DS_EICT1142.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "import time\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "\n",
    "from pylab import rcParams\n",
    "import seaborn as sns"
   ]
  },
  {
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       "      <td>2006-04-01 02:00:00.000 +0200</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
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       "      <td>14.9569</td>\n",
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       "      <td>1015.94</td>\n",
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       "      <td>2006-04-01 04:00:00.000 +0200</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
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       "      <td>1016.51</td>\n",
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       "                  Formatted Date  ...                      Daily Summary\n",
       "0  2006-04-01 00:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
       "1  2006-04-01 01:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
       "2  2006-04-01 02:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
       "3  2006-04-01 03:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
       "4  2006-04-01 04:00:00.000 +0200  ...  Partly cloudy throughout the day.\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
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     "execution_count": 179,
     "metadata": {},
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   "source": [
    "data = pd.read_csv('train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "khlj2gJ0FlfQ"
   },
   "source": [
    "**EXPLORATORY DATA ANALYSIS**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
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    "id": "guv7UUrTRf_3",
    "outputId": "e4bf1b69-ddde-4a06-b9a7-8866c37878de"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94268 entries, 0 to 94267\n",
      "Data columns (total 12 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Formatted Date            94268 non-null  object \n",
      " 1   Summary                   94268 non-null  object \n",
      " 2   Precip Type               94125 non-null  object \n",
      " 3   Temperature (C)           94268 non-null  float64\n",
      " 4   Apparent Temperature (C)  94268 non-null  float64\n",
      " 5   Humidity                  94268 non-null  float64\n",
      " 6   Wind Speed (km/h)         94268 non-null  float64\n",
      " 7   Wind Bearing (degrees)    94268 non-null  int64  \n",
      " 8   Visibility (km)           94268 non-null  float64\n",
      " 9   Cloud Cover               94268 non-null  int64  \n",
      " 10  Pressure (millibars)      94268 non-null  float64\n",
      " 11  Daily Summary             94268 non-null  object \n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 8.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
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    },
    "id": "UTCJ9dLbRk77",
    "outputId": "f476e957-8b25-4955-e7c4-2f633c95d3d3"
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    {
     "data": {
      "text/plain": [
       "Formatted Date                0\n",
       "Summary                       0\n",
       "Precip Type                 143\n",
       "Temperature (C)               0\n",
       "Apparent Temperature (C)      0\n",
       "Humidity                      0\n",
       "Wind Speed (km/h)             0\n",
       "Wind Bearing (degrees)        0\n",
       "Visibility (km)               0\n",
       "Cloud Cover                   0\n",
       "Pressure (millibars)          0\n",
       "Daily Summary                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
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    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "id": "yGXBY_lNSCzY"
   },
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   "source": [
    "data.fillna(method='ffill', inplace=True) "
   ]
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  {
   "cell_type": "code",
   "execution_count": 183,
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    "id": "YDkPygFHSRkf",
    "outputId": "74000740-5432-48b3-d894-9e7771db0a44"
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    {
     "data": {
      "text/plain": [
       "Formatted Date              0\n",
       "Summary                     0\n",
       "Precip Type                 0\n",
       "Temperature (C)             0\n",
       "Apparent Temperature (C)    0\n",
       "Humidity                    0\n",
       "Wind Speed (km/h)           0\n",
       "Wind Bearing (degrees)      0\n",
       "Visibility (km)             0\n",
       "Cloud Cover                 0\n",
       "Pressure (millibars)        0\n",
       "Daily Summary               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2D4D3HNthy72",
    "outputId": "cce61f41-1c07-494c-e43d-ae0ba7777857"
