AQ forecating using Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00cc0936",
   "metadata": {},
   "source": [
    "<h1>Forecasting Air Quality in Nairobi: January 2024 Analysis </h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "44db6b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pprint import PrettyPrinter #used to print data in more human-readable format \n",
    "from pymongo import MongoClient # Importing MongoClient from pymongo for interacting with MongoDB databases\n",
    "import matplotlib.pyplot as plt #for visualization\n",
    "import plotly.express as px #for plots\n",
    "import pytz #library used in working with timezones\n",
    "from sklearn.linear_model import LinearRegression #model which we are going to use\n",
    "from sklearn.metrics import mean_absolute_error #for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "af0720c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the pretty printer and giving indent \n",
    "pp = PrettyPrinter(indent = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934f0943",
   "metadata": {},
   "source": [
    "<h2> Preparing the data<h2>\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4fee6a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ {'empty': False, 'name': 'admin', 'sizeOnDisk': 40960},\n",
      "  {'empty': False, 'name': 'air-quality', 'sizeOnDisk': 47640576},\n",
      "  {'empty': False, 'name': 'config', 'sizeOnDisk': 110592},\n",
      "  {'empty': False, 'name': 'local', 'sizeOnDisk': 73728}]\n"
     ]
    }
   ],
   "source": [
    "# creating the client to connect to db \n",
    "client = MongoClient(host = \"localhost\", port = 27017)\n",
    "# printing the list of db available at client\n",
    "pp.pprint(list(client.list_databases()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda1a630",
   "metadata": {},
   "source": [
    "<h3> Assigning the air-quality database to db </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3839fd5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = client[\"air-quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc5d1a45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JanNairobi\n",
      "output_data\n"
     ]
    }
   ],
   "source": [
    "# printing the list of collection available at db\n",
    "for c in db.list_collections():\n",
    "    print(c['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b3fbeec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assigning the collection \"output_data\" to nairobi\n",
    "nairobi = db[\"output_data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6fdf204b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collection(Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'air-quality'), 'output_data.key.0')\n"
     ]
    }
   ],
   "source": [
    "nairobi_keys = nairobi.key[0]\n",
    "print(nairobi_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "acf398af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "579674"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of documents in nairobi\n",
    "nairobi.count_documents({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "06507111",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ 'P2': 22.4,\n",
      "  '_id': ObjectId('65ead2091775cfa69ec377c0'),\n",
      "  'lat': -1.289,\n",
      "  'location': 3573,\n",
      "  'lon': 36.825,\n",
      "  'sensor_id': 49,\n",
      "  'sensor_type': 'pms5003',\n",
      "  'timestamp': '2024-01-01T00:00:10.062960+00:00'}\n"
     ]
    }
   ],
   "source": [
    "# this will only show for only one \n",
    "result = nairobi.find_one({})\n",
    "pp.pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f00cf17a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ '_id': ObjectId('65ead2261775cfa69ecc5019'),\n",
      "  'lat': -1.289,\n",
      "  'location': 3573,\n",
      "  'lon': 36.825,\n",
      "  'sensor_id': 50,\n",
      "  'sensor_type': 'DHT22',\n",
      "  'temperature': 19.6,\n",
      "  'timestamp': '2024-01-31T23:59:25.265328+00:00'}\n",
      "{ '_id': ObjectId('65ead2261775cfa69ecc5018'),\n",
      "  'humidity': 99.9,\n",
      "  'lat': -1.289,\n",
      "  'location': 3573,\n",
      "  'lon': 36.825,\n",
      "  'sensor_id': 50,\n",
      "  'sensor_type': 'DHT22',\n",
      "  'timestamp': '2024-01-31T23:59:25.265328+00:00'}\n",
      "{ 'P0': 6.4,\n",
      "  '_id': ObjectId('65ead2261775cfa69ecc5017'),\n",
      "  'lat': -1.289,\n",
      "  'location': 3573,\n",
      "  'lon': 36.825,\n",
      "  'sensor_id': 49,\n",
      "  'sensor_type': 'pms5003',\n",
      "  'timestamp': '2024-01-31T23:59:24.169254+00:00'}\n"
     ]
    }
   ],
   "source": [
    "# Retrieve the last input in the dataset\n",
    "latest_input = nairobi.find({}).sort([('_id', -1)]).limit(3)\n",
    "\n",
    "for document in latest_input:\n",
    "    pp.pprint(document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "07a9f515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[35, 76, 3573, 3612, 3626]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for distinct site  \n",
    "nairobi.distinct(\"location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b79da210",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Documents from site 35: 72001\n",
      "Documents from site 3573: 375573\n",
