Random_forest_Classifier_AI.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGJRrkXhbjtW"
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      "source": [
        "Loading Dataset and performing EDA and data pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23097 entries, 0 to 23096\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Diagnosed_Condition       23097 non-null  int64  \n",
            " 1   Treated_with_drugs        23084 non-null  object \n",
            " 2   Patient_Age               23097 non-null  int64  \n",
            " 3   Patient_Body_Mass_Index   23097 non-null  float64\n",
            " 4   Patient_Smoker            23097 non-null  object \n",
            " 5   Patient_Rural_Urban       23097 non-null  object \n",
            " 6   Patient_mental_condition  23097 non-null  object \n",
            " 7   Number_of_prev_cond       21862 non-null  float64\n",
            " 8   Survived_1_year           23097 non-null  int64  \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "pharma_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/pharma_data/Training_set_begs.csv')\n",
        "pharma_data.head()\n",
        "pharma_data = pharma_data[['Diagnosed_Condition','Treated_with_drugs', 'Patient_Age', 'Patient_Body_Mass_Index', 'Patient_Smoker','Patient_Rural_Urban','Patient_mental_condition', 'Number_of_prev_cond','Survived_1_year']]\n",
        "pharma_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYENu0VyB5XS"
      },
      "source": [
        "Converting values to numerical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [
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          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23097 entries, 0 to 23096\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Diagnosed_Condition       23097 non-null  int64  \n",
            " 1   Treated_with_drugs        23097 non-null  float64\n",
            " 2   Patient_Age               23097 non-null  int64  \n",
            " 3   Patient_Body_Mass_Index   23097 non-null  float64\n",
            " 4   Patient_Smoker            23097 non-null  int64  \n",
            " 5   Patient_Rural_Urban       23097 non-null  int64  \n",
            " 6   Patient_mental_condition  23097 non-null  int64  \n",
            " 7   Number_of_prev_cond       23097 non-null  float64\n",
            " 8   Survived_1_year           23097 non-null  int64  \n",
            "dtypes: float64(3), int64(6)\n",
            "memory usage: 1.6 MB\n"
          ]
        }
      ],
      "source": [
        "pharma_data['Patient_Rural_Urban'] = pharma_data['Patient_Rural_Urban'].apply(lambda x: 1 if x == 'RURAL' else 2)\n",
        "pharma_data['Patient_Smoker'] = pharma_data['Patient_Smoker'].apply(lambda x: 4 if x == 'YES' else ( 5 if x == 'NO' else 6))\n",
        "pharma_data['Patient_mental_condition'] = pharma_data['Patient_mental_condition'].apply(lambda x: 1 if x == 'Stable' else 0)\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "ordinal_encoder = OrdinalEncoder()\n",
        "pharma_data[['Treated_with_drugs']] = ordinal_encoder.fit_transform(pharma_data[['Treated_with_drugs']])\n",
        "pharma_data['Treated_with_drugs'].fillna(pharma_data['Treated_with_drugs'].mode()[0], inplace=True)\n",
        "pharma_data['Number_of_prev_cond'].fillna(pharma_data['Number_of_prev_cond'].mode()[0], inplace=True)\n",
        "pharma_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u-S86SXfg6rU"
      },
      "outputs": [],
      "source": [
        "X = pharma_data.drop('Survived_1_year', axis = 1)\n",
        "Y = pharma_data['Survived_1_year']\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30, random_state=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V6QqJ-EHAV2Y"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, make_scorer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IswTMDJeAl9a",
        "outputId": "0416c904-d2c4-4912-c496-dffafb8dd45d"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, min_samples_leaf=2, n_jobs=1)"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#first RF classifier\n",
        "R1 = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
        "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
        "            min_impurity_decrease=0.0,\n",
        "            min_samples_leaf=2, min_samples_split=2,\n",
        "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
        "            oob_score=False, random_state=None, verbose=0,\n",
        "            warm_start=False)\n",
        "R1.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5HX1yGXQ7vn"
      },
      "source": [
        "testing against test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BcORea7QQ_Fc"
      },
      "outputs": [],
      "source": [
        "test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/pharma_data/Testing_set_begs.csv')\n",
        "test_data = test_data[['Diagnosed_Condition','Treated_with_drugs', 'Patient_Age', 'Patient_Body_Mass_Index', 'Patient_Smoker','Patient_Rural_Urban','Patient_mental_condition', 'Number_of_prev_cond']]\n",
        "test_data['Patient_Rural_Urban'] = test_data['Patient_Rural_Urban'].apply(lambda x: 1 if x == 'RURAL' else 2)\n",
        "test_data['Patient_Smoker'] = test_data['Patient_Smoker'].apply(lambda x: 4 if x == 'YES' else ( 5 if x == 'NO' else 6))\n",
        "test_data['Patient_mental_condition'] = test_data['Patient_mental_condition'].apply(lambda x: 1 if x == 'Stable' else 0)\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "ordinal_encoder = OrdinalEncoder()\n",
        "test_data[['Treated_with_drugs']] = ordinal_encoder.fit_transform(test_data[['Treated_with_drugs']])\n",
        "test_data['Treated_with_drugs'].fillna(test_data['Treated_with_drugs'].mode()[0], inplace=True)\n",
        "test_data['Number_of_prev_cond'].fillna(test_data['Number_of_prev_cond'].mode()[0], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNV4vqQiSxi9"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "y_R1 = R1.predict(test_data)\n",
        "test_data['Survived_1_year']=y_R1.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#download files\n",
        "# To create Dataframe of predicted value with particular respective index\n",
        "res = pd.DataFrame(y_R1) #preditcions are nothing but the final predictions of your model on input features of your new unseen test data\n",
        "res.columns = [\"prediction\"]\n",
        "\n",
        "# To download the csv file locally\n",
        "from google.colab import files\n",
        "res.to_csv('prediction_results.csv')         \n",
        "files.download('prediction_results.csv')"
      ],
      "metadata": {
        "colab": {
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