{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Temperature Time Series\n",
    "\n",
    "https://www.kaggle.com/datasets/ianpetrustan/global-temperature-time-series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis\n",
    "\n",
    "For this part, we will open and check the dataset for any missing values, inconsistencies, any outliers or missing data, and deal with them if we ever found one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Source</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>2016-12-06</td>\n",
       "      <td>0.7895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>2016-12-06</td>\n",
       "      <td>0.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>0.7504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>0.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>2016-10-06</td>\n",
       "      <td>0.7292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source        Date    Mean\n",
       "0     GCAG  2016-12-06  0.7895\n",
       "1  GISTEMP  2016-12-06  0.8100\n",
       "2     GCAG  2016-11-06  0.7504\n",
       "3  GISTEMP  2016-11-06  0.9300\n",
       "4     GCAG  2016-10-06  0.7292"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Opening the dataset\n",
    "df = pd.read_csv('monthly_csv.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3288 entries, 0 to 3287\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Source  3288 non-null   object \n",
      " 1   Date    3288 non-null   object \n",
      " 2   Mean    3288 non-null   float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 77.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source has 0 missing values\n",
      "Date has 0 missing values\n",
      "Mean has 0 missing values\n"
     ]
    }
   ],
   "source": [
    "# checking nulls for every column\n",
    "for column in df.columns:\n",
    "    print(f\"{column} has {df[column].isnull().sum()} missing values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nunique: 2\n",
      "Source has ['GCAG' 'GISTEMP'] unique values\n",
      "nunique: 1644\n",
      "Date has ['2016-12-06' '2016-11-06' '2016-10-06' ... '1880-03-06' '1880-02-06'\n",
      " '1880-01-06'] unique values\n",
      "nunique: 1674\n",
      "Mean has [ 0.7895  0.81    0.7504 ... -0.0499 -0.1357  0.0009] unique values\n"
     ]
    }
   ],
   "source": [
    "# checking unique vals\n",
    "for column in df.columns:\n",
    "    print(f\"nunique: {df[column].nunique()}\")\n",
    "    print(f\"{column} has {df[column].unique()} unique values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see we have multiple dates, as the number of unique dates is only 1644. Why is this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1880-01-06    2\n",
       "1880-02-06    2\n",
       "1880-03-06    2\n",
       "1880-04-06    2\n",
       "1880-05-06    2\n",
       "             ..\n",
       "2016-08-06    2\n",
       "2016-09-06    2\n",
       "2016-10-06    2\n",
       "2016-11-06    2\n",
       "2016-12-06    2\n",
       "Name: Source, Length: 1644, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Date')['Source'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's because each source (GCAG, GISTEMP) contribute a mean for each month. 1644 * 2 = 3288"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Source</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>1987-04-06</td>\n",
       "      <td>0.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>2001-10-06</td>\n",
       "      <td>0.5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>1979-01-06</td>\n",
       "      <td>0.1602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>1898-02-06</td>\n",
       "      <td>-0.3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>2008-11-06</td>\n",
       "      <td>0.6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>1965-02-06</td>\n",
       "      <td>-0.1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>1947-03-06</td>\n",
       "      <td>0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2685</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>1905-02-06</td>\n",
       "      <td>-0.5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>1970-10-06</td>\n",
       "      <td>0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>1984-10-06</td>\n",
       "      <td>0.1500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Source        Date    Mean\n",
       "713   GISTEMP  1987-04-06  0.2400\n",
       "365   GISTEMP  2001-10-06  0.5200\n",
       "910      GCAG  1979-01-06  0.1602\n",
       "2853  GISTEMP  1898-02-06 -0.3400\n",
       "195   GISTEMP  2008-11-06  0.6600\n",
       "1244     GCAG  1965-02-06 -0.1966\n",
       "1675  GISTEMP  1947-03-06  0.0500\n",
       "2685  GISTEMP  1905-02-06 -0.5900\n",
       "1109  GISTEMP  1970-10-06  0.0500\n",
       "773   GISTEMP  1984-10-06  0.1500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.to_datetime(df['Date'])\n",
    "df['Date'] = dates\n",
    "\n",
    "df['Year'] = df['Date'].dt.year\n",
    "df['Month'] = df['Date'].dt.month\n",
    "df['Day'] = df['Date'].dt.day\n",
    "\n",
