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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        pass\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd1aa6d5",
   "metadata": {
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   "source": [
    "# Open file with pd.read_csv\n",
    "df_train = pd.read_csv(\"/kaggle/input/icr-identify-age-related-conditions/train.csv\")\n",
    "df_test = pd.read_csv(\"/kaggle/input/icr-identify-age-related-conditions/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "563c47ff",
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   "source": [
    "# Convert 'A' and 'B' values in 'EJ' column to 0 and 1 respectively\n",
    "df_train['EJ'] = df_train['EJ'].map({'A': 0, 'B': 1})\n",
    "df_test['EJ'] = df_test['EJ'].map({'A': 0, 'B': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af9245ad",
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   "source": [
    "# Split the training data into features (X) and target variable (y)\n",
    "X_train = df_train.drop([\"Class\", \"Id\"], axis=1)  # Exclude non-numeric columns\n",
    "y_train = df_train[\"Class\"]\n",
    "\n",
    "# Split the test data into features (X_test)\n",
    "X_test = df_test.drop(\"Id\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "# Identify columns with missing values\n",
    "columns_with_missing = X_train.columns[X_train.isna().any()].tolist()\n",
    "\n",
    "# Impute missing values with the mean of each column\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "X_train_imputed = imputer.fit_transform(X_train)\n",
    "X_test_imputed = imputer.transform(X_test)\n",
    "\n",
    "# Scale the features using StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train_imputed)\n",
    "X_test_scaled = scaler.transform(X_test_imputed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c337184",
   "metadata": {
    "execution": {
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   "source": [
    "# Get feature importances\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train_scaled, y_train)\n",
    "feature_importances = rfc.feature_importances_\n",
    "\n",
    "# Create a DataFrame for feature importance\n",
    "importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': feature_importances})\n",
    "\n",
    "# Sort the features by importance (descending order)\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce5fddae",
   "metadata": {
    "execution": {
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   "source": [
    "# Select the top important variables\n",
    "num_variables = 10  # Specify the number of top important variables to use\n",
    "important_variables = importance_df['Feature'].tolist()[:num_variables]\n",
    "X_train_important = X_train_scaled[:, importance_df.index[:num_variables]]\n",
    "X_test_important = X_test_scaled[:, importance_df.index[:num_variables]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e746beb",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Train the random forest model using only the important variables\n",
    "rfc_important = RandomForestClassifier()\n",
    "rfc_important.fit(X_train_important, y_train)\n",
    "\n",
    "# Predict on the test set using only the important variables\n",
    "rfc_pred = rfc_important.predict(X_test_important)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13cf4b5b",
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   "source": [
    "# Predict probabilities for each class in the test set\n",
    "rfc_pred_proba = rfc.predict_proba(X_test_scaled)\n",
    "\n",
    "# Create a DataFrame to store the predictions\n",
    "predictions_df = pd.DataFrame({'Id': df_test['Id'],\n",
    "                               'class_0': rfc_pred_proba[:, 0],\n",
    "                               'class_1': rfc_pred_proba[:, 1]})\n",
    "\n",
    "# Save the predictions to a CSV file\n",
    "predictions_df.to_csv('submission.csv', index=False)"
   ]
  }
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