without_codePD_model_Final (1).ipynb

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   "source": [
    "#import the basic libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive/\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "\n",
    "drive.mount('/content/drive/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
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     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "mUkK8GACroQK"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 43315,
     "status": "ok",
     "timestamp": 1693906023302,
     "user": {
      "displayName": "SOUMYA NAYAK",
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    "id": "ZXVaauOUq3tt"
   },
   "outputs": [],
   "source": [
    "#load all the datasets we have created previously\n",
    "\n",
    "load_data_inputs_train=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_inputs_train.csv',index_col=0)\n",
    "load_data_targets_train=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_targets_train.csv',index_col=0,header=None)\n",
    "load_data_inputs_test=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_inputs_test.csv',index_col=0)\n",
    "load_data_targets_test=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/ML Projects/Credit risk analysis/loan_data_targets_test.csv',index_col=0,header=None)"
   ]
  },
  {
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      "text/plain": [
       "        Unnamed: 0        id  member_id  loan_amnt  funded_amnt  \\\n",
       "427211      427211  12796369   14818505      24000        24000   \n",
       "206088      206088   1439740    1691948      10000        10000   \n",
       "136020      136020   5214749    6556909      20425        20425   \n",
       "412305      412305  13827698   15890016      17200        17200   \n",
       "36159        36159    422455     496525       8400         8400   \n",
       "\n",
       "        funded_amnt_inv        term  int_rate  installment grade  ...  \\\n",
       "427211          24000.0   36 months      8.90       762.08     A  ...   \n",
       "206088          10000.0   36 months     14.33       343.39     C  ...   \n",
       "136020          20425.0   36 months      8.90       648.56     A  ...   \n",
       "412305          17200.0   36 months     16.59       609.73     D  ...   \n",
       "36159            7450.0   36 months     12.84       282.40     C  ...   \n",
       "\n",
       "       dti:20-25 dti:25-30 dti:30-35 dti:35-40  dti:>40  \\\n",
       "427211         1         0         0         0        0   \n",
       "206088         0         0         0         0        0   \n",
       "136020         0         0         0         0        0   \n",
       "412305         0         0         0         0        0   \n",
       "36159          1         0         0         0        0   \n",
       "\n",
       "       mths_since_last_record:Missing mths_since_last_record:0-90  \\\n",
       "427211                              1                           0   \n",
       "206088                              1                           0   \n",
       "136020                              1                           0   \n",
       "412305                              1                           0   \n",
       "36159                               1                           0   \n",
       "\n",
       "       mths_since_last_record:90-120 mths_since_last_record:120-127  \\\n",
       "427211                             0                              0   \n",
       "206088                             0                              0   \n",
       "136020                             0                              0   \n",
       "412305                             0                              0   \n",
       "36159                              0                              0   \n",
       "\n",
       "       mths_since_last_record:>=127  \n",
       "427211                            0  \n",
       "206088                            0  \n",
       "136020                            0  \n",
       "412305                            0  \n",
       "36159                             0  \n",
       "\n",
       "[5 rows x 338 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the load_data_inputs_train dataframe\n",
    "load_data_inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "executionInfo": {
     "elapsed": 496,
     "status": "ok",
     "timestamp": 1693906033201,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "MlnZAXkQq3tv",
    "outputId": "c75f75d3-8ef5-48f5-99b8-0289e6c6d753"
   },
   "outputs": [
    {
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "\n",
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       "        document.querySelector('#df-1b3f261f-18d9-448b-857b-786293972ba6 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-1b3f261f-18d9-448b-857b-786293972ba6');\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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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "\n",
       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    border-radius: 50%;\n",
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       "    display: none;\n",
       "    fill: var(--fill-color);\n",
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       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
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       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-019af1e6-4c40-4bd9-ad91-3392b9ac1754 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                 1\n",
       "0                 \n",
       "NaN       good_bad\n",
       "427211.0         1\n",
       "206088.0         1\n",
       "136020.0         1\n",
       "412305.0         0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the load_data_targets_train dataframe\n",
    "load_data_targets_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 582,
     "status": "ok",
     "timestamp": 1693906049044,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "k2_HNVWmq3tv"
   },
   "outputs": [],
   "source": [
    "#drop the first row as it is string\n",
    "load_data_targets_train=load_data_targets_train.drop(load_data_targets_train.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "executionInfo": {
     "elapsed": 653,
     "status": "ok",
     "timestamp": 1693906051676,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "5mgLyCKnq3tw",
    "outputId": "ed20a32b-971f-4518-9a15-78d1202aac77"
   },
   "outputs": [
    {
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "        document.querySelector('#df-6fb2be00-11df-433d-809d-448f4fffde41 button.colab-df-convert');\n",
       "      buttonEl.style.display =\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",
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       "            title=\"Suggest charts.\"\n",
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       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
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       "    </g>\n",
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-ac427dc1-1c4b-411c-9d21-5c6f80311b50 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "          1\n",
       "0          \n",
       "427211.0  1\n",
       "206088.0  1\n",
       "136020.0  1\n",
       "412305.0  0\n",
       "36159.0   0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the load_data_targets_train dataframe\n",
    "load_data_targets_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1693906054605,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "9e0cB_nJq3tw",
    "outputId": "bd99886a-5da8-4d1a-8fdf-775c597b6f3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 338)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the shape of load_data_inputs_train dataframe\n",
    "load_data_inputs_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1693906057245,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "mFqsf-Jtq3tw",
    "outputId": "c520d3a4-8278-4170-b1a8-ddccc5a76022"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the shape ofload_data_targets_train dataframe\n",
    "load_data_targets_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1693906060333,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "aJPLxAIvq3tw",
    "outputId": "14375408-d1eb-40be-c370-00d1fc69435d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 338)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the shape of load_data_inputs_test dataframe\n",
    "load_data_inputs_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 769,
     "status": "ok",
     "timestamp": 1693906063823,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "6yY2sBZEq3tx",
    "outputId": "756eb0cf-f2cf-472a-eb66-37e01957926b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93258, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the shape of load_data_targets_test dataframe\n",