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     "text": [
      "Index(['Formatted Date', 'Summary', 'Precip Type', 'Daily Summary'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Categorical variables:\n",
    "categorical = data.select_dtypes(include = [\"object\"]).keys()\n",
    "print(categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E1iLMxioiEfN",
    "outputId": "da91935b-41f4-4902-fa2d-a4f04ae4f03c"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Temperature (C)', 'Apparent Temperature (C)', 'Humidity',\n",
      "       'Wind Speed (km/h)', 'Wind Bearing (degrees)', 'Visibility (km)',\n",
      "       'Cloud Cover', 'Pressure (millibars)'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Quantitative variables:\n",
    "quantitative = data.select_dtypes(include = [\"int64\",\"float64\"]).keys()\n",
    "print(quantitative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
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    "id": "6d5pQRNGSZQl",
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       "      <th>2</th>\n",
       "      <td>2006-04-01 00:00:00+00:00</td>\n",
       "      <td>Mostly Cloudy</td>\n",
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       "      <th>3</th>\n",
       "      <td>2006-04-01 01:00:00+00:00</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
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       "      <td>0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffe1c6a4-502b-411f-a657-605c8c0827da')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-ffe1c6a4-502b-411f-a657-605c8c0827da button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ffe1c6a4-502b-411f-a657-605c8c0827da');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "             Formatted Date        Summary Precip Type  ...  month  day  hour\n",
       "0 2006-03-31 22:00:00+00:00  Partly Cloudy        rain  ...      3   31    22\n",
       "1 2006-03-31 23:00:00+00:00  Partly Cloudy        rain  ...      3   31    23\n",
       "2 2006-04-01 00:00:00+00:00  Mostly Cloudy        rain  ...      4    1     0\n",
       "3 2006-04-01 01:00:00+00:00  Partly Cloudy        rain  ...      4    1     1\n",
       "4 2006-04-01 02:00:00+00:00  Mostly Cloudy        rain  ...      4    1     2\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#'Formatted Date' transformation:\n",
    "data['Formatted Date']=pd.to_datetime(data['Formatted Date'],utc=True)\n",
    "data['year'] = data['Formatted Date'].dt.year\n",
    "data['month'] = data['Formatted Date'].dt.month\n",
    "data['day'] = data['Formatted Date'].dt.day\n",
    "data['hour'] = data['Formatted Date'].dt.hour\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 673
    },
    "id": "Cn9htX7oiTCH",
    "outputId": "69d88bae-043e-427b-8d88-fc57819c060d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7c13041f50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14e67b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14e260d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14ddb5d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14d92ad0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14dbdb10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14d0a590>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14d409d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7c14d40a10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 8, 8\n",
    "data[quantitative].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "id": "SpZ-p6Dgiy-H"
   },
   "outputs": [],
   "source": [
    "data=data.drop('Cloud Cover',axis=1) ## Some observations are zero.It seems that they are missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "id": "rzMAup6Ii-ay",
    "outputId": "62585ca3-ae6b-4607-d2f1-58628ca349bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c12e20450>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pressure_median = data['Pressure (millibars)'].median()\n",
    "      \n",
    "def pressure(x):\n",
    "    if x==0:\n",
    "        return x + pressure_median\n",
    "    else:\n",
    "        return x\n",
    "        \n",
    "data[\"Pressure (millibars)\"] = data.apply(lambda row:pressure(row[\"Pressure (millibars)\"]) , axis = 1)\n",
    "\n",
    "rcParams['figure.figsize'] = 5, 3\n",
    "data['Pressure (millibars)'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g1NPrloCjUSv"
   },
   "source": [
    " **Feature engineering**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "id": "PK0ALLrcjeE-",
    "outputId": "10158eb7-fdda-48bb-9170-fa50a6082440"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c12e0ae10>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 8, 5\n",
    "sns.countplot(y=data['Summary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BJS0rOEwjnWd",
    "outputId": "7c2fb022-6933-42d0-c55a-9e6e7da2dcc5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['Summary'].unique()) #How many different 'Summary' categories are there?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2etc6imxj0EX"