    "df.drop('Date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3288 entries, 0 to 3287\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Source  3288 non-null   object \n",
      " 1   Mean    3288 non-null   float64\n",
      " 2   Year    3288 non-null   int32  \n",
      " 3   Month   3288 non-null   int32  \n",
      " 4   Day     3288 non-null   int32  \n",
      "dtypes: float64(1), int32(3), object(1)\n",
      "memory usage: 90.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "    }\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>Source</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>0.7895</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>0.7504</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>0.9300</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>0.7292</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source    Mean  Year  Month  Day\n",
       "0     GCAG  0.7895  2016     12    6\n",
       "1  GISTEMP  0.8100  2016     12    6\n",
       "2     GCAG  0.7504  2016     11    6\n",
       "3  GISTEMP  0.9300  2016     11    6\n",
       "4     GCAG  0.7292  2016     10    6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Mean</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Source_GCAG</th>\n",
       "      <th>Source_GISTEMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.7895</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8100</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7504</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.9300</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7292</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mean  Year  Month  Day  Source_GCAG  Source_GISTEMP\n",
       "0  0.7895  2016     12    6            1               0\n",
       "1  0.8100  2016     12    6            0               1\n",
       "2  0.7504  2016     11    6            1               0\n",
       "3  0.9300  2016     11    6            0               1\n",
       "4  0.7292  2016     10    6            1               0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turning categoricals to dummy variables\n",
    "df = pd.get_dummies(df, prefix=['Source'], dtype=int)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3288, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5)\n",
    "\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_)\n",
    "plt.title(\"K-Means\")\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "\n",
    "plt.scatter(centers[:, 0], centers[:, 1], s=300, c='red', marker='X')\n",
    "\n",
    "plt.title(\"K-Means Clustering\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246874.0856942815"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing inertia: Measures how far data points are from their centroids.\n",
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# elbow method, looking for optimal K\n",
    "K = range(1, 10)\n",
    "inertias = []\n",
    "for k in K:\n",
    "    kmean = KMeans(n_clusters=k, random_state=0)\n",
    "    kmean.fit(X)\n",
    "    inertias.append(kmean.inertia_)\n",
    "    \n",
    "plt.plot(K, inertias, 'bo-')\n",
    "plt.xlabel(\"number of clusters K\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.title(\"Elbow Method\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 7.895e-01,  2.016e+03,  1.200e+01,  6.000e+00,  1.000e+00,\n",
       "          0.000e+00],\n",
       "        [ 8.100e-01,  2.016e+03,  1.200e+01,  6.000e+00,  0.000e+00,\n",
       "          1.000e+00],\n",
       "        [ 7.504e-01,  2.016e+03,  1.100e+01,  6.000e+00,  1.000e+00,\n",
       "          0.000e+00],\n",
       "        ...,\n",
       "        [-2.100e-01,  1.880e+03,  2.000e+00,  6.000e+00,  0.000e+00,\n",
       "          1.000e+00],\n",
       "        [ 9.000e-04,  1.880e+03,  1.000e+00,  6.000e+00,  1.000e+00,\n",
       "          0.000e+00],\n",
       "        [-3.000e-01,  1.880e+03,  1.000e+00,  6.000e+00,  0.000e+00,\n",
       "          1.000e+00]], shape=(3288, 6)),\n",
       " (3288, 6))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster centers: [[ 4.03897870e-01  1.99400000e+03  6.50000000e+00  6.00000000e+00\n",
      "   5.00000000e-01  5.00000000e-01]\n",
      " [-2.48820471e-01  1.90250000e+03  6.50000000e+00  6.00000000e+00\n",
      "   5.00000000e-01  5.00000000e-01]\n",
      " [-3.73269928e-02  1.94850000e+03  6.50000000e+00  6.00000000e+00\n",
      "   5.00000000e-01  5.00000000e-01]]\n",
      "Labels: [0 0 0 ... 1 1 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_2 = KMeans(n_clusters=3, init='k-means++') # applying 3\n",
    "kmeans_2.fit(X)\n",
    "\n",
    "# Get the cluster centroids and labels\n",
    "centroids = kmeans_2.cluster_centers_\n",
    "labels = kmeans_2.labels_\n",
    "\n",
    "print(\"Cluster centers:\", centroids)\n",
    "print(\"Labels:\", labels)\n",
    "\n",
    "for cluster in range(4):\n",
    "    cluster_points = X[labels == cluster]\n",
    "    plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster}')\n",
    "\n",
    "# Plot centroids\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], s=300, c='red', marker='X')\n",
    "\n",
    "\n",
    "plt.title(\"K-Means Clustering\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Mean</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Source_GCAG</th>\n",
