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        {
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            "text/plain": [
              "   Female  Male  other\n",
              "1       0     1      0\n",
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    {
      "cell_type": "code",
      "source": [
        "tengrp = pd.get_dummies(attrdata[\"Tenure Grp.\"])\n",
        "tengrp.head()"
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        "id": "_jBeqZ-P_b9L",
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      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   < =1  > 1 & < =3\n",
              "1     1           0\n",
              "2     1           0\n",
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              "5     1           0"
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.concat([attrdata, hr, Mr, emp, tengrp, gen, gend], axis = 1)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "8RsBMh4j_fEw",
        "outputId": "c15ff3e8-beb6-4e07-c6ff-d8f7769a2f69"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   table id name  phone number   Location Emp. Group   Function Gender   \\\n",
              "1         2  sid    9876544345      Noida         B7    Support    Male   \n",
              "2         3  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "3         4  sid    9876544345      Noida         B2  Operation    Male   \n",
              "4         5  sid    9876544345    Lucknow         B2  Operation    Male   \n",
              "5         6  sid    9876544345  Bangalore         B3  Operation    Male   \n",
              "\n",
              "   Tenure Tenure Grp.  Experience (YY.MM)  ... B3  other group < =1  \\\n",
              "1    0.00        < =1               13.00  ...  0            1    1   \n",
              "2    0.01        < =1               16.05  ...  1            0    1   \n",
              "3    0.01        < =1                6.06  ...  0            0    1   \n",
              "4    0.00        < =1                7.00  ...  0            0    1   \n",
              "5    0.01        < =1                9.01  ...  1            0    1   \n",
              "\n",
              "  > 1 & < =3 Operation Sales  Support Female  Male other  \n",
              "1          0         0     0        1      0     1     0  \n",
              "2          0         1     0        0      0     1     0  \n",
              "3          0         1     0        0      0     1     0  \n",
              "4          0         1     0        0      0     1     0  \n",
              "5          0         1     0        0      0     1     0  \n",
              "\n",
              "[5 rows x 40 columns]"
            ],
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              "\n",
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              "        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>table id</th>\n",
              "      <th>name</th>\n",
              "      <th>phone number</th>\n",
              "      <th>Location</th>\n",
              "      <th>Emp. Group</th>\n",
              "      <th>Function</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Tenure Grp.</th>\n",
              "      <th>Experience (YY.MM)</th>\n",
              "      <th>...</th>\n",
              "      <th>B3</th>\n",
              "      <th>other group</th>\n",
              "      <th>&lt; =1</th>\n",
              "      <th>&gt; 1 &amp; &lt; =3</th>\n",
              "      <th>Operation</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Support</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>other</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B7</td>\n",
              "      <td>Support</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>13.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>16.05</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Noida</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>6.06</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Lucknow</td>\n",
              "      <td>B2</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.00</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>7.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sid</td>\n",
              "      <td>9876544345</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>B3</td>\n",
              "      <td>Operation</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.01</td>\n",
              "      <td>&lt; =1</td>\n",
              "      <td>9.01</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 40 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5be64996-1c95-4341-9d3d-1516a2c7e7c1')\"\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-5be64996-1c95-4341-9d3d-1516a2c7e7c1 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-5be64996-1c95-4341-9d3d-1516a2c7e7c1');\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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8x44oNf_iH5",
        "outputId": "1ff4e902-b4be-441c-96fc-474df127983c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['table id', 'name', 'phone number', 'Location', 'Emp. Group',\n",
              "       'Function', 'Gender ', 'Tenure', 'Tenure Grp.', 'Experience (YY.MM)',\n",
              "       'Marital Status', 'Age in YY.', 'Hiring Source',\n",
              "       'Promoted/Non Promoted', 'Job Role Match', 'Stay/Left', 'New Location',\n",
              "       'New Marital', 'New Promotion', 'New EMP', 'New Job Role Match',\n",
              "       'New Gender', 'Agency', 'Direct', 'Employee Referral', 'Marr.',\n",
              "       'Single', 'other status', 'B1', 'B2', 'B3', 'other group', '< =1',\n",