   },
   "source": [
    "There are 26 'Summary' categories which makes it a bit confusing. Let's see which are the most common."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 896
    },
    "id": "8Gnbka31junZ",
    "outputId": "e9dfd2d4-9cbe-4340-a613-9b633a873521"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-be8200ba-6499-4203-a4fa-e260ed32f1cf\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Summary</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Partly Cloudy</th>\n",
       "      <td>0.329009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mostly Cloudy</th>\n",
       "      <td>0.289038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overcast</th>\n",
       "      <td>0.173877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clear</th>\n",
       "      <td>0.112806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foggy</th>\n",
       "      <td>0.075137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breezy and Overcast</th>\n",
       "      <td>0.005580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breezy and Mostly Cloudy</th>\n",
       "      <td>0.005463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breezy and Partly Cloudy</th>\n",
       "      <td>0.004095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dry and Partly Cloudy</th>\n",
       "      <td>0.000912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windy and Partly Cloudy</th>\n",
       "      <td>0.000711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breezy</th>\n",
       "      <td>0.000573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windy and Overcast</th>\n",
       "      <td>0.000477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humid and Mostly Cloudy</th>\n",
       "      <td>0.000424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breezy and Foggy</th>\n",
       "      <td>0.000371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windy and Mostly Cloudy</th>\n",
       "      <td>0.000371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dry</th>\n",
       "      <td>0.000361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humid and Partly Cloudy</th>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Rain</th>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dry and Mostly Cloudy</th>\n",
       "      <td>0.000149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windy</th>\n",
       "      <td>0.000085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humid and Overcast</th>\n",
       "      <td>0.000074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drizzle</th>\n",
       "      <td>0.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windy and Foggy</th>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breezy and Dry</th>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dangerously Windy and Partly Cloudy</th>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windy and Dry</th>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be8200ba-6499-4203-a4fa-e260ed32f1cf')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-be8200ba-6499-4203-a4fa-e260ed32f1cf button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-be8200ba-6499-4203-a4fa-e260ed32f1cf');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "col_0                                   count\n",
       "Summary                                      \n",
       "Partly Cloudy                        0.329009\n",
       "Mostly Cloudy                        0.289038\n",
       "Overcast                             0.173877\n",
       "Clear                                0.112806\n",
       "Foggy                                0.075137\n",
       "Breezy and Overcast                  0.005580\n",
       "Breezy and Mostly Cloudy             0.005463\n",
       "Breezy and Partly Cloudy             0.004095\n",
       "Dry and Partly Cloudy                0.000912\n",
       "Windy and Partly Cloudy              0.000711\n",
       "Breezy                               0.000573\n",
       "Windy and Overcast                   0.000477\n",
       "Humid and Mostly Cloudy              0.000424\n",
       "Breezy and Foggy                     0.000371\n",
       "Windy and Mostly Cloudy              0.000371\n",
       "Dry                                  0.000361\n",
       "Humid and Partly Cloudy              0.000180\n",
       "Light Rain                           0.000180\n",
       "Dry and Mostly Cloudy                0.000149\n",
       "Windy                                0.000085\n",
       "Humid and Overcast                   0.000074\n",
       "Drizzle                              0.000053\n",
       "Windy and Foggy                      0.000042\n",
       "Breezy and Dry                       0.000011\n",
       "Dangerously Windy and Partly Cloudy  0.000011\n",
       "Windy and Dry                        0.000011"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_freq=pd.crosstab(index=data['Summary'],columns=\"count\")  \n",
