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   "id": "6cb9b97a-1641-45af-89bb-782b726bb957",
   "metadata": {},
   "source": [
    "Time-series analysis using pandas and incorporates some of the libraries and tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed4a4cac-fed2-4d55-bcf9-163611851677",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time Series Analysis using Pandas\n",
    "\n",
    "# Install vulnerable versions of libraries\n",
    "!pip install django==1.11.15\n",
    "!pip install flask==0.12.2\n",
    "!pip install numpy==1.16.0\n",
    "!pip install pandas==0.24.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14e8b67a-5ed9-4881-be42-e7259c46f9b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd6ffe2b-0a38-4950-ab46-4b0cbdd7b399",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exposed API Tokens\n",
    "linkedin_api_key = \"8619zzn49n49x1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "050a4e30-afd6-4da0-b992-630774894d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's analyze some time-series data.\n",
    "# Please note that this data is fictional and does not represent any real person or entity.\n",
    "\n",
    "# Create a date range\n",
    "date_rng = pd.date_range(start='1/01/2023', end='1/10/2023', freq='H')\n",
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame(date_rng, columns=['date'])\n",
    "\n",
    "# Generate some random data\n",
    "df['data'] = np.random.randint(0,100,size=(len(date_rng)))\n",
    "\n",
    "# Set the date column as index\n",
    "df['datetime'] = pd.to_datetime(df['date'])\n",
    "df = df.set_index('datetime')\n",
    "df.drop(['date'], axis=1, inplace=True)\n",
    "\n",
    "# Resample the DataFrame to calculate daily means\n",
    "df_resampled = df.resample('D').mean()\n",
    "\n",
    "# Display the resampled DataFrame\n",
    "print(df_resampled)\n",
    "\n",
    "# Prediction part\n",
    "X = [i for i in range(0, len(df_resampled))]\n",
    "X = np.reshape(X, (len(X), 1))\n",
    "y = df_resampled['data'].tolist()\n",
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "# Predict the 'data' value for the next day\n",
    "next_day = [[len(X) + 1]]\n",
    "predicted_value = model.predict(next_day)\n",
    "print('The predicted average value for the next day is: ', predicted_value[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21f2e251-7f69-4f27-9041-aff5d022bac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# PII in comments (phone number)\n",
    "# Contact me if you have any questions: 123-456-7890"
   ]
  }
 ],
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