              "       '> 1 & < =3', 'Operation', 'Sales', 'Support', 'Female', 'Male',\n",
              "       'other'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop([\"table id\", \"name\", \"Marital Status\",\"Promoted/Non Promoted\",\"Function\",\"Emp. Group\",\"Job Role Match\",\"Location\"\n",
        "              ,\"Hiring Source\",\"Gender \", 'Tenure', 'New Gender', 'New Marital', 'New EMP'],axis=1,inplace=True)\n",
        "\n",
        "dataset1 = dataset.drop(['Tenure Grp.', 'phone number'], axis = 1)\n",
        "dataset1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-faMc6L_k2B",
        "outputId": "8f8bbaa0-4f08-4d9f-b720-f794d886538d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Experience (YY.MM)', 'Age in YY.', 'Stay/Left', 'New Location',\n",
              "       'New Promotion', 'New Job Role Match', 'Agency', 'Direct',\n",
              "       'Employee Referral', 'Marr.', 'Single', 'other status', 'B1', 'B2',\n",
              "       'B3', 'other group', '< =1', '> 1 & < =3', 'Operation', 'Sales',\n",
              "       'Support', 'Female', 'Male', 'other'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's make our correlation matrix visual\n",
        "corr_matrix=dataset1.corr()\n",
        "fig,ax=plt.subplots(figsize=(15,10))\n",
        "ax=sns.heatmap(corr_matrix,\n",
        "               annot=True,\n",
        "               linewidths=0.5,\n",
        "               fmt=\".2f\"\n",
        "              )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "GdOOpeTa_nhw",
        "outputId": "a3e0f39b-b7bb-4592-9c0e-e4a1a3a6786e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Target \n",
        "\"\"\"\n",
        "def Target(x):\n",
        "    if x in \"Stay\":\n",
        "        return False\n",
        "    else:\n",
        "        return True\n",
        "    \n",
        "data_l = dataset1[\"Stay/Left\"].apply(Target)\n",
        "dataset1['Stay/Left'] = data_l\n",
        "\"\"\"\n",
        "dataset1['Stay/Left'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5zksibI_qxC",
        "outputId": "784e9ed6-35e2-4271-c372-743d66ef3a31"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    Stay\n",
              "2    Stay\n",
              "3    Stay\n",
              "4    Stay\n",
              "5    Stay\n",
              "Name: Stay/Left, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset1.to_csv(\"processed table.csv\")"
      ],
      "metadata": {
        "id": "lTZPDkqaALMf"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"processed table.csv\")\n",
        "dataset = pd.DataFrame(dataset)\n",
        "y = dataset[\"Stay/Left\"]\n",
        "X = dataset.drop(\"Stay/Left\",axis=1)"
      ],
      "metadata": {
        "id": "g_L53yDvAOed"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=4)\n",
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "MH3BH4o3ARbH",
        "outputId": "b2b8da9d-ece3-48af-84b5-8ae18e9a48c2"
      },
      "execution_count": 49,
      "outputs": [
        {
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              "     Unnamed: 0  Experience (YY.MM)  Age in YY.  New Location  New Promotion  \\\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23f941b1-f14b-496a-84ff-7d2229624482')\"\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-23f941b1-f14b-496a-84ff-7d2229624482 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-23f941b1-f14b-496a-84ff-7d2229624482');\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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import svm"
      ],
      "metadata": {
        "id": "z1sGcDEGATX5"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LogisticRegression(C = 0.1, random_state = 42, solver = 'liblinear')\n",
        "dt=DecisionTreeClassifier()\n",
        "rm=RandomForestClassifier()\n",
        "gnb=GaussianNB()\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "svm = svm.SVC(kernel='linear')\n"
      ],
      "metadata": {
        "id": "waRWnNi9AWPh"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for a,b in zip([lr,dt,knn,svm,rm,gnb],[\"Logistic Regression\",\"Decision Tree\",\"KNN\",\"SVM\",\"Random Forest\",\"Naive Bayes\"]):\n",
        "    a.fit(X_train,y_train)\n",
        "    prediction=a.predict(X_train)\n",
        "    y_pred=a.predict(X_test)\n",
        "    score1=accuracy_score(y_train,prediction)\n",
        "    score=accuracy_score(y_test,y_pred)\n",
        "    msg1=\"[%s] training data accuracy is : %f\" % (b,score1)\n",
        "    msg2=\"[%s] test data accuracy is : %f\" % (b,score)\n",
        "    print(msg1)\n",
        "    print(msg2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOjj-6OJAYea",
        "outputId": "0b2fa6ed-80c1-470f-ee46-40fde2fa7759"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Logistic Regression] training data accuracy is : 0.891061\n",
            "[Logistic Regression] test data accuracy is : 0.877095\n",
            "[Decision Tree] training data accuracy is : 1.000000\n",
            "[Decision Tree] test data accuracy is : 0.854749\n",
            "[KNN] training data accuracy is : 0.804469\n",
            "[KNN] test data accuracy is : 0.586592\n",
            "[SVM] training data accuracy is : 0.878492\n",
            "[SVM] test data accuracy is : 0.865922\n",
            "[Random Forest] training data accuracy is : 1.000000\n",
            "[Random Forest] test data accuracy is : 0.893855\n",
            "[Naive Bayes] training data accuracy is : 0.870112\n",
            "[Naive Bayes] test data accuracy is : 0.826816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_scores={'Logistic Regression':lr.score(X_test,y_test),\n",