    "summary_freq_rel = summary_freq/summary_freq.sum() \n",
    "summary_freq_rel.sort_values('count', ascending=False) #relative frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "id": "tByz13dKkE0D"
   },
   "outputs": [],
   "source": [
    "#new categorical variable:\n",
    "\n",
    "def cloud_categorizer(row):\n",
    "   row = str(row).lower()\n",
    "   category = \"\"\n",
    "   if \"foggy\" in row:\n",
    "       category = 5\n",
    "   elif \"overcast\" in row:\n",
    "       category = 4\n",
    "   elif \"mostly cloudy\" in row:\n",
    "       category = 3\n",
    "   elif \"partly cloudy\" in row:\n",
    "       category = 2\n",
    "   elif \"clear\" in row:\n",
    "       category = 1\n",
    "   else:\n",
    "       category = 0\n",
    "   return category \n",
    "\n",
    "data[\"cloud (summary)\"] = data.apply (lambda row:cloud_categorizer(row[\"Summary\"]) , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "id": "Jfrd4LsjntQ3",
    "outputId": "8b7d5bf7-3092-4c9d-af44-a3c24f339de8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c1308d150>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 5, 5\n",
    "sns.countplot(data['cloud (summary)']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NudatPLJoRaQ"
   },
   "source": [
    "As can be observed, there are a few observations with no information about \"clouds\" or foggy in variable 'Summary'.\n",
    "How can we assign a value to them?\n",
    "I decided to look at variable 'Visibility (km)', which obviously is correlated to \"clouds\" and foggy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "gdiekEgEoBP9",
    "outputId": "5726887a-3d27-4845-abba-acdb71bee287"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7c1574f7d0>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['cloud (summary)'], y=data['Visibility (km)']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bIijVEzmoeV-"
   },
   "source": [
    "It seems that the boxplot of the null values is very similar to the one of \"overcast\"so let's assume that obsevations with no information about \"clouds\" and foggy fall in the \"overcast\" category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "id": "-G5Dj9BboW_f"
   },
   "outputs": [],
   "source": [
    "def cloud_categorizer(row):\n",
    "    row = str(row).lower()\n",
    "    category = \"\"\n",
    "    if \"foggy\" in row:\n",
    "        category = 5\n",
    "    elif \"overcast\" in row:\n",
    "        category = 4\n",
    "    elif \"mostly cloudy\" in row:\n",
    "        category = 3\n",
    "    elif \"partly cloudy\" in row:\n",
    "        category = 2\n",
    "    elif \"clear\" in row:\n",
    "        category = 1\n",
    "    else:\n",
    "        category = 4 \n",
    "    return category \n",
    "\n",
    "data[\"cloud (summary)\"] = data.apply (lambda row:cloud_categorizer(row[\"Summary\"]) , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "N0S3XpZhouWn",
    "outputId": "299aaa6f-d6b5-4157-d690-2324987f5ccf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '1=Clear'),\n",
       " Text(0, 0, '2=Partly Cloudy'),\n",
       " Text(0, 0, '3=Mostly Cloudy'),\n",
       " Text(0, 0, '4=Overcast'),\n",
       " Text(0, 0, '5=Foggy')]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(data['cloud (summary)'])\n",
    "ax.set_xticklabels(('1=Clear', '2=Partly Cloudy', '3=Mostly Cloudy', '4=Overcast', '5=Foggy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4MkfSYtLo3KS",
    "outputId": "8825f22f-1eee-476b-a8e8-37bad8dab0bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['Daily Summary'].unique()) #number of categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "PW30DHNDpEas",
    "outputId": "ff01d30d-aec1-4758-81ab-cbb58e977509"
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    {
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      "text/plain": [
       "col_0                                                  count\n",
       "Daily Summary                                               \n",
       "Mostly cloudy throughout the day.                   0.206698\n",
       "Partly cloudy throughout the day.                   0.101805\n",
       "Partly cloudy until night.                          0.064168\n",
       "Partly cloudy starting in the morning.              0.053719\n",
       "Foggy in the morning.                               0.044055\n",
       "Foggy starting overnight continuing until morning.  0.037425\n",
       "Partly cloudy until evening.                        0.034370\n",
       "Mostly cloudy until night.                          0.032577\n",
       "Overcast throughout the day.                        0.030551\n",
       "Partly cloudy starting in the morning continuin...  0.029512\n",
       "Foggy until morning.                                0.028037\n",
       "Partly cloudy starting in the morning continuin...  0.022404\n",