    "load_data_targets_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1693906065999,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "MS5DuxX3q3tx",
    "outputId": "8ec754ec-e2e9-40ef-a6de-98f8aff93b47"
   },
   "outputs": [
    {
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       "        document.querySelector('#df-840c9c3a-31ea-4170-bfcb-4cedcdac468d 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-840c9c3a-31ea-4170-bfcb-4cedcdac468d');\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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       "\n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
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       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-3ed9a84f-463b-4f83-91aa-6ed5daf5b51a button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                 1\n",
       "0                 \n",
       "NaN       good_bad\n",
       "362514.0         1\n",
       "288564.0         1\n",
       "213591.0         1\n",
       "263083.0         1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the load_data_targets_test dataframe\n",
    "load_data_targets_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1693906069469,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "2M2g7PXlq3tx"
   },
   "outputs": [],
   "source": [
    "#drop the first row as it is string\n",
    "load_data_targets_test=load_data_targets_test.drop(load_data_targets_test.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 699,
     "status": "ok",
     "timestamp": 1693906071979,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "ca0769Ecq3ty"
   },
   "outputs": [],
   "source": [
    "#select only the important features we have created before in preprocessing steps and create a list of those features\n",
    "\n",
    "feature_selection=[\n",
    "'grade:A',\n",
    "'grade:B',\n",
    "'grade:C',\n",
    "'grade:D',\n",
    "'grade:E',\n",
    "'grade:F',\n",
    "'grade:G',\n",
    "'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "'home_ownership:OWN',\n",
    "'home_ownership:MORTGAGE',\n",
    "'addr_state:ND_NE_IA_NC_FL_HI_AL',\n",
    "'addr_state:NM_VA',\n",
    "'addr_state:OK_TN_MO_LA_MD_NC',\n",
    "'addr_state:UT_KY_AZ_NJ',\n",
    "'addr_state:AR_MI_PA_OH_MN',\n",
    "'addr_state:RI_MA_DE_SD_IN',\n",
    "'addr_state:GA_WA_OR',\n",
    "'addr_state:WI_MT',\n",
    "'addr_state:IL_CT',\n",
    "'addr_state:KS_SC_CO_VT_AK_MS',\n",
    "'addr_state:WV_NH_WY_DC_ME_ID',\n",
    "'verification_status:Verified',\n",
    "'verification_status:Source Verified',\n",
    "'verification_status:Not Verified',\n",
    "    'purpose:educ_sm_b_wedd_ren_en_mov_house',\n",
    "    'purpose:small_business',\n",
    "    'purpose:educational',\n",
    "    'purpose:renewable_energy',\n",
    "    'purpose:moving',\n",
    "    'purpose:house',\n",
    "    'purpose:other',\n",
    "    'purpose:medical',\n",
    "    'purpose:wedding',\n",
    "    'purpose:vacation',\n",
    "    'purpose:debt_consolidation',\n",
    "    'purpose:home_improvement',\n",
    "    'purpose:major_purchase',\n",
    "    'purpose:car',\n",
    "    'purpose:credit_card',\n",
    "    'initial_list_status:f',\n",
    "    'initial_list_status:w',\n",
    "'term:36',\n",
    "'term:60',\n",
    "'emp_length:0',\n",
    "'emp_length:1',\n",
    "'emp_length:2-4',\n",
    "'emp_length:5-6',\n",
    "'emp_length:7-9',\n",
    "'emp_length:10',\n",
    "'mths_since_issued_date:<38',\n",
    "'mths_since_issued_date:38-39',\n",
    "'mths_since_issued_date:40-41',\n",
    "'mths_since_issued_date:42-48',\n",
    "'mths_since_issued_date:49-52',\n",
    "'mths_since_issued_date:53-64',\n",
    "'mths_since_issued_date:65-84',\n",
    "'mths_since_issued_date:>84',\n",
    "'int_rate:<9.548',\n",
    "'int_rate:9.548-12.025',\n",
    "'int_rate:12.025-15.74',\n",
    "'int_rate:15.74-20.281',\n",
    "'int_rate:20.281',\n",
    "    'mths_since_earliest_cr_line:<140',\n",
    "    'mths_since_earliest_cr_line:140-239',\n",
    "    'mths_since_earliest_cr_line:240-339',\n",
    "    'mths_since_earliest_cr_line:340-439',\n",
    "    'mths_since_earliest_cr_line:440-539',\n",
    "    'mths_since_earliest_cr_line:540-600',\n",
    "    'mths_since_earliest_cr_line:>600',\n",
    "    'delinq_2yrs:0',\n",
    "    'delinq_2yrs:0-3',\n",
    "    'delinq_2yrs:3-7',\n",
    "    'delinq_2yrs:7-9',\n",
    "    'delinq_2yrs:9-11',\n",
    "    'delinq_2yrs:11-12',\n",
    "    'delinq_2yrs:12-18',\n",
    "    'delinq_2yrs:>18',\n",
    "    'inq_last_6mths:0',\n",
    "    'inq_last_6mths:0-3',\n",
    "    'inq_last_6mths:3-7',\n",
    "    'inq_last_6mths:7-9',\n",
    "    'inq_last_6mths:9-11',\n",
    "    'inq_last_6mths:11-12',\n",
    "    'inq_last_6mths:12-18',\n",
    "    'inq_last_6mths:18',\n",
    "    'open_acc:=0',\n",
    "    'open_acc:0-1',\n",
    "    'open_acc:1-4',\n",
    "    'open_acc:4-28',\n",
    "    'open_acc:28-35',\n",
    "    'open_acc:35-45',\n",
    "    'open_acc:>45',\n",
    "    'pub_rec:0-3',\n",
    "    'pub_rec:3-6',\n",
    "    'pub_rec:6-8',\n",
    "    'pub_rec:8',\n",
    "    'total_acc:<0',\n",
    "    'total_acc:0-4',\n",
    "    'total_acc:4-62',\n",
    "    'total_acc:62-72',\n",
    "    'total_acc:72-85',\n",
    "    'total_acc:>85',\n",
    "    'acc_now_delinq:<0',\n",
    "    'acc_now_delinq:0-1',\n",
    "    'acc_now_delinq:1-2',\n",
    "    'acc_now_delinq:2-3',\n",
    "    'acc_now_delinq:3-5',\n",
    "    'acc_now_delinq:>5',\n",
    "'annual_inc:<20k',\n",
    "'annual_inc:20k-30k',\n",
    "'annual_inc:30k-40k',\n",
    "'annual_inc:40k-50k',\n",
    "'annual_inc:50k-60k',\n",
    "'annual_inc:60k-70k',\n",
    "'annual_inc:70k-80k',\n",
    "'annual_inc:80k-90k',\n",
    "'annual_inc:90k-100k',\n",
    "'annual_inc:100k-120k',\n",
    "'annual_inc:120k-140k',\n",
    "'annual_inc:>140k',\n",
    "'total_rev_hi_lim:<=5k',\n",
    "'total_rev_hi_lim:5k_10k',\n",
    "'total_rev_hi_lim:10k_20k',\n",
    "'total_rev_hi_lim:20k_30k',\n",
    "'total_rev_hi_lim:30k_40k',\n",
    "'total_rev_hi_lim:40k_50k',\n",
    "'total_rev_hi_lim:50k_65k',\n",
    "'total_rev_hi_lim:65k_80k',\n",
    "'total_rev_hi_lim:>80k',\n",
    "'dti:<5',\n",
    "'dti:5-10',\n",
    "'dti:10-15',\n",
    "'dti:15-20',\n",
    "'dti:20-25',\n",
    "'dti:25-30',\n",
    "'dti:30-35',\n",
    "'dti:35-40',\n",
    "'dti:>40',\n",
    "'mths_since_last_delinq:Missing',\n",
    "'mths_since_last_delinq:0-3',\n",
    "'mths_since_last_delinq:4-30',\n",
    "'mths_since_last_delinq:31-56',\n",
    "'mths_since_last_delinq:>=57',\n",
    "'mths_since_last_record:Missing',\n",
    "'mths_since_last_record:0-90',\n",
    "'mths_since_last_record:90-120',\n",
    "'mths_since_last_record:120-127',\n",
    "'mths_since_last_record:>=127','loan_amnt','funded_amnt','int_rate','installment']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 517,
     "status": "ok",
     "timestamp": 1693906081457,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "btsXxSLUq3tz"
   },
   "outputs": [],
   "source": [
    "#assign them into a inputs_train_with_ref_category with only those columns which are in the list(fetaure selection)\n",
    "inputs_train_with_ref_category = load_data_inputs_train.reindex(feature_selection, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 708,
     "status": "ok",
     "timestamp": 1693906084288,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "CM6_dPMTq3tz",
    "outputId": "9e7eb8e4-3953-49ff-9807-b8f736e62360"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 373028 entries, 427211 to 121958\n",
      "Columns: 152 entries, grade:A to installment\n",
      "dtypes: float64(2), int64(150)\n",
      "memory usage: 435.4 MB\n"
     ]
    }
   ],
   "source": [
    "#check the basic info\n",
    "inputs_train_with_ref_category.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "executionInfo": {
     "elapsed": 681,
     "status": "ok",
     "timestamp": 1693906087746,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "GJcRwuTrq3tz",
    "outputId": "7d216ad3-d689-482c-da8b-a6f51d2d0608"
   },
   "outputs": [
    {
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       "    <div class=\"colab-df-buttons\">\n",
       "\n",
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       "\n",
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       "\n",
       "    .colab-df-buttons div {\n",
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       "    [theme=dark] .colab-df-convert {\n",
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       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "\n",
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       "        document.querySelector('#df-3c4da795-6373-4eb0-bd72-33052f807f35 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\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",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
