4a-logistic_regression.ipynb

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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"review_history_with_time_features.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ZvZxo-5G6U2z",
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    "outputId": "409c9cbd-64bf-456d-acde-f3f6fed92ff6",
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   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(df['was_remembered'], df['time_since_prev_review_standard'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Sdy2PKFQ4emn",
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   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split(df, test_size = 0.2):\n",
    "  unique_card_ids = df['card_id'].unique()\n",
    "\n",
    "  train_card_ids, test_card_ids = train_test_split(unique_card_ids, test_size=test_size, random_state=42)\n",
    "  df_train = df[df['card_id'].isin(train_card_ids)]\n",
    "  df_test = df[df['card_id'].isin(test_card_ids)]\n",
    "  return df_train, df_test\n",
    "\n",
    "df_train, df_test = split(df, .15)"
   ]
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    "outputId": "784a7e56-5c93-4320-cb16-c228a1e4060e"
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   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(df_train[['time_since_prev_review_standard']], df_train['was_remembered'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "oGeis9b790mT",
   "metadata": {
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   "outputs": [],
   "source": [
    "preds = lr.predict_proba(df_test[['time_since_prev_review_standard']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3SGuo8DH9-_p",
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    },
    "id": "3SGuo8DH9-_p",
    "outputId": "e817b77e-6b51-430e-f654-6c3d56300345"
   },
   "outputs": [],
   "source": [
    "roc_auc_score(df_test[\"was_remembered\"], preds[:, 1])"
   ]
  }
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