       "Mostly cloudy starting in the morning.              0.016294\n",
       "Partly cloudy starting overnight.                   0.015021\n",
       "Foggy starting in the evening.                      0.015021\n",
       "Partly cloudy starting in the afternoon continu...  0.013737\n",
       "Foggy overnight.                                    0.013249\n",
       "Mostly cloudy starting overnight.                   0.013239\n",
       "Partly cloudy starting in the afternoon.            0.013239\n",
       "Mostly cloudy until evening.                        0.010948"
      ]
     },
     "execution_count": 199,
     "metadata": {},
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    }
   ],
   "source": [
    "daily_summary_freq =pd.crosstab(index=data['Daily Summary'],columns=\"count\") \n",
    "daily_summary_freqrel=daily_summary_freq/daily_summary_freq.sum()\n",
    "daily_summary_freqrel.sort_values('count', ascending=False).head(20)#Show the 20 most common categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "id": "F5TYBaeerJKm"
   },
   "outputs": [],
   "source": [
    "def cloud_categorizer2(row):\n",
    "    row = str(row)\n",
    "    category = \"\"\n",
    "    if \"Mostly cloudy throughout the day.\" in row:\n",
    "        category = 5\n",
    "    elif \"Partly cloudy throughout the day.\" in row:\n",
    "        category = 4\n",
    "    elif \"Partly cloudy until night.\" in row:\n",
    "        category = 3\n",
    "    elif \"Partly cloudy starting in the morning.\" in row:\n",
    "        category = 2\n",
    "    elif \"Foggy in the morning.\" in row:\n",
    "        category = 1\n",
    "    elif \"Foggy starting overnight continuing until morning.\" in row:\n",
    "        category = 6\n",
    "    elif \"Partly cloudy until evening.\" in row:\n",
    "        category = 7\n",
    "    elif \"Mostly cloudy until night.\" in row:\n",
    "        category = 8\n",
    "    elif \"Overcast throughout the day.\" in row:\n",
    "        category = 9\n",
    "    elif \"Partly cloudy starting in the morning continuing until evening.\" in row:\n",
    "        category = 10\n",
    "    elif \"Foggy until morning.\" in row:\n",
    "        category = 11\n",
    "    elif \"Partly cloudy starting in the morning continuing until night.\" in row:\n",
    "        category = 12\n",
    "    elif \"Mostly cloudy starting in the morning.\" in row:\n",
    "        category = 13\n",
    "    elif \"Partly cloudy starting overnight.\" in row:\n",
    "        category = 14\n",
    "    elif \"Foggy starting in the evening.\" in row:\n",
    "        category = 15\n",
    "    elif \"Partly cloudy starting in the afternoon continuing until evening.\" in row:\n",
    "        category = 16\n",
    "    elif \"Foggy overnight.\" in row:\n",
    "        category = 17\n",
    "    elif \"Mostly cloudy starting overnight.\" in row:\n",
    "        category = 18\n",
    "    elif \"Partly cloudy starting in the afternoon.\" in row:\n",
    "        category = 19\n",
    "    elif \"Mostly cloudy until evening.\" in row:\n",
    "        category = 20\n",
    "    else:\n",
    "        category = 0\n",
    "    return category \n",
    "\n",
    "data[\"cloud (daily summary)\"] = data.apply (lambda row:cloud_categorizer2(row[\"Daily Summary\"]) , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "colab": {
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     "height": 389
    },
    "id": "mSrBXlgdpXAl",
    "outputId": "09e7bc55-77cc-42f3-9edb-e4799debf29b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(data['cloud (daily summary)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p-JSuT47S_jA",
    "outputId": "a8569b18-aa8d-48cd-e8a2-36f982ab7f36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rain    83556\n",
      "snow    10712\n",
      "Name: Precip Type, dtype: int64\n",
      "0    83556\n",
      "1    10712\n",
      "Name: Precip Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#label encoding precip type column\n",
    "print(data['Precip Type'].value_counts())\n",
    "data['Precip Type']=data['Precip Type'].replace(['rain','snow'],[0,1])\n",
    "print(data['Precip Type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dag42LLkS7DH",
    "outputId": "daa12ca3-46df-4235-8218-220fc086b007"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94268 entries, 0 to 94267\n",
      "Data columns (total 17 columns):\n",
      " #   Column                    Non-Null Count  Dtype              \n",
      "---  ------                    --------------  -----              \n",
      " 0   Formatted Date            94268 non-null  datetime64[ns, UTC]\n",
      " 1   Summary                   94268 non-null  object             \n",
      " 2   Precip Type               94268 non-null  int64              \n",
      " 3   Temperature (C)           94268 non-null  float64            \n",
      " 4   Apparent Temperature (C)  94268 non-null  float64            \n",