       "\n",
       "<div id=\"df-a376a2ab-15b3-4828-bd58-e544ed621197\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a376a2ab-15b3-4828-bd58-e544ed621197')\"\n",
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       "            style=\"display:none;\">\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a376a2ab-15b3-4828-bd58-e544ed621197 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  grade:G  \\\n",
       "427211        1        0        0        0        0        0        0   \n",
       "206088        0        0        1        0        0        0        0   \n",
       "136020        1        0        0        0        0        0        0   \n",
       "412305        0        0        0        1        0        0        0   \n",
       "36159         0        0        1        0        0        0        0   \n",
       "\n",
       "        home_ownership:RENT_OTHER_NONE_ANY  home_ownership:OWN  \\\n",
       "427211                                   0                   0   \n",
       "206088                                   0                   0   \n",
       "136020                                   0                   0   \n",
       "412305                                   1                   0   \n",
       "36159                                    0                   0   \n",
       "\n",
       "        home_ownership:MORTGAGE  ...  mths_since_last_delinq:>=57  \\\n",
       "427211                        1  ...                            0   \n",
       "206088                        1  ...                            0   \n",
       "136020                        1  ...                            0   \n",
       "412305                        0  ...                            0   \n",
       "36159                         1  ...                            0   \n",
       "\n",
       "        mths_since_last_record:Missing  mths_since_last_record:0-90  \\\n",
       "427211                               1                            0   \n",
       "206088                               1                            0   \n",
       "136020                               1                            0   \n",
       "412305                               1                            0   \n",
       "36159                                1                            0   \n",
       "\n",
       "        mths_since_last_record:90-120  mths_since_last_record:120-127  \\\n",
       "427211                              0                               0   \n",
       "206088                              0                               0   \n",
       "136020                              0                               0   \n",
       "412305                              0                               0   \n",
       "36159                               0                               0   \n",
       "\n",
       "        mths_since_last_record:>=127  loan_amnt  funded_amnt  int_rate  \\\n",
       "427211                             0      24000        24000      8.90   \n",
       "206088                             0      10000        10000     14.33   \n",
       "136020                             0      20425        20425      8.90   \n",
       "412305                             0      17200        17200     16.59   \n",
       "36159                              0       8400         8400     12.84   \n",
       "\n",
       "        installment  \n",
       "427211       762.08  \n",
       "206088       343.39  \n",
       "136020       648.56  \n",
       "412305       609.73  \n",
       "36159        282.40  \n",
       "\n",
       "[5 rows x 152 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the head of inputs_train_with_ref_category\n",
    "inputs_train_with_ref_category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 569,
     "status": "ok",
     "timestamp": 1693906092953,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "PkQOjrF4q3tz"
   },
   "outputs": [],
   "source": [
    "#create a list of reference categories selecting only one column from each parent column\n",
    "ref_categories=['grade:G',\n",
    "               'home_ownership:RENT_OTHER_NONE_ANY',\n",
    "               'addr_state:ND_NE_IA_NC_FL_HI_AL',\n",
    "               'verification_status:Verified',\n",
    "                'purpose:educ_sm_b_wedd_ren_en_mov_house',\n",
    "                'initial_list_status:f',\n",
    "                'term:60',\n",
    "                'emp_length:0',\n",
    "                'mths_since_issued_date:>84',\n",
    "                'int_rate:20.281',\n",
    "                'mths_since_earliest_cr_line:<140',\n",
    "                'delinq_2yrs:>18',\n",
    "                'inq_last_6mths:7-9',\n",
    "                'open_acc:=0',\n",
    "                'pub_rec:0-3',\n",
    "                'total_acc:>85',\n",
    "                'acc_now_delinq:<0',\n",
    "                'total_rev_hi_lim:<=5k',\n",
    "                'annual_inc:<20k',\n",
    "                'dti:>40',\n",
    "                'mths_since_last_delinq:0-3',\n",
    "                'mths_since_last_record:Missing'\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "executionInfo": {
     "elapsed": 859,
     "status": "ok",
     "timestamp": 1693906097770,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "Q3UUnSiDq3t0",
    "outputId": "18c178ad-f968-4535-d4fb-955e19350412"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>grade:A</th>\n",
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       "      <th>grade:D</th>\n",
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       "      <th>loan_amnt</th>\n",
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       "      <th>206088</th>\n",
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       "      <td>14.33</td>\n",
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       "      <th>136020</th>\n",
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       "      <th>412305</th>\n",
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       "      <td>609.73</td>\n",
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       "<p>5 rows × 130 columns</p>\n",
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       "        document.querySelector('#df-8a684467-dcd9-4685-b95b-766444441057 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const element = document.querySelector('#df-8a684467-dcd9-4685-b95b-766444441057');\n",
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       "                                                    [key], {});\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "\n",
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       "\n",
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       "\n",
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       "  }\n",
       "\n",
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       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-709a64c2-c5a6-4b45-8c11-28518ef22f6d button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
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      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "427211        1        0        0        0        0        0   \n",
       "206088        0        0        1        0        0        0   \n",
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       "412305        0        0        0        1        0        0   \n",
       "36159         0        0        1        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "427211                   0                        1                 0   \n",
       "206088                   0                        1                 0   \n",
       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "\n",
       "        addr_state:OK_TN_MO_LA_MD_NC  ...  mths_since_last_delinq:31-56  \\\n",
       "427211                             0  ...                             0   \n",
       "206088                             0  ...                             0   \n",
       "136020                             0  ...                             1   \n",
       "412305                             0  ...                             0   \n",
       "36159                              0  ...                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:0-90  \\\n",
       "427211                            0                            0   \n",
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       "136020                            0                            0   \n",
       "412305                            0                            0   \n",
       "36159                             0                            0   \n",
       "\n",
       "        mths_since_last_record:90-120  mths_since_last_record:120-127  \\\n",
       "427211                              0                               0   \n",
