RFC_Model.ipynb

{
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  "metadata": {
    "colab": {
      "name": "RFC Model",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZtuUVpcCEno",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0gBTuTZCPp_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv(\"/content/train_mod.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJMuBPLsIBdW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tr = pd.read_csv(\"/content/train.csv\")\n",
        "y=tr[\"Survived\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XI4MvhrtIBhQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSigtyYyChRw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test = pd.read_csv(\"/content/test_mod.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wURpx3hFDF5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = test\n",
        "X_test = X_test.drop(columns=[\"PassengerId\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNilALBuHlVS",
        "colab_type": "code",
        "outputId": "fd459bb1-f76d-4d8d-d82e-dccc7c9de63c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  Parch     Fare  Embarked  Title  FamilySize\n",
              "0       3    1  22.0      0   7.2500         1      1           2\n",
              "1       1    0  38.0      0  71.2833         2      3           2\n",
              "2       3    0  26.0      0   7.9250         1      4           1\n",
              "3       1    0  35.0      0  53.1000         1      3           2\n",
              "4       3    1  35.0      0   8.0500         1      1           1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcMtnZeXDNNy",
        "colab_type": "code",
        "outputId": "e61b5d03-b10a-44bd-efe2-5bf8512862fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV,StratifiedKFold,cross_val_score,train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "rfc = RandomForestClassifier(random_state=100)\n",
        "param_grid = { \n",
        "    'n_estimators': [200, 500],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4,5,6,7,8],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}\n",
        "kf = StratifiedKFold(n_splits=5)\n",
        "grfc = GridSearchCV(estimator = rfc,param_grid=param_grid,cv=kf)\n",
        "X_train,X_tes,y_train,y_test = train_test_split(train,y,random_state=69)\n",
        "grfc.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
              "             error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False, random_state=100,\n",
              "                                              verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [4, 5, 6, 7, 8],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'n_estimators': [200, 500]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2ZbRFVyKsr0",
        "colab_type": "code",
        "outputId": "3b561e5a-6e16-4ace-acb5-27c8c9f85416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grfc.best_score_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8339131410616092"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6YBB3hiLbns",
        "colab_type": "code",
        "outputId": "f7774311-0d5e-4664-e926-070c7c5c56c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "grfc.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini',\n",
              " 'max_depth': 6,\n",
              " 'max_features': 'log2',\n",
              " 'n_estimators': 200}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8155B9zLfBe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred1 = grfc.predict(X_tes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hU4F_HEuLkAA",
        "colab_type": "code",
        "outputId": "a854c134-9e03-4451-9517-b545d524ee45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy_score(y_test,y_pred1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8475336322869955"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "691ZsEhwLpQM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred1 = grfc.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IF1rxJMtLwfu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rfc_grid= pd.DataFrame({\n",
        "        \"PassengerId\": test[\"PassengerId\"],\n",
        "        \"Survived\": y_pred1\n",
        "    })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9SAdpu7MBQl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rfc_grid.to_csv(\"/content/rfc_grid_data12.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHnKXzsiMI6M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}