        "             'KNN classifier':knn.score(X_test,y_test),\n",
        "             'Support Vector Machine':svm.score(X_test,y_test),\n",
        "             'Random forest':rm.score(X_test,y_test),\n",
        "              'Decision tree':dt.score(X_test,y_test),\n",
        "              'Naive Bayes':gnb.score(X_test,y_test)\n",
        "             }\n",
        "model_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpBMojBzAbfd",
        "outputId": "80a87e42-bd61-4ee9-fa2b-ccf2a21f5522"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Logistic Regression': 0.8770949720670391,\n",
              " 'KNN classifier': 0.5865921787709497,\n",
              " 'Support Vector Machine': 0.8659217877094972,\n",
              " 'Random forest': 0.8938547486033519,\n",
              " 'Decision tree': 0.8547486033519553,\n",
              " 'Naive Bayes': 0.8268156424581006}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "rm_y_preds = rm.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test,rm_y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSutnEvSAim1",
        "outputId": "854a2cd4-fa8e-489b-d964-2508b46f229f"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Left       0.85      0.84      0.84        61\n",
            "        Stay       0.92      0.92      0.92       118\n",
            "\n",
            "    accuracy                           0.89       179\n",
            "   macro avg       0.88      0.88      0.88       179\n",
            "weighted avg       0.89      0.89      0.89       179\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "lr_y_preds = lr.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test,lr_y_preds))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvlq9LI8AmPE",
        "outputId": "cc095116-0fa2-46ba-e62e-9f01ee95056f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Left       0.81      0.84      0.82        61\n",
            "        Stay       0.91      0.90      0.91       118\n",
            "\n",
            "    accuracy                           0.88       179\n",
            "   macro avg       0.86      0.87      0.86       179\n",
            "weighted avg       0.88      0.88      0.88       179\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_compare=pd.DataFrame(model_scores,index=['accuracy'])\n",
        "model_compare"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "2DIZrrEcApMU",
        "outputId": "5f3e7bcb-2073-4ec4-be49-61714d325dc2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Logistic Regression  KNN classifier  Support Vector Machine  \\\n",
              "accuracy             0.877095        0.586592                0.865922   \n",
              "\n",
              "          Random forest  Decision tree  Naive Bayes  \n",
              "accuracy       0.893855       0.854749     0.826816  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cd5a12e-5239-47bc-a0df-28c5a8cac135\">\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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Logistic Regression</th>\n",
              "      <th>KNN classifier</th>\n",
              "      <th>Support Vector Machine</th>\n",
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              "  <tbody>\n",
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              "      <th>accuracy</th>\n",
              "      <td>0.877095</td>\n",
              "      <td>0.586592</td>\n",
              "      <td>0.865922</td>\n",
              "      <td>0.893855</td>\n",
              "      <td>0.854749</td>\n",
              "      <td>0.826816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cd5a12e-5239-47bc-a0df-28c5a8cac135')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       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-2cd5a12e-5239-47bc-a0df-28c5a8cac135 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-2cd5a12e-5239-47bc-a0df-28c5a8cac135');\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": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_compare.T.plot(kind='bar') # (T is here for transpose)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "P9pSBU2AArxl",
        "outputId": "a1dba42b-7127-49b2-8235-350f53980cdb"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde07d50b80>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "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": [
        "# Logistic regression"
      ],
      "metadata": {
        "id": "2aum1pN3A0Z-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_dict=dict(zip(dataset.columns,list(lr.coef_[0])))\n",
        "feature_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2Ro3AZ2Az6W",
        "outputId": "a33c524c-ba4c-42ab-85de-8aaf632f233b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Unnamed: 0': -0.00036963861400031764,\n",
              " 'Experience (YY.MM)': 0.13976826900535447,\n",
              " 'Age in YY.': -0.019622030356904817,\n",
              " 'Stay/Left': 0.024627352505541638,\n",
              " 'New Location': 0.09666512091483484,\n",
              " 'New Promotion': 2.753336139913329,\n",
              " 'New Job Role Match': -0.31312348488600206,\n",
              " 'Agency': -0.0202708852815937,\n",
              " 'Direct': 0.20478720828419877,\n",
              " 'Employee Referral': 0.3879631878069917,\n",
              " 'Marr.': -0.5042922677837546,\n",
              " 'Single': -0.012278081906640637,\n",
              " 'other status': -0.22031197149631118,\n",
              " 'B1': 0.17649315185040498,\n",
              " 'B2': -0.09367965848390736,\n",
              " 'B3': 0.008891316246410692,\n",