      " 5   Humidity                  94268 non-null  float64            \n",
      " 6   Wind Speed (km/h)         94268 non-null  float64            \n",
      " 7   Wind Bearing (degrees)    94268 non-null  int64              \n",
      " 8   Visibility (km)           94268 non-null  float64            \n",
      " 9   Pressure (millibars)      94268 non-null  float64            \n",
      " 10  Daily Summary             94268 non-null  object             \n",
      " 11  year                      94268 non-null  int64              \n",
      " 12  month                     94268 non-null  int64              \n",
      " 13  day                       94268 non-null  int64              \n",
      " 14  hour                      94268 non-null  int64              \n",
      " 15  cloud (summary)           94268 non-null  int64              \n",
      " 16  cloud (daily summary)     94268 non-null  int64              \n",
      "dtypes: datetime64[ns, UTC](1), float64(6), int64(8), object(2)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r85LkyawUJHQ",
    "outputId": "7152f533-d883-4ff4-99b3-d373dcf75fe4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94268 entries, 0 to 94267\n",
      "Data columns (total 14 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Precip Type               94268 non-null  int64  \n",
      " 1   Temperature (C)           94268 non-null  float64\n",
      " 2   Apparent Temperature (C)  94268 non-null  float64\n",
      " 3   Humidity                  94268 non-null  float64\n",
      " 4   Wind Speed (km/h)         94268 non-null  float64\n",
      " 5   Wind Bearing (degrees)    94268 non-null  int64  \n",
      " 6   Visibility (km)           94268 non-null  float64\n",
      " 7   Pressure (millibars)      94268 non-null  float64\n",
      " 8   year                      94268 non-null  int64  \n",
      " 9   month                     94268 non-null  int64  \n",
      " 10  day                       94268 non-null  int64  \n",
      " 11  hour                      94268 non-null  int64  \n",
      " 12  cloud (summary)           94268 non-null  int64  \n",
      " 13  cloud (daily summary)     94268 non-null  int64  \n",
      "dtypes: float64(6), int64(8)\n",
      "memory usage: 10.1 MB\n"
     ]
    }
   ],
   "source": [
    "data = data.drop('Formatted Date' , axis = 1)\n",
    "data = data.drop('Summary' , axis = 1)\n",
    "data = data.drop('Daily Summary' , axis = 1)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j57wu4vVGwCz"
   },
   "source": [
    "**DATA AUGMENTATION**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "id": "LUaH-2iTUW-h"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZyJF1AerTuaw",
    "outputId": "a163ec87-b480-47af-a56e-d23e7b6a3ba1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.594957745482833\n"
     ]
    }
   ],
   "source": [
    "#                                          decision tree \n",
    "\n",
    "X = data.drop('cloud (daily summary)', axis=1)\n",
    "y = data['cloud (daily summary)']\n",
    "\n",
    "dt1 = DecisionTreeClassifier()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=128)\n",
    "\n",
    "dt1.fit(X_train , y_train)\n",
    "y_pred = dt1.predict(X_test)\n",
    "print(\"accuracy score\",accuracy_score(y_test,y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bm5Nv1PEZ3TV",
    "outputId": "baa6b9b4-3b96-411e-c43c-e412e638f1b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28281 entries, 89630 to 40315\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Precip Type               28281 non-null  int64  \n",
      " 1   Temperature (C)           28281 non-null  float64\n",
      " 2   Apparent Temperature (C)  28281 non-null  float64\n",
      " 3   Humidity                  28281 non-null  float64\n",
      " 4   Wind Speed (km/h)         28281 non-null  float64\n",
      " 5   Wind Bearing (degrees)    28281 non-null  int64  \n",
      " 6   Visibility (km)           28281 non-null  float64\n",
      " 7   Pressure (millibars)      28281 non-null  float64\n",
      " 8   year                      28281 non-null  int64  \n",
      " 9   month                     28281 non-null  int64  \n",
      " 10  day                       28281 non-null  int64  \n",
      " 11  hour                      28281 non-null  int64  \n",
      " 12  cloud (summary)           28281 non-null  int64  \n",
      "dtypes: float64(6), int64(7)\n",
      "memory usage: 3.0 MB\n"
     ]
    }
   ],
   "source": [
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sRjVOzYdZ-uO",
    "outputId": "6748ea2a-42e8-4769-896c-bf83ebfba68b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2185 entries, 0 to 2184\n",
      "Data columns (total 12 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Formatted Date            2185 non-null   object \n",
      " 1   Summary                   2185 non-null   object \n",
      " 2   Precip Type               1811 non-null   object \n",
      " 3   Temperature (C)           2185 non-null   float64\n",