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       "136020                              0                               0   \n",
       "412305                              0                               0   \n",
       "36159                               0                               0   \n",
       "\n",
       "        mths_since_last_record:>=127  loan_amnt  funded_amnt  int_rate  \\\n",
       "427211                             0      24000        24000      8.90   \n",
       "206088                             0      10000        10000     14.33   \n",
       "136020                             0      20425        20425      8.90   \n",
       "412305                             0      17200        17200     16.59   \n",
       "36159                              0       8400         8400     12.84   \n",
       "\n",
       "        installment  \n",
       "427211       762.08  \n",
       "206088       343.39  \n",
       "136020       648.56  \n",
       "412305       609.73  \n",
       "36159        282.40  \n",
       "\n",
       "[5 rows x 130 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop all the reference column from inputs_train_with_ref_category\n",
    "inputs_train=inputs_train_with_ref_category.drop(ref_categories,axis=1)\n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "olZWz0Ulq3t0"
   },
   "source": [
    "PD MODLE ESTIMATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W9Iaut_0q3t1"
   },
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 1205,
     "status": "ok",
     "timestamp": 1693906105641,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "KnH23Mfcq3t1"
   },
   "outputs": [],
   "source": [
    "#import logistic regression and metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 505,
     "status": "ok",
     "timestamp": 1693906106144,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "8wg5xQs7q3t1"
   },
   "outputs": [],
   "source": [
    "#create an object of logistic regression with max_iter=200\n",
    "reg = LogisticRegression(max_iter=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "executionInfo": {
     "elapsed": 492,
     "status": "ok",
     "timestamp": 1693906109488,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "Clc7UY34q3t2",
    "outputId": "73f2d759-624f-4d52-8b56-9f163260d369"
   },
   "outputs": [
    {
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       "\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "        console.error('Error during call to suggestCharts:', error);\n",
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      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
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       "427211                   0                        1                 0   \n",
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       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "\n",
       "        addr_state:OK_TN_MO_LA_MD_NC  ...  mths_since_last_delinq:31-56  \\\n",
       "427211                             0  ...                             0   \n",
       "206088                             0  ...                             0   \n",
       "136020                             0  ...                             1   \n",
       "412305                             0  ...                             0   \n",
       "36159                              0  ...                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:0-90  \\\n",
       "427211                            0                            0   \n",
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       "136020                            0                            0   \n",
       "412305                            0                            0   \n",
       "36159                             0                            0   \n",
       "\n",
       "        mths_since_last_record:90-120  mths_since_last_record:120-127  \\\n",
       "427211                              0                               0   \n",
       "206088                              0                               0   \n",
       "136020                              0                               0   \n",
       "412305                              0                               0   \n",
       "36159                               0                               0   \n",
       "\n",
       "        mths_since_last_record:>=127  loan_amnt  funded_amnt  int_rate  \\\n",
       "427211                             0      24000        24000      8.90   \n",
       "206088                             0      10000        10000     14.33   \n",
       "136020                             0      20425        20425      8.90   \n",
       "412305                             0      17200        17200     16.59   \n",
       "36159                              0       8400         8400     12.84   \n",
       "\n",
       "        installment  \n",
       "427211       762.08  \n",
       "206088       343.39  \n",
       "136020       648.56  \n",
       "412305       609.73  \n",
       "36159        282.40  \n",
       "\n",
       "[5 rows x 130 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the head of inputs_train dataframe\n",
    "inputs_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 507,
     "status": "ok",
     "timestamp": 1693906116338,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "0Ea0xcIxroQe"
   },
   "outputs": [],
   "source": [
    "#to display max columns\n",
    "pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 620,
     "status": "ok",
     "timestamp": 1693906119620,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "y1YBIYSuroQf",
    "outputId": "9d4ad814-e418-47e2-e298-84259c02d54c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade:A                                0\n",
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       "grade:C                                0\n",
       "grade:D                                0\n",
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       "total_acc:<0                           0\n",
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       "total_acc:4-62                         0\n",
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       "total_rev_hi_lim:50k_65k               0\n",
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       "total_rev_hi_lim:>80k                  0\n",
       "dti:<5                                 0\n",
       "dti:5-10                               0\n",
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       "dti:15-20                              0\n",
       "dti:20-25                              0\n",
       "dti:25-30                              0\n",
       "dti:30-35                              0\n",
       "dti:35-40                              0\n",
       "mths_since_last_delinq:Missing         0\n",
       "mths_since_last_delinq:4-30            0\n",
       "mths_since_last_delinq:31-56           0\n",
       "mths_since_last_delinq:>=57            0\n",
       "mths_since_last_record:0-90            0\n",
       "mths_since_last_record:90-120          0\n",
       "mths_since_last_record:120-127         0\n",
       "mths_since_last_record:>=127           0\n",
       "loan_amnt                              0\n",
       "funded_amnt                            0\n",
       "int_rate                               0\n",
       "installment                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values in training\n",
    "inputs_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1693906126029,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "_ngeyYw8q3t3"
   },
   "outputs": [],
   "source": [
    "#convert the inputs_train into float\n",
    "inputs_train=inputs_train.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 490,
     "status": "ok",
     "timestamp": 1693906128777,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "ZS4ZymSZq3t4"
   },
   "outputs": [],
   "source": [
    "#convert the load_data_targets_train into float\n",
    "load_data_targets_train=load_data_targets_train.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "executionInfo": {
     "elapsed": 16518,
     "status": "ok",
     "timestamp": 1693906147337,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "06kR6-t7q3t4",
    "outputId": "06a133fb-ebaf-4298-c521-dda6d92ceb9e"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit the LR object onto the inputs_train,load_data_targets_train\n",
    "reg.fit(inputs_train,load_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QfExNegPtkRa"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 503,
     "status": "ok",
     "timestamp": 1693906281549,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "xJLRXx9aroQ2"
   },
   "outputs": [],
   "source": [
    "#preparing the test data with only those variables which are in feature selection\n",