      "Documents from site 3612: 67785\n",
      "Documents from site 3626: 30964\n",
      "Documents from site 76: 33351\n"
     ]
    }
   ],
   "source": [
    "# counting the number of documents at each location\n",
    "print(\"Documents from site 35:\", nairobi.count_documents({\"location\":35}))\n",
    "print(\"Documents from site 3573:\", nairobi.count_documents({\"location\":3573}))\n",
    "print(\"Documents from site 3612:\", nairobi.count_documents({\"location\":3612}))\n",
    "print(\"Documents from site 3626:\", nairobi.count_documents({\"location\":3626}))\n",
    "print(\"Documents from site 76:\", nairobi.count_documents({\"location\":76}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e3efb3",
   "metadata": {},
   "source": [
    "<h4> we will use site 3573 as it has the most no of documents </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4b08e7af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of humidity values: 75132\n",
      "Count of temperature values: 75132\n",
      "Count of P0 values: 75103\n",
      "Count of P1 values: 75103\n",
      "Count of P2 values: 75103\n"
     ]
    }
   ],
   "source": [
    "# we are counting the number of reading for each measuremnt type\n",
    "# Initialize counters for each measurement type\n",
    "humidity_count = 0\n",
    "temperature_count = 0\n",
    "p0_count = 0\n",
    "p1_count = 0\n",
    "p2_count = 0\n",
    "\n",
    "# Query to filter documents by location 3573\n",
    "query = {\"location\": 3573}\n",
    "\n",
    "# Iterate over the documents in the collection matching the query\n",
    "for doc in nairobi.find(query):\n",
    "    # Increment the count for each measurement type based on its presence in the document\n",
    "    if 'humidity' in doc:\n",
    "        humidity_count += 1\n",
    "    if 'temperature' in doc:\n",
    "        temperature_count += 1\n",
    "    if 'P0' in doc:\n",
    "        p0_count += 1\n",
    "    if 'P1' in doc:\n",
    "        p1_count += 1\n",
    "    if 'P2' in doc:\n",
    "        p2_count += 1\n",
    "\n",
    "# Print counts\n",
    "print(\"Count of humidity values:\", humidity_count)\n",
    "print(\"Count of temperature values:\", temperature_count)\n",
    "print(\"Count of P0 values:\", p0_count)\n",
    "print(\"Count of P1 values:\", p1_count)\n",
    "print(\"Count of P2 values:\", p2_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c623fa8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'P2': 22.4, 'timestamp': '2024-01-01T00:00:10.062960+00:00'}\n"
     ]
    }
   ],
   "source": [
    "# retreive the reading from site 3573 \n",
    "result = nairobi.find(\n",
    "    {\"location\": 3573},\n",
    "    {\"P2\": 1, \"timestamp\": 1, \"_id\": 0}  # Projection parameter \n",
    ")\n",
    "pp.pprint(result.next())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5cf8537e",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P2</th>\n",
       "      <th>P2.L1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2024-01-01 04:00:00+03:00</th>\n",
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      ],
      "text/plain": [
       "                                  P2      P2.L1\n",
       "timestamp                                      \n",
       "2024-01-01 04:00:00+03:00  20.631081  22.537019\n",
       "2024-01-01 05:00:00+03:00  20.193750  20.631081\n",
       "2024-01-01 06:00:00+03:00  17.472273  20.193750\n",
       "2024-01-01 07:00:00+03:00  17.708796  17.472273\n",
       "2024-01-01 08:00:00+03:00  16.832432  17.708796"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the records from results and querying it into the dataframe \n",
    "df = pd.DataFrame(result).set_index(\"timestamp\")\n",
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0be85a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we need to change the \n",
    "# Convert index to DateTimeIndex\n",
    "df.index = pd.to_datetime(df.index)\n",
    "\n",
    "# Convert the timezone to nairobi for the index\n",
    "df.index = df.index.tz_convert(\"Africa/Nairobi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "297d2ba7",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P2</th>\n",
       "      <th>P2.L1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01 04:00:00+03:00</th>\n",
       "      <td>20.631081</td>\n",
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      "text/plain": [
       "                                  P2      P2.L1\n",
       "timestamp                                      \n",
       "2024-01-01 04:00:00+03:00  20.631081  22.537019\n",
       "2024-01-01 05:00:00+03:00  20.193750  20.631081\n",
       "2024-01-01 06:00:00+03:00  17.472273  20.193750\n",
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       "2024-01-01 08:00:00+03:00  16.832432  17.708796"
      ]
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     "execution_count": 73,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499b5e40",
   "metadata": {},
   "source": [