       "      <th>Source_GISTEMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.7895</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8100</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.7504</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.9300</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7292</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mean  Year  Month  Day  Source_GCAG  Source_GISTEMP\n",
       "0  0.7895  2016     12    6            1               0\n",
       "1  0.8100  2016     12    6            0               1\n",
       "2  0.7504  2016     11    6            1               0\n",
       "3  0.9300  2016     11    6            0               1\n",
       "4  0.7292  2016     10    6            1               0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction with Sample Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X = pd.DataFrame({\n",
    "    \"Source\": [\"GISTEMP\"],\n",
    "    \"Mean\": [0.7895],\n",
    "    \"Date\": [\"2016-01-15\"],\n",
    "                      })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Source</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>0.7895</td>\n",
       "      <td>2016-01-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source    Mean        Date\n",
       "0  GISTEMP  0.7895  2016-01-15"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_data(input_X: pd.DataFrame) -> np.array:\n",
    "    \"\"\" \n",
    "    Processes user input data into usable form for the KMeans model to predict.\n",
    "    \n",
    "    Args:\n",
    "        input_X (pd.DataFrame): Input data in dataframe format with one instance.\n",
    "    \n",
    "    Returns:\n",
    "        np.array: An numpy array for the KMeans model to predict\n",
    "    \"\"\"\n",
    "    \n",
    "    input_X = input_X.copy()\n",
    "    \n",
    "    # Split up the dates\n",
    "    if 'Date' in input_X.columns:\n",
    "        dates = pd.to_datetime(input_X['Date'])\n",
    "        input_X['Date'] = dates\n",
    "\n",
    "        input_X['Year'] = input_X['Date'].dt.year\n",
    "        input_X['Month'] = input_X['Date'].dt.month\n",
    "        input_X['Day'] = input_X['Date'].dt.day\n",
    "\n",
    "        input_X.drop('Date', axis=1, inplace=True)\n",
    "    \n",
    "    input_X = pd.get_dummies(input_X, prefix=['Source'], dtype=int)\n",
    "    \n",
    "    for col in ['Source_GCAG', 'Source_GISTEMP']:\n",
    "        if col not in input_X.columns:\n",
    "            input_X[col] = 0\n",
    "\n",
    "    # Reorder columns to ensure correct order\n",
    "    input_X = input_X[['Mean', 'Year', 'Month', 'Day', 'Source_GCAG', 'Source_GISTEMP']]\n",
    "   \n",
    "    arr_X = input_X.to_numpy() \n",
    "    \n",
    "    return arr_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Source</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>0.7895</td>\n",
       "      <td>2016-01-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source    Mean        Date\n",
       "0  GISTEMP  0.7895  2016-01-15"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7.895e-01 2.016e+03 1.000e+00 1.500e+01 0.000e+00 1.000e+00]] (1, 6)\n"
     ]
    }
   ],
   "source": [
    "arr_X = process_data(new_X)\n",
    "print(arr_X, arr_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted clusters: [0]\n"
     ]
    }
   ],
   "source": [
    "predictions = kmeans_2.predict(arr_X)\n",
    "print(f\"Predicted clusters: {predictions}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_clusters(model: KMeans, X: np.array, input_X: np.array) -> None:\n",
    "    \"\"\"\n",
    "    Plots the predicted class to the clusters.\n",
    "    \n",
    "    Args:\n",
    "        model (KMeans): A KMeans model trained on X input\n",
    "        X (np.array): The numpy array version of the dataset\n",
    "        input_X (np.array): The numpy array of the input \n",
    "        \n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    centroids = model.cluster_centers_\n",
    "    labels = model.labels_\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    for cluster in range(3):\n",
    "        cluster_points = X[labels == cluster]\n",
    "        plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster}')\n",
    "\n",
    "    # Plot centroids\n",
    "    plt.scatter(centroids[:, 0], centroids[:, 1], s=200, c='black', marker='X', label='Centroids')\n",
    "\n",
    "    # Highlight the predicted cluster and point\n",
    "    plt.scatter(input_X[:, 0], input_X[:, 1], s=300, c='red', marker='P', label=f'Predicted Cluster: {predictions[0]}')\n",
    "\n",
    "    plt.title('K-Means Clustering with Predicted Point')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 6)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_2.predict(arr_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_clusters(kmeans_2, X, arr_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/kmeans_model.pkl']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(kmeans_2, './models/kmeans_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}