    "inputs_test_with_ref_category= load_data_inputs_test.reindex(feature_selection, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "executionInfo": {
     "elapsed": 643,
     "status": "ok",
     "timestamp": 1693906285543,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "U6FBk1fIroQ3",
    "outputId": "50785e42-bd31-4939-82f8-b87ad69f50e8"
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17200</td>\n",
       "      <td>17200</td>\n",
       "      <td>16.59</td>\n",
       "      <td>609.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36159</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8400</td>\n",
       "      <td>8400</td>\n",
       "      <td>12.84</td>\n",
       "      <td>282.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 130 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0de57614-0c1e-462f-b16e-1a5c6a9729a7')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-0de57614-0c1e-462f-b16e-1a5c6a9729a7 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-0de57614-0c1e-462f-b16e-1a5c6a9729a7');\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",
       "\n",
       "\n",
       "<div id=\"df-a0fb87c2-7d7a-44ef-bc3e-4a24a70737f9\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0fb87c2-7d7a-44ef-bc3e-4a24a70737f9')\"\n",
       "            title=\"Suggest charts.\"\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",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-a0fb87c2-7d7a-44ef-bc3e-4a24a70737f9 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "        grade:A  grade:B  grade:C  grade:D  grade:E  grade:F  \\\n",
       "427211        1        0        0        0        0        0   \n",
       "206088        0        0        1        0        0        0   \n",
       "136020        1        0        0        0        0        0   \n",
       "412305        0        0        0        1        0        0   \n",
       "36159         0        0        1        0        0        0   \n",
       "\n",
       "        home_ownership:OWN  home_ownership:MORTGAGE  addr_state:NM_VA  \\\n",
       "427211                   0                        1                 0   \n",
       "206088                   0                        1                 0   \n",
       "136020                   0                        1                 0   \n",
       "412305                   0                        0                 0   \n",
       "36159                    0                        1                 0   \n",
       "\n",
       "        addr_state:OK_TN_MO_LA_MD_NC  ...  mths_since_last_delinq:31-56  \\\n",
       "427211                             0  ...                             0   \n",
       "206088                             0  ...                             0   \n",
       "136020                             0  ...                             1   \n",
       "412305                             0  ...                             0   \n",
       "36159                              0  ...                             0   \n",
       "\n",
       "        mths_since_last_delinq:>=57  mths_since_last_record:0-90  \\\n",
       "427211                            0                            0   \n",
       "206088                            0                            0   \n",
       "136020                            0                            0   \n",
       "412305                            0                            0   \n",
       "36159                             0                            0   \n",
       "\n",
       "        mths_since_last_record:90-120  mths_since_last_record:120-127  \\\n",
       "427211                              0                               0   \n",
       "206088                              0                               0   \n",
       "136020                              0                               0   \n",
       "412305                              0                               0   \n",
       "36159                               0                               0   \n",
       "\n",
       "        mths_since_last_record:>=127  loan_amnt  funded_amnt  int_rate  \\\n",
       "427211                             0      24000        24000      8.90   \n",
       "206088                             0      10000        10000     14.33   \n",
       "136020                             0      20425        20425      8.90   \n",
       "412305                             0      17200        17200     16.59   \n",
       "36159                              0       8400         8400     12.84   \n",
       "\n",
       "        installment  \n",
       "427211       762.08  \n",
       "206088       343.39  \n",
       "136020       648.56  \n",
       "412305       609.73  \n",
       "36159        282.40  \n",
       "\n",
       "[5 rows x 130 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the reference categories in the inputs_test_with_ref_category\n",
    "inputs_test= inputs_test_with_ref_category.drop(ref_categories,axis=1)\n",
    "\n",
    "#check the inputs_test dataframe\n",
    "inputs_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 753,
     "status": "ok",
     "timestamp": 1693906290953,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "J2gFQWpbroQ3",
    "outputId": "4c61d66b-e3e3-4ab5-ce0e-52199a4f4e1e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade:A                                0\n",
       "grade:B                                0\n",
       "grade:C                                0\n",
       "grade:D                                0\n",
       "grade:E                                0\n",
       "grade:F                                0\n",
       "home_ownership:OWN                     0\n",
       "home_ownership:MORTGAGE                0\n",
       "addr_state:NM_VA                       0\n",
       "addr_state:OK_TN_MO_LA_MD_NC           0\n",
       "addr_state:UT_KY_AZ_NJ                 0\n",
       "addr_state:AR_MI_PA_OH_MN              0\n",
       "addr_state:RI_MA_DE_SD_IN              0\n",
       "addr_state:GA_WA_OR                    0\n",
       "addr_state:WI_MT                       0\n",
       "addr_state:IL_CT                       0\n",
       "addr_state:KS_SC_CO_VT_AK_MS           0\n",
       "addr_state:WV_NH_WY_DC_ME_ID           0\n",
       "verification_status:Source Verified    0\n",
       "verification_status:Not Verified       0\n",
       "purpose:small_business                 0\n",
       "purpose:educational                    0\n",
       "purpose:renewable_energy               0\n",
       "purpose:moving                         0\n",
       "purpose:house                          0\n",
       "purpose:other                          0\n",
       "purpose:medical                        0\n",
       "purpose:wedding                        0\n",
       "purpose:vacation                       0\n",
       "purpose:debt_consolidation             0\n",
       "purpose:home_improvement               0\n",
       "purpose:major_purchase                 0\n",
       "purpose:car                            0\n",
       "purpose:credit_card                    0\n",
       "initial_list_status:w                  0\n",
       "term:36                                0\n",
       "emp_length:1                           0\n",
       "emp_length:2-4                         0\n",
       "emp_length:5-6                         0\n",
       "emp_length:7-9                         0\n",
       "emp_length:10                          0\n",
       "mths_since_issued_date:<38             0\n",
       "mths_since_issued_date:38-39           0\n",
       "mths_since_issued_date:40-41           0\n",
       "mths_since_issued_date:42-48           0\n",
       "mths_since_issued_date:49-52           0\n",
       "mths_since_issued_date:53-64           0\n",
       "mths_since_issued_date:65-84           0\n",
       "int_rate:<9.548                        0\n",
       "int_rate:9.548-12.025                  0\n",
       "int_rate:12.025-15.74                  0\n",
       "int_rate:15.74-20.281                  0\n",
       "mths_since_earliest_cr_line:140-239    0\n",
       "mths_since_earliest_cr_line:240-339    0\n",
       "mths_since_earliest_cr_line:340-439    0\n",
       "mths_since_earliest_cr_line:440-539    0\n",
       "mths_since_earliest_cr_line:540-600    0\n",
       "mths_since_earliest_cr_line:>600       0\n",
       "delinq_2yrs:0                          0\n",
       "delinq_2yrs:0-3                        0\n",
       "delinq_2yrs:3-7                        0\n",
       "delinq_2yrs:7-9                        0\n",
       "delinq_2yrs:9-11                       0\n",
       "delinq_2yrs:11-12                      0\n",
       "delinq_2yrs:12-18                      0\n",
       "inq_last_6mths:0                       0\n",
       "inq_last_6mths:0-3                     0\n",
       "inq_last_6mths:3-7                     0\n",
       "inq_last_6mths:9-11                    0\n",