    "<h2> Visualization through plots <h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "15d7e1d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Distribution of PM2.5 reading'}>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the box plot to visualize the oultiers present in our data\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "df[\"P2\"].plot(kind = \"box\", vert = False, title = \"Distribution of PM2.5 reading\", ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8856f10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing the outliers\n",
    "df = df[df[\"P2\"] < 500 ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "764862c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01 03:00:00+03:00</th>\n",
       "      <td>22.537019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 04:00:00+03:00</th>\n",
       "      <td>20.631081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 05:00:00+03:00</th>\n",
       "      <td>20.193750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 06:00:00+03:00</th>\n",
       "      <td>17.472273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 07:00:00+03:00</th>\n",
       "      <td>17.708796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  P2\n",
       "timestamp                           \n",
       "2024-01-01 03:00:00+03:00  22.537019\n",
       "2024-01-01 04:00:00+03:00  20.631081\n",
       "2024-01-01 05:00:00+03:00  20.193750\n",
       "2024-01-01 06:00:00+03:00  17.472273\n",
       "2024-01-01 07:00:00+03:00  17.708796"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resampling the data to hourly format and converting that data into the dataframe  and impute missing values with forward fill\n",
    "\n",
    "df = df[\"P2\"].resample(\"1H\").mean().fillna(method = \"ffill\").to_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3fb486f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'PM 2.5 Time Series'}, xlabel='Time', ylabel='PM 2.5'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of P2 reading of January month\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "df[\"P2\"].plot(xlabel = \"Time\", ylabel = \"PM 2.5\", title = \"PM 2.5 Time Series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3f8c32a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Weekly rolling average'}, xlabel='timestamp', ylabel='PM 2.5'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# weekly rolling average or moving average\n",
    "# we have use rolling as 168 as it is the hours in a week.\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "df[\"P2\"].rolling(168).mean().plot(ax=ax, ylabel = \"PM 2.5\",title = \"Weekly rolling average\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9b0b32",
   "metadata": {},
   "source": [
    "<h2>Correlation</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a76259d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P2</th>\n",
       "      <th>P2.L1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01 04:00:00+03:00</th>\n",
       "      <td>20.631081</td>\n",
       "      <td>22.537019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 05:00:00+03:00</th>\n",
       "      <td>20.193750</td>\n",
       "      <td>20.631081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 06:00:00+03:00</th>\n",
       "      <td>17.472273</td>\n",
       "      <td>20.193750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 07:00:00+03:00</th>\n",
       "      <td>17.708796</td>\n",
       "      <td>17.472273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 08:00:00+03:00</th>\n",
       "      <td>16.832432</td>\n",
       "      <td>17.708796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  P2      P2.L1\n",
       "timestamp                                      \n",
       "2024-01-01 04:00:00+03:00  20.631081  22.537019\n",
       "2024-01-01 05:00:00+03:00  20.193750  20.631081\n",
       "2024-01-01 06:00:00+03:00  17.472273  20.193750\n",
       "2024-01-01 07:00:00+03:00  17.708796  17.472273\n",
       "2024-01-01 08:00:00+03:00  16.832432  17.708796"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we are creating this new columns inorder to find the correlation between current hour and previous hour\n",
    "df[\"P2.L1\"] = df[\"P2\"].shift(1)\n",
    "df.dropna(inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "77e62764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P2</th>\n",
       "      <th>P2.L1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.775347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P2.L1</th>\n",
       "      <td>0.775347</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             P2     P2.L1\n",
       "P2     1.000000  0.775347\n",
       "P2.L1  0.775347  1.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we are finding correlation \n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fb0682be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PM 2.5 Autocorrelation')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter graph of correlation for understanding correlation in  visualization\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.scatter(x = df[\"P2.L1\"], y = df[\"P2\"])\n",
    "ax.plot([0,120],[0,120], linestyle = \"--\", color = \"orange\")\n",
    "plt.xlabel(\"P2.L1\")\n",
    "plt.ylabel(\"P2\")\n",
    "plt.title(\"PM 2.5 Autocorrelation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a0629a",
   "metadata": {},
   "source": [
    "<h2> Splitting the data <h2>\n",
