mlessentials/Lab04/Exercise4.03/Exercise4_03.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "source": [
    "file_url = '../Dataset/openml_phpZNNasq.csv'"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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   "source": [
    "df = pd.read_csv(file_url)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "outputs": [],
   "source": [
    "df.drop(columns='animal', inplace=True)\n",
    "y = df.pop('type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.4, random_state=188)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "37f61ce7-2919-46e2-b02f-06104b194cad"
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   "source": [
    "rf_model = RandomForestClassifier(random_state=42, n_estimators=30, max_depth=5)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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   "outputs": [],
   "source": [
    "train_preds = rf_model.predict(X_train)\n",
    "test_preds = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "source": [
    "train_acc = accuracy_score(y_train, train_preds)\n",
    "test_acc = accuracy_score(y_test, test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "4aa6f1bf-e512-4414-b65f-5b37550af617"
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   "outputs": [],
   "source": [
    "print(train_acc)\n",
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {
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   "outputs": [],
   "source": [
    "rf_model2 = RandomForestClassifier(random_state=42, n_estimators=30, max_depth=2)\n",
    "rf_model2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "train_preds2 = rf_model2.predict(X_train)\n",
    "test_preds2 = rf_model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "train_acc2 = accuracy_score(y_train, train_preds2)\n",
    "test_acc2 = accuracy_score(y_test, test_preds2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "outputId": "42047520-00c7-46f5-e48e-7ae62738b9b3"
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   "outputs": [],
   "source": [
    "print(train_acc2)\n",
    "print(test_acc2)"
   ]
  }
 ],
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