       "inq_last_6mths:11-12                   0\n",
       "inq_last_6mths:12-18                   0\n",
       "inq_last_6mths:18                      0\n",
       "open_acc:0-1                           0\n",
       "open_acc:1-4                           0\n",
       "open_acc:4-28                          0\n",
       "open_acc:28-35                         0\n",
       "open_acc:35-45                         0\n",
       "open_acc:>45                           0\n",
       "pub_rec:3-6                            0\n",
       "pub_rec:6-8                            0\n",
       "pub_rec:8                              0\n",
       "total_acc:<0                           0\n",
       "total_acc:0-4                          0\n",
       "total_acc:4-62                         0\n",
       "total_acc:62-72                        0\n",
       "total_acc:72-85                        0\n",
       "acc_now_delinq:0-1                     0\n",
       "acc_now_delinq:1-2                     0\n",
       "acc_now_delinq:2-3                     0\n",
       "acc_now_delinq:3-5                     0\n",
       "acc_now_delinq:>5                      0\n",
       "annual_inc:20k-30k                     0\n",
       "annual_inc:30k-40k                     0\n",
       "annual_inc:40k-50k                     0\n",
       "annual_inc:50k-60k                     0\n",
       "annual_inc:60k-70k                     0\n",
       "annual_inc:70k-80k                     0\n",
       "annual_inc:80k-90k                     0\n",
       "annual_inc:90k-100k                    0\n",
       "annual_inc:100k-120k                   0\n",
       "annual_inc:120k-140k                   0\n",
       "annual_inc:>140k                       0\n",
       "total_rev_hi_lim:5k_10k                0\n",
       "total_rev_hi_lim:10k_20k               0\n",
       "total_rev_hi_lim:20k_30k               0\n",
       "total_rev_hi_lim:30k_40k               0\n",
       "total_rev_hi_lim:40k_50k               0\n",
       "total_rev_hi_lim:50k_65k               0\n",
       "total_rev_hi_lim:65k_80k               0\n",
       "total_rev_hi_lim:>80k                  0\n",
       "dti:<5                                 0\n",
       "dti:5-10                               0\n",
       "dti:10-15                              0\n",
       "dti:15-20                              0\n",
       "dti:20-25                              0\n",
       "dti:25-30                              0\n",
       "dti:30-35                              0\n",
       "dti:35-40                              0\n",
       "mths_since_last_delinq:Missing         0\n",
       "mths_since_last_delinq:4-30            0\n",
       "mths_since_last_delinq:31-56           0\n",
       "mths_since_last_delinq:>=57            0\n",
       "mths_since_last_record:0-90            0\n",
       "mths_since_last_record:90-120          0\n",
       "mths_since_last_record:120-127         0\n",
       "mths_since_last_record:>=127           0\n",
       "loan_amnt                              0\n",
       "funded_amnt                            0\n",
       "int_rate                               0\n",
       "installment                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values in inputs test\n",
    "inputs_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "executionInfo": {
     "elapsed": 2745,
     "status": "ok",
     "timestamp": 1693906299013,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "yXRux7a1roQ4"
   },
   "outputs": [],
   "source": [
    "#predict the inputs_test\n",
    "y_hat_test = reg.predict(inputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1693906300936,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "PXPRJQJIroQ4"
   },
   "outputs": [],
   "source": [
    "#astype the y_hat_test into int\n",
    "a=y_hat_test.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "executionInfo": {
     "elapsed": 613,
     "status": "ok",
     "timestamp": 1693906303756,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "2R3bKuMcroQ5"
   },
   "outputs": [],
   "source": [
    "#predict the predict_proba of inputs_test\n",
    "y_hat_test_proba = reg.predict_proba(inputs_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 478,
     "status": "ok",
     "timestamp": 1693906308644,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "08cRH_H1roQ5",
    "outputId": "857f293d-6aa2-4705-8810-8be825a34b06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06379614, 0.93620386],\n",
       "       [0.12258813, 0.87741187],\n",
       "       [0.07356947, 0.92643053],\n",
       "       ...,\n",
       "       [0.06015295, 0.93984705],\n",
       "       [0.05209354, 0.94790646],\n",
       "       [0.0450374 , 0.9549626 ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the y_hat_test_proba\n",
    "y_hat_test_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 485,
     "status": "ok",
     "timestamp": 1693906311836,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "mRgGPIWcroQ5",
    "outputId": "8d52618b-8799-480d-86a7-00d58165deae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93620386, 0.87741187, 0.92643053, ..., 0.93984705, 0.94790646,\n",
       "       0.9549626 ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take only one column from the y_hat_test_proba\n",
    "y_hat_test_proba[: ][: ,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1693906315412,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "HlPkm3NDroQ6"
   },
   "outputs": [],
   "source": [
    "#take only one column from the y_hat_test_proba\n",
    "y_hat_test_proba=y_hat_test_proba[: ][: ,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1693906319964,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "B8yZljMwtkUT"
   },
   "outputs": [],
   "source": [
    "#assign load_data_targets_test into a new variable\n",
    "loan_data_targets_test_temp = load_data_targets_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1693906322249,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "2LvFU6S2tkXS"
   },
   "outputs": [],
   "source": [
    "#reset the index of load_data_targets_test_temp\n",
    "loan_data_targets_test_temp.reset_index(drop= True, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1693906326358,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "sxQEgTFEtkaI"
   },
   "outputs": [],
   "source": [
    "#concat the original output and predicted probability in one dataframe\n",
    "df_actual_predicted_probs = pd.concat([loan_data_targets_test_temp,pd.DataFrame(y_hat_test_proba)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1693906326904,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "BaJQjNJttkcr",
    "outputId": "3c6fad41-dcfb-4526-ae84-239f2de65ca5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373028, 2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#chekc the shape of df_actual_predicted_probs\n",
    "df_actual_predicted_probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1693906328863,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "ndTzmMHmtkfW"
   },
   "outputs": [],
   "source": [
    "#change the column names into meaningful names\n",
    "df_actual_predicted_probs.columns = ['load_data_targets_test','y_hat_test_proba']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1693906330721,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "mKb7CuaSroQ8"
   },
   "outputs": [],
   "source": [
    "#match both the index\n",
    "df_actual_predicted_probs.index=load_data_inputs_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 680,
     "status": "ok",
     "timestamp": 1693906356089,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "DeZqA7dgroQ8",
    "outputId": "dfdab36e-6297-4231-c81b-ed0987bdaa17"
   },
   "outputs": [
    {
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      "text/plain": [
       "       load_data_targets_test  y_hat_test_proba\n",
       "427211                      1          0.936204\n",
       "206088                      1          0.877412\n",
       "136020                      1          0.926431\n",
       "412305                      1          0.774372\n",
       "36159                       1          0.873116"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the head of df_actual_predicted_probs\n",
    "df_actual_predicted_probs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1693906360783,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "_uOiGZF0roQ9"
   },
   "outputs": [],
   "source": [
    "#take the threshold as 0.9 as assign 0 and 1 accordingly using the np.where function\n",
    "tr =0.9\n",
    "df_actual_predicted_probs['y_hat_test']=np.where(df_actual_predicted_probs['y_hat_test_proba']>tr,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1693906361303,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "iTuPcK_TroQ9",
    "outputId": "12cac55e-ad71-4a6e-8b17-9ad07f79e454"
   },
   "outputs": [