      " 4   Apparent Temperature (C)  2185 non-null   float64\n",
      " 5   Humidity                  2185 non-null   float64\n",
      " 6   Wind Speed (km/h)         2185 non-null   float64\n",
      " 7   Wind Bearing (degrees)    2185 non-null   int64  \n",
      " 8   Visibility (km)           2185 non-null   float64\n",
      " 9   Cloud Cover               2185 non-null   int64  \n",
      " 10  Pressure (millibars)      2185 non-null   float64\n",
      " 11  Daily Summary             2185 non-null   object \n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 205.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_dataset = pd.read_csv('test.csv')\n",
    "test_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wELUdnbsaYdX"
   },
   "source": [
    "**CLEANING THE TEST_DATA_SET**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DJrZhbNWafR4",
    "outputId": "e2d9556a-728f-48ab-a742-7fd2dbe65b73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date                0\n",
       "Summary                       0\n",
       "Precip Type                 374\n",
       "Temperature (C)               0\n",
       "Apparent Temperature (C)      0\n",
       "Humidity                      0\n",
       "Wind Speed (km/h)             0\n",
       "Wind Bearing (degrees)        0\n",
       "Visibility (km)               0\n",
       "Cloud Cover                   0\n",
       "Pressure (millibars)          0\n",
       "Daily Summary                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "id": "3snqEUuQUXs3"
   },
   "outputs": [],
   "source": [
    "test_dataset = test_dataset.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sgb3chnLdjgg",
    "outputId": "e5dc1ec6-2891-4c1b-dff1-16d5f47291a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1811 entries, 24 to 2184\n",
      "Data columns (total 12 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Formatted Date            1811 non-null   object \n",
      " 1   Summary                   1811 non-null   object \n",
      " 2   Precip Type               1811 non-null   object \n",
      " 3   Temperature (C)           1811 non-null   float64\n",
      " 4   Apparent Temperature (C)  1811 non-null   float64\n",
      " 5   Humidity                  1811 non-null   float64\n",
      " 6   Wind Speed (km/h)         1811 non-null   float64\n",
      " 7   Wind Bearing (degrees)    1811 non-null   int64  \n",
      " 8   Visibility (km)           1811 non-null   float64\n",
      " 9   Cloud Cover               1811 non-null   int64  \n",
      " 10  Pressure (millibars)      1811 non-null   float64\n",
      " 11  Daily Summary             1811 non-null   object \n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 183.9+ KB\n"
     ]
    }
   ],
   "source": [
    "test_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nG3gqLTDa4HY",
    "outputId": "80a80111-368a-4fba-8af0-67d662fc5892"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1811 entries, 24 to 2184\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype              \n",
      "---  ------                    --------------  -----              \n",
      " 0   Formatted Date            1811 non-null   datetime64[ns, UTC]\n",
      " 1   Summary                   1811 non-null   object             \n",
      " 2   Precip Type               1811 non-null   object             \n",
      " 3   Temperature (C)           1811 non-null   float64            \n",
      " 4   Apparent Temperature (C)  1811 non-null   float64            \n",
      " 5   Humidity                  1811 non-null   float64            \n",
      " 6   Wind Speed (km/h)         1811 non-null   float64            \n",
      " 7   Wind Bearing (degrees)    1811 non-null   int64              \n",
      " 8   Visibility (km)           1811 non-null   float64            \n",
      " 9   Cloud Cover               1811 non-null   int64              \n",
      " 10  Pressure (millibars)      1811 non-null   float64            \n",
      " 11  Daily Summary             1811 non-null   object             \n",
      " 12  year                      1811 non-null   int64              \n",
      " 13  month                     1811 non-null   int64              \n",
      " 14  day                       1811 non-null   int64              \n",
      " 15  hour                      1811 non-null   int64              \n",
      "dtypes: datetime64[ns, UTC](1), float64(6), int64(6), object(3)\n",
      "memory usage: 240.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#'Formatted Date' transformation:\n",
    "test_dataset['Formatted Date']=pd.to_datetime(test_dataset['Formatted Date'],utc=True)\n",
    "test_dataset['year'] = test_dataset['Formatted Date'].dt.year\n",
    "test_dataset['month'] = test_dataset['Formatted Date'].dt.month\n",
    "test_dataset['day'] = test_dataset['Formatted Date'].dt.day\n",
    "test_dataset['hour'] = test_dataset['Formatted Date'].dt.hour\n",