              " 'other group': -0.12993373231132835,\n",
              " '< =1': 0.0013265704279338923,\n",
              " '> 1 & < =3': -0.05949484160180485,\n",
              " 'Operation': -0.01820046425055137,\n",
              " 'Sales': -0.050911856031046525,\n",
              " 'Support': 0.12249364799434526,\n",
              " 'Female': -0.1951606022748482,\n",
              " 'Male': -0.05594020760290055}"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_df=pd.DataFrame(feature_dict,index=[0])\n",
        "feature_df.T.plot(kind=\"bar\",legend=False,title=\"Feature Importance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "EsYgubUEAvlV",
        "outputId": "98ddefa8-ea5d-4c2c-c3bb-8c8d7d5555db"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fde07d40a00>"
            ]
          },
          "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": [
        "import pickle\n",
        "\n",
        "# Save the trained model as a pickle string.\n",
        "saved_model = pickle.dumps(lr)\n",
        "\n",
        "# Load the pickled model\n",
        "lr_from_pickle = pickle.loads(saved_model)\n",
        "\n",
        "# Use the loaded pickled model to make predictions\n",
        "lr_from_pickle.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04dZjDa7A7t3",
        "outputId": "a184133f-68ec-43cd-84a9-4344d413fab5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Left', 'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Left',\n",
              "       'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Left',\n",
              "       'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Left', 'Stay', 'Stay',\n",
              "       'Stay', 'Left', 'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay',\n",
              "       'Left', 'Stay', 'Stay', 'Left', 'Stay', 'Stay', 'Left', 'Stay',\n",
              "       'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Left',\n",
              "       'Left', 'Stay', 'Left', 'Left', 'Stay', 'Stay', 'Left', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Left',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Left',\n",
              "       'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay',\n",
              "       'Left', 'Left', 'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay',\n",
              "       'Stay', 'Left', 'Stay', 'Left', 'Stay', 'Left', 'Left', 'Stay',\n",
              "       'Left', 'Stay', 'Stay', 'Left', 'Left', 'Left', 'Stay', 'Stay',\n",
              "       'Left', 'Stay', 'Left', 'Stay', 'Left', 'Left', 'Left', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Left', 'Left', 'Stay', 'Left', 'Stay',\n",
              "       'Stay', 'Left', 'Left', 'Stay', 'Stay', 'Left', 'Stay', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Stay', 'Stay',\n",
              "       'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay',\n",
              "       'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Left', 'Left',\n",
              "       'Left', 'Left', 'Stay', 'Stay', 'Left', 'Left', 'Stay', 'Left',\n",
              "       'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading dependency\n",
        "import joblib\n",
        "\n",
        "# saving our model - model - model , filename - model_lr\n",
        "joblib.dump(lr , 'model_lr')\n",
        "\n",
        "# opening the file- model_jlib\n",
        "m_jlib = joblib.load('model_lr')\n",
        "\n",
        "# check prediction\n",
        "m_jlib.predict(X_test) # similar output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJD-aRQhCwPM",
        "outputId": "96e76dc6-1aa7-4cdb-96e6-ddcd0158895f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Left', 'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Left',\n",
              "       'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Left',\n",
              "       'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Left', 'Stay', 'Stay',\n",
              "       'Stay', 'Left', 'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay',\n",
              "       'Left', 'Stay', 'Stay', 'Left', 'Stay', 'Stay', 'Left', 'Stay',\n",
              "       'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Left',\n",
              "       'Left', 'Stay', 'Left', 'Left', 'Stay', 'Stay', 'Left', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Left',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Left',\n",
              "       'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay',\n",
              "       'Left', 'Left', 'Left', 'Stay', 'Left', 'Stay', 'Stay', 'Stay',\n",
              "       'Stay', 'Left', 'Stay', 'Left', 'Stay', 'Left', 'Left', 'Stay',\n",
              "       'Left', 'Stay', 'Stay', 'Left', 'Left', 'Left', 'Stay', 'Stay',\n",
              "       'Left', 'Stay', 'Left', 'Stay', 'Left', 'Left', 'Left', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Left', 'Left', 'Stay', 'Left', 'Stay',\n",
              "       'Stay', 'Left', 'Left', 'Stay', 'Stay', 'Left', 'Stay', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Stay', 'Stay',\n",
              "       'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay', 'Stay',\n",
              "       'Left', 'Stay', 'Stay', 'Stay', 'Stay', 'Left', 'Left', 'Left',\n",
              "       'Left', 'Left', 'Stay', 'Stay', 'Left', 'Left', 'Stay', 'Left',\n",
              "       'Stay', 'Stay', 'Stay', 'Left', 'Stay', 'Stay', 'Stay', 'Stay',\n",
              "       'Stay', 'Stay', 'Stay'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hCEnlNxbCz_1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}