    {
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       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4504</td>\n",
       "      <td>5686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36926</td>\n",
       "      <td>46141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
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       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
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       "      background-color: #E8F0FE;\n",
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       "      border-radius: 50%;\n",
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       "      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",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-6627db80-160d-4b44-98eb-0e2781b2a5ff 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-6627db80-160d-4b44-98eb-0e2781b2a5ff');\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",
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       "\n",
       "\n",
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       "            title=\"Suggest charts.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-47b77d9b-98f5-4494-af12-76abf27b9b57 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           4504   5686\n",
       "1          36926  46141"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the crosstab of actual and prdicted values\n",
    "pd.crosstab(df_actual_predicted_probs['load_data_targets_test'],df_actual_predicted_probs['y_hat_test'],\n",
    "    rownames = ['Actual'], colnames = ['Predicted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pI4wEqTLroQ9"
   },
   "source": [
    "# Generalized function for model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "executionInfo": {
     "elapsed": 604,
     "status": "ok",
     "timestamp": 1693906372780,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "nyMaznSGroQ-"
   },
   "outputs": [],
   "source": [
    "def x_tr_model(model,x_train,y_train,x_test,y_test,tr):\n",
    "    model.fit(x_train,y_train)\n",
    "    pred_y = model.predict(x_test)\n",
    "    y_hat_test_proba = reg.predict_proba(inputs_test)\n",
    "    y_hat_test_proba=y_hat_test_proba[: ][: ,1]\n",
    "    loan_data_targets_test_temp = y_test\n",
    "    loan_data_targets_test_temp.reset_index(drop= True, inplace= True)\n",
    "    df_actual_predicted_probs = pd.concat([loan_data_targets_test_temp,pd.DataFrame(y_hat_test_proba)], axis=1)\n",
    "    df_actual_predicted_probs.columns =  ['load_data_targets_test','y_hat_test_proba']\n",
    "    df_actual_predicted_probs.index=load_data_inputs_test.index\n",
    "    df_actual_predicted_probs['y_hat_test']= np.where(df_actual_predicted_probs['y_hat_test_proba'] > tr, 1,0)\n",
    "\n",
    "    return pd.crosstab(df_actual_predicted_probs['load_data_targets_test'],df_actual_predicted_probs['y_hat_test'],rownames = ['Actual'], colnames = ['Predicted'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 15087,
     "status": "ok",
     "timestamp": 1693906439273,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "y-3EmKxUroQ-",
    "outputId": "65400659-d228-49e3-f673-3421de0cf726"
   },
   "outputs": [
    {
     "data": {
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "  .colab-df-quickchart-complete:disabled,\n",
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       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-8431acbd-77bf-40be-adb5-4046973746ac button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           4504   5686\n",
       "1          36926  46141"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pass the model Logistic Regressions with 150 max_iter\n",
    "x_tr_model(LogisticRegression(max_iter=150),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1693906439274,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "S7xf45f6roQ-"
   },
   "outputs": [],
   "source": [
    "#import models  RandomForest,DT,Gradient Boosting\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 168851,
     "status": "ok",
     "timestamp": 1693906611286,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "FiCKDkPkroQ-",
    "outputId": "1b1790de-ae99-4e1f-8056-12e7c90722d0"
   },
   "outputs": [
    {
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       "<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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
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       "      background-color: #3B4455;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "        document.querySelector('#df-7e47c29a-6508-460a-be63-af0eaae1eb33 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-7e47c29a-6508-460a-be63-af0eaae1eb33');\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",
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       "            title=\"Suggest charts.\"\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",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-f6743dc2-b6aa-4c6f-9599-77a6aff859f7 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted      0      1\n",
       "Actual                 \n",
       "0           2423   7767\n",
       "1          19903  63164"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results from RandomForest\n",
    "x_tr_model(RandomForestClassifier(),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 18091,
     "status": "ok",
     "timestamp": 1693906675571,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "BjGlIsi6roQ_",
    "outputId": "c5595a0c-eef9-46b2-faff-52e2cc1849aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-08aba527-37d3-47df-a6f6-e158bfb6f0f0\" 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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1073</td>\n",
       "      <td>9117</td>\n",
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       "      <td>74467</td>\n",
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       "    <div class=\"colab-df-buttons\">\n",
       "\n",
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       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "      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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-08aba527-37d3-47df-a6f6-e158bfb6f0f0 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-08aba527-37d3-47df-a6f6-e158bfb6f0f0');\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",
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       "\n",
       "\n",
       "<div id=\"df-c2ace3e2-ec51-4c2b-87c5-4a6dbb6fc24c\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2ace3e2-ec51-4c2b-87c5-4a6dbb6fc24c')\"\n",
       "            title=\"Suggest charts.\"\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",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-c2ace3e2-ec51-4c2b-87c5-4a6dbb6fc24c button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted     0      1\n",
       "Actual                \n",
       "0          1073   9117\n",
       "1          8600  74467"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Results from DT\n",
    "x_tr_model(DecisionTreeClassifier(),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 221892,
     "status": "ok",
     "timestamp": 1693906910067,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "scAVUhDVroQ_",
    "outputId": "3c13b9e7-64f4-4016-91a0-fd5a7fa07cbf"
   },
   "outputs": [
    {
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
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       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
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       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
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       "        document.querySelector('#df-f8459bf9-f1c8-48a2-8229-6846591afdbc button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted     0      1\n",
       "Actual                \n",
       "0          1073   9117\n",
       "1          8600  74467"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Results from GBC\n",
    "x_tr_model(GradientBoostingClassifier(),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-b0p6xkptCXw"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dq0NIiAStCzi"
   },
   "source": [
    "### **Hyper-Parameter Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "executionInfo": {
     "elapsed": 488,
     "status": "ok",
     "timestamp": 1693906948835,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "vUMnIjSbroQ_"
   },
   "outputs": [],
   "source": [
    "# Helper function to perform hyper parameter tunning with RandomizedSearchCV\n",
    "def random_Search(model,X_train, Y_train,param_grid):\n",
    "\n",
    "    from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "    # Random search of parameters, using 3 fold cross validation,\n",
    "    # search across 60 different combinations, and use all available cores\n",