    "    <h3> we are splitting the dataframe into feature and target </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "634bba88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P2.L1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-01-01 04:00:00+03:00</th>\n",
       "      <td>22.537019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 05:00:00+03:00</th>\n",
       "      <td>20.631081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 06:00:00+03:00</th>\n",
       "      <td>20.193750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 07:00:00+03:00</th>\n",
       "      <td>17.472273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-01 08:00:00+03:00</th>\n",
       "      <td>17.708796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               P2.L1\n",
       "timestamp                           \n",
       "2024-01-01 04:00:00+03:00  22.537019\n",
       "2024-01-01 05:00:00+03:00  20.631081\n",
       "2024-01-01 06:00:00+03:00  20.193750\n",
       "2024-01-01 07:00:00+03:00  17.472273\n",
       "2024-01-01 08:00:00+03:00  17.708796"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here my target is P2 so i am assiging it to y and feature to x\n",
    "target = \"P2\"\n",
    "y = df[target]\n",
    "X = df.drop(columns = target)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5363f33",
   "metadata": {},
   "source": [
    "<h3> splitting the data into training and testing data </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b46e7d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we are splitting 80% into training and 20% into testing data \n",
    "\n",
    "cutoff = int(len(X)*0.8)\n",
    "\n",
    "X_train, y_train = X.iloc[:cutoff], y.iloc[:cutoff]\n",
    "X_test, y_test = X.iloc[cutoff:], y.iloc[cutoff:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c0bfed7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if our data is splitted properly and checking the count if it matches to X \n",
    "len(X_train) + len(X_test)  == len(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d0721c5",
   "metadata": {},
   "source": [
    "<h2>Building Model</h2>\n",
    "<h3> Baseline <h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eb3359b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean P2 Reading: 28.89\n",
      "Baseline MAE: 7.3\n"
     ]
    }
   ],
   "source": [
    "# Building the model\n",
    "y_mean = y_train.mean()\n",
    "y_pred_baseline = [y_mean] * len(y_train)\n",
    "mae_baseline = mean_absolute_error(y_train, y_pred_baseline)\n",
    "\n",
    "print(\"Mean P2 Reading:\", round(y_train.mean(), 2))\n",
    "print(\"Baseline MAE:\", round(mae_baseline, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65364c6",
   "metadata": {},
   "source": [
    "<h3>Iterate</h3>\n",
    "<h4>instantiate the linear regression model and fitting the training data</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b3f4b881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training our model with Linear regression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0927ceb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MAE: 4.24\n",
      "Test MAE: 4.89\n"
     ]
    }
   ],
   "source": [
    "# calculate the training and testing MAE for our model\n",
    "training_mae = mean_absolute_error(y_train, model.predict(X_train) )\n",
    "test_mae = mean_absolute_error(y_test, model.predict(X_test))\n",
    "print(\"Training MAE:\", round(training_mae, 2))\n",
    "print(\"Test MAE:\", round(test_mae, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679626c4",
   "metadata": {},
   "source": [
    "<h3>Creating the dataframe which contains the test data and prediction by the model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fcf24496",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                              y_test     y_pred\n",
       "timestamp                                      \n",
       "2024-01-25 22:00:00+03:00  20.490926  24.868417\n",
       "2024-01-25 23:00:00+03:00  30.208850  22.448567\n",
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      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred_test = pd.DataFrame(\n",
    "    {\n",
    "        \"y_test\":y_test,\n",
    "        \"y_pred\":model.predict(X_test)\n",
    "    }\n",
    ")\n",
    "df_pred_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e646bf45",
   "metadata": {},
   "source": [
    "<h3> Plotting the time series model </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ed0f8da5",
   "metadata": {},
   "outputs": [
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       "var gd = document.getElementById('93b46372-a8a6-4ae4-9c79-2b19b85813f5');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time series line plot \n",
    "fig = px.line(df_pred_test, labels = {\"value\":\"P2\"})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f3c6fe0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image('AQ forecating using Linear Regression.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53949cfb",
   "metadata": {},
   "source": [
    "GitHub does not render interactive elements from Jupyter Notebooks, including plots generated using libraries like Matplotlib or Plotly. So i have added this plot as png with same name as this file."
   ]
  }
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