    "    random = RandomizedSearchCV(estimator = model, param_distributions = param_grid, n_iter = 10, cv = 2, verbose=2, random_state=42, n_jobs = -1)# Fit the random search model\n",
    "    random.fit(X_train, Y_train)\n",
    "    print(random.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11580,
     "status": "ok",
     "timestamp": 1693906974145,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "W1T0Nn6aroRA",
    "outputId": "1f6282a1-4039-4a86-8401-23534406182a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 10 candidates, totalling 20 fits\n",
      "{'splitter': 'best', 'min_weight_fraction_leaf': 0.1, 'min_samples_leaf': 9, 'max_leaf_nodes': 10, 'max_features': 'auto', 'max_depth': 3}\n"
     ]
    }
   ],
   "source": [
    "# for decision tree classifier\n",
    "# create parameters dict for tunning\n",
    "DTR_para_grid = {\n",
    "                  \"splitter\":[\"best\",\"random\"],\n",
    "            \"max_depth\" : [1,3,12],\n",
    "           \"min_samples_leaf\":[1,4,5,9],\n",
    "           \"min_weight_fraction_leaf\":[0.1,0.3,0,0.7,0.9],\n",
    "           \"max_features\":[\"auto\",\"log2\"],\n",
    "           \"max_leaf_nodes\":[10,40,60,80]\n",
    "              }\n",
    "\n",
    "# passing data for hyper parameter tunning with Randomized search cv\n",
    "random_Search(DecisionTreeClassifier(),inputs_train,load_data_targets_train, DTR_para_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3180807,
     "status": "ok",
     "timestamp": 1693910158287,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "hGPKGzogroRA",
    "outputId": "62c700e4-2c83-4aca-ae74-f5643efbf043"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 10 candidates, totalling 20 fits\n",
      "{'n_estimators': 200, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 66}\n"
     ]
    }
   ],
   "source": [
    "# for random forest classifier\n",
    "# create parameters dict for tunning\n",
    "rf_para_grid = {'n_estimators': [int(x) for x in np.arange(200,500,100)],\n",
    "               'max_features': ['auto', 'sqrt'],\n",
    "               'max_depth': [int(x) for x in np.linspace(10, 80, num = 11)],\n",
    "               'min_samples_leaf': [1, 2, 4]\n",
    "\n",
    "               }\n",
    "\n",
    "# passing data for hyper parameter tunning with Randomized search cv\n",
    "random_Search(RandomForestClassifier(),inputs_train,load_data_targets_train, rf_para_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 1621,
     "status": "ok",
     "timestamp": 1693910549789,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "gTePGcMkqsQg",
    "outputId": "ff0b5cf4-27f1-47e6-dfd7-c09ee9870f26"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1073</td>\n",
       "      <td>9117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8600</td>\n",
       "      <td>74467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21bf60fc-5ec6-4ad4-acb1-71de3d8ec506')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-21bf60fc-5ec6-4ad4-acb1-71de3d8ec506 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-21bf60fc-5ec6-4ad4-acb1-71de3d8ec506');\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",
       "\n",
       "\n",
       "<div id=\"df-5a1cb8f3-8d04-4b3d-99cf-e23cbe953b27\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a1cb8f3-8d04-4b3d-99cf-e23cbe953b27')\"\n",
       "            title=\"Suggest charts.\"\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",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-5a1cb8f3-8d04-4b3d-99cf-e23cbe953b27 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted     0      1\n",
       "Actual                \n",
       "0          1073   9117\n",
       "1          8600  74467"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a DT model with best parameters\n",
    "x_tr_model(DecisionTreeClassifier(splitter= 'best', min_weight_fraction_leaf= 0.1, min_samples_leaf= 9, max_leaf_nodes= 10, max_features='auto', max_depth= 3),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "executionInfo": {
     "elapsed": 348821,
     "status": "ok",
     "timestamp": 1693911246236,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "Mqf-qHhZq1pm",
    "outputId": "9b546798-cacd-4563-acb9-7d31d00a2dfc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-50bbda1b-d037-4bfb-a43b-529b17ee4a88\" 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>Predicted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1073</td>\n",
       "      <td>9117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8600</td>\n",
       "      <td>74467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50bbda1b-d037-4bfb-a43b-529b17ee4a88')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-50bbda1b-d037-4bfb-a43b-529b17ee4a88 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-50bbda1b-d037-4bfb-a43b-529b17ee4a88');\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",
       "\n",
       "\n",
       "<div id=\"df-1a4e2b6b-2576-4bc1-be5c-4bf6b390ccb4\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1a4e2b6b-2576-4bc1-be5c-4bf6b390ccb4')\"\n",
       "            title=\"Suggest charts.\"\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",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-1a4e2b6b-2576-4bc1-be5c-4bf6b390ccb4 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "Predicted     0      1\n",
       "Actual                \n",
       "0          1073   9117\n",
       "1          8600  74467"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a Random Forest model with best parameters\n",
    "x_tr_model(RandomForestClassifier(n_estimators= 200, min_samples_leaf= 2, max_features= 'sqrt', max_depth= 66),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3517551,
     "status": "ok",
     "timestamp": 1693918715201,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "yUDN4YUoq1se",
    "outputId": "365482e6-08b6-4685-92e4-d4e560ee9aec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 10 candidates, totalling 20 fits\n",
      "{'n_estimators': 50, 'max_depth': 1, 'learning_rate': 0.01}\n"
     ]
    }
   ],
   "source": [
    "# for Gradient Boosting classifier\n",
    "# create parameters dict for tunning\n",
    "gb_parameters = {\n",
    "    \"n_estimators\":[50,250,500],\n",
    "    \"max_depth\":[1,3,5,7,9],\n",
    "    \"learning_rate\":[0.01,0.1,1,10]\n",
    "}\n",
    "\n",
    "# passing data for hyper parameter tunning with Randomized search cv\n",
    "random_Search(GradientBoostingClassifier(),inputs_train,load_data_targets_train, gb_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K4FtugGX9asn"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
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    "colab": {
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     "height": 143
    },
    "executionInfo": {
     "elapsed": 47636,
     "status": "ok",
     "timestamp": 1693918910757,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "EwTHYxWvq1vC",
    "outputId": "b8604bf0-7093-40f7-8112-b144013ac9ea"
   },
   "outputs": [
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      "text/plain": [
       "Predicted     0      1\n",
       "Actual                \n",
       "0          1073   9117\n",
       "1          8600  74467"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a Gradient Boosting model with best parameters\n",
    "x_tr_model(GradientBoostingClassifier(n_estimators= 50, max_depth= 1, learning_rate=0.01),inputs_train,load_data_targets_train,inputs_test,load_data_targets_test,0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "executionInfo": {
     "elapsed": 41302,
     "status": "ok",
     "timestamp": 1693918978997,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "OHbAP5iKq1xW",
    "outputId": "38cace04-b337-4591-d155-2ddad3a019bc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.01, max_depth=1, n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.01, max_depth=1, n_estimators=50)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.01, max_depth=1, n_estimators=50)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a model for saving the model in further operation\n",
    "grad_boss = GradientBoostingClassifier(n_estimators= 50, max_depth= 1, learning_rate= 0.01)\n",
    "grad_boss.fit(inputs_train,load_data_targets_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "executionInfo": {
     "elapsed": 594,
     "status": "ok",
     "timestamp": 1693919188376,
     "user": {
      "displayName": "SOUMYA NAYAK",
      "userId": "05998776148464585121"
     },
     "user_tz": -330
    },
    "id": "HWf0uWFHq1zu"
   },
   "outputs": [],
   "source": [
    "#save the gradient boosting model for further use\n",
    "import pickle\n",
    "pickle.dump(grad_boss,open('pd_model_gradient.sav','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "ut7VjyJqq12O"
   },
   "outputs": [],
   "source": []
  }
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