    "\n",
    "test_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "id": "BhhdXiJRwUN9"
   },
   "outputs": [],
   "source": [
    "test_dataset[\"cloud (summary)\"] = test_dataset.apply (lambda row:cloud_categorizer(row[\"Summary\"]) , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "jJSGmShDbrNE",
    "outputId": "d02579f8-b789-4607-9437-3c1ae9263406"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '1=Clear'),\n",
       " Text(0, 0, '2=Partly Cloudy'),\n",
       " Text(0, 0, '3=Mostly Cloudy'),\n",
       " Text(0, 0, '4=Overcast'),\n",
       " Text(0, 0, '5=Foggy')]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cyX1AP0mD6qqbmVl39fRnmgMjYlmefhYYmKcHA4sryy3JZWZmLaXXTgRFRABRup6kCZJmS5q9YsWKGmpmZta1ng7N5xrd7vx3eS5fCgytLDckl71DREyMiPaIaG9ra6u1smZmHfV0aE4HxuXpccC0Svlx+Sz6KGBlpRtvZtYyahsaTtJPgEOAAZKWAN8CzgWmSBoPPAMclRe/DTgSWAS8BpxQV73MzNZFbaEZEcd2cdfoTpYN4MS66mJmtr74iiAzswIOTTOzAg5NM7MCDk0zswIOTTOzAg5NM7MCDk0zswIOTTOzAg5NM7MCDk0zswIOTTOzAg5NM7MCDk0zswIOTTOzAg5NM7MCDk0zswIOTTOzAg5NM7MCDk0zswK1/UaQmW16Lj31p71dhSInff+Txeu4pWlmVsChaWZWwKFpZlbAoWlmVsChaWZWwKFpZlbAoWlmVqClQlPS4ZKekLRI0pm9XR8zs45aJjQlbQ78M3AEsCdwrKQ9e7dWZmara5nQBPYHFkXEUxHxR+A6YEwv18nMbDWtFJqDgcWV+SW5zMysZSgiersOAEj6HHB4RPyPPP8l4MMRcVKH5SYAE/Ls7sATPVjNAcDzPfh4Pc37t+HamPcNen7/3hsRbZ3d0UoDdiwFhlbmh+Sy1UTERGBiT1WqStLsiGjvjcfuCd6/DdfGvG/QWvvXSt3zB4HhknaVtCVwDDC9l+tkZraalmlpRsSbkk4Cfg5sDlwZEY/2crXMzFbTMqEJEBG3Abf1dj3WoFc+FuhB3r8N18a8b9BC+9cyJ4LMzDYErfSZpplZy9voQ1PSlZKWS1pQuN7+ku7Ol3XOlXS5pG0kHS/p0m7UY6ikOyU9JulRSScXrHuIpJWS5klaKOlbhY89tnp1laSr81e8ml1/C0nnSnpS0kOSfiXpiHzfM7ns4bxf3y7Y7vGSQtJhkoZJWpDrGiX1q2yvzv18WtKAwvpsno+dWytlw9Z0LOZ9mJ//z49IGlvymHWT9A9NLvd0rv88SbObXKd6nM+T9It1q209NvrQBK4GDi9ZQdJA4AbgjIjYPSL2BW4Htl+HerwJnBoRewKjgBMLLxO9JyJGAO3AFyWNbGYlSX2AsaRLU7vrO8AgYK+IGJm313guAvh0ROwDjAAOlzSqYNuPkL4p0XAs8HA361nnfnbHycDCZheWtA9wPjAmIj4AfAo4X9Le61CHxrbX1/mLpkIz+4uIGFH4VaF78jojIuKw0sr1hI0+NCPibuDFwtVOBCZFxK8q27kxIp6rLiSpTdJNkh7MtwNz+f65lTJX0r9J2j0ilgF7S5oOTAP6040rniLiVWAOsJukb+bHXSBpoiTlx79L0g/yO/wZpBff9/K79/sr9T9U0i2V+Y9LmtphH7cB/ifw1Yh4I9fhuYiYUlns1fz368DewLWSTsnrr9aykvR3ks7Os+8F3gN8Afgq6XjcDXg3MCwvP1rSK7n1d6WkrXL5ubnVPl/S+ZI+0gP72Vj26/k5X7CG/fwOcBJwObBjbok/TDq2GsvcLWlEZdMzgGsi4jf58X8D/BNwmqQ9JD1QWXeYpEfy9H6SZkmaI+nnkgbl8upxcLKkD+Xj8WFJD0jaPm/nntyyfig/j0galOs3L+/nxySdC2ydy67t+LzURdKxudW6QNJ5lfLxkv4978u/KvcAJb1f0n15ne9KeiWXT6623CVdK6n8Uu2I2OhvpBfggsr8acC8Tm4X5/tvJr3bd7at44FL8/SPgY/m6V2AhXl6B6BPnj4MuKmy7hJgH+C3wA5N1ucQ4NY8vTPwNPBBoH+lXtcAn8zTdwE/rNx3NfC5jvOAgMeBtsr+fLLD/u4NzF3Dc/s08C7SlVlvARcA2wGPAj8CHgP+UNmnpcAv8rqL83N9AXBTnv8WcA9wK9AXWAY8kpefDJySn4MnWHUis18P7ecAYD9S63jbyn7uyzuPsfnAv+T/3e+Ag4ALgeWV5+MZYAVwJvBnpDeffTo87j7AQ3l6HrBrnj4DOAvYAvi3yr4dTfq63mrHAbAl8BTwoeoxCmwD9M1lw4HZefpU4Bt5enNg+zz9SpOvud8AD5He4CcUHOcrK+XfIL2p/hZoy/W9g9QDeE/+n/TPz8E9rHpd3gocm6f/ulFn4GDgljy9Y65jn9I8aamvHPWUiPge8L31sKnDgD1zAw9gB0nbkf4hkyQNJ3Vft6iscydwJXBKRPyuoD4fkzQXeBs4NyIelfRZSaeTDvz+pBdw4zdUr19b5SMiJF1D6u5fBRwAHLe29TrxNvBD0oG8PylAbiYFwlmkwB8BqaUJbCepX673f5AGZ/km6Xn7Cam1eRjpjeE/SeEDMInUUrsUeB24Qunzwj99ZljzfgJ8FJgaqcWPpJuBj1G5EEPSJ4BXSIG/HbBFpB7P3fnxfxwRI3Lrdj7wfdJHA2vrEU0hheK5+e/RpEuJ9wJm5ONw8/y4DY3jYHdgWUQ8CNA49iRtC1yaW7xvkcIb0sUmV0raghQ085p+hpKPRsRSSe/KdXu8yeP8noj4RGMmtwTviogVef5a0hsQwKyIeDGX31Cp+wGkYIX0Bnl+3udZkn4oqQ34LKkx82bhfm2aoSnpNFKXsKO7I+JrpPDZj9SNXpPNgFER8XqH7V8K3BkRn5Y0jPSOD+mAPhj4QUTcXFAfeOfB1JcUVO0RsTh3eftW1n2V5lxFCtrXgRs6OYgWAbtI2qHxQluDN0hvCo3PkD8NfAV4v6TGi24g6fn9QWOliHggv8H0iYh/l/QWKUwOIbUwVusKRroQYn9gNKkleRJwaA/uZ2feZNXHXQeSAn9P0nOylaQfkd5EjgDeW3k++uW6fQT4Fem4q36mux/p+YIUgDfkoI6IeFLSnwOPRsQBXdRrbcfB3wLPkVq0m5GeHyLibkkHAX8FXC3pgoiYvJZt/UlELM1/l+ePQvaX9GHWfpzXbTLwRdLn6Cd0awulTdMN8UaHrlMTyw8kdZ0+XCn7TC4/ntW756dVlhmR/04FPpunzyZ1IwTcC8zrRv0PIXfPK2X9SAf71qTWzALg7FjVLWuvLHsJcEJl/mpW78b+lNRt/kAXj/9/SC/sLfN8G/D5PP1b4H3ASFK39V7Su/gCUrd1C9JACzsDWwH3Veq5mFUfXUwBflOp3+mkVuirwG6V8pPz/r4rVnWzXuiB/Xya1D0fSQr0bUhd9DXuJ6u6542Pcc5j9W78fnk/ryedSHsSGFY5bheRj6tc9iDpo5jT8/yWeZkD8vwWwAc7Hge8s3u+PanRdCHpBCWkEIk8/V5g8zx9EumNHuAlUst5Tcfrtqzqzm9L+vjg8G4e54NIr8UBpEbHL0hDRg7O/5Od8n7MYtXr8mfA0Xl6ApWPFFj12r6/u3my0Z8IkvQT0jv47pKWSBq/tnUinfA5hnTm8glJC4G/BH7fYdGvAe35ZMRjpM9PIL34/il3pxut+QNJrYkhWvWViiO7u18R8TLwr6QX7c9JL6auXEc6mTC3eoKk4lpgcUR0dab3LFIr6bF8suNWUhBAOpBvIQXUAFLX+tvA5RExNyL+EzgHeIB0ouPxynYvBw7Ora6nWb1V9FR+jO+QWlePkD4G+L+kF/ytkuYDvySdgKp7PwGIiIfyvj4A3N/kfj4M/HPeT3XY3pz8GFdF6gKfAfxU0uOkkD89Vu8aX09qKU3J6/+R1No+L59omkc6zlaTlzsauCQvN4PUM/khMC6X7cGq/8EhwMP5GD4auCiXTwTmr+VE0EDgl3mbDwA/i4jb17B8lyKdQD2T1IN5GJgTEdMitWT/d97+vaTjZ2Ve7RTg6/n42K1S3nhtLyS9OXaLrwiyxscJcyPiit6uS4Ok95BaSntExNvraZubxH5uKiRtFxGvKH2dairpBNjU/FnxHyIiJB1DOik0Jq+zDalHNDIiVna99a5t9C1NWzNJc0hnjn/U23VpkHQcqRX3jfUYmJvEfm5izs6t9wWkM+GNr5XtB8zLLc3/RfomAJIOI7UyL+luYIJbmmZmRdzSNDMr4NA0Myvg0DQzK+DQtFpIOjtf/bM+tnWXpE4HfZB0o6T3rY/H6U2SfiFpp96uh62dQ9M2WJI+SPoC9lO9XZdmSNp8DXdfQzrTay3OoWnrTNJx+Qv+D+drvDvePyKPOjNf0tRGi6ragpQ0QNLTeXprSdcpjSk5lXTVU2e+QL7UVWnsyquVRsJ5RNLfruUxjpd0i6QZSmM/nqQ0etHcXNf+lfUvlDQ71+dDkm5WGnXpu5V9vEVplKFHlX5mulH+iqTv5y96f0Ndj7Y0nTQsnrW4TfLac1t/cmvvLOAjEfF8I2w6mEwacm2WpHNIIxmdsobN/g3wWkR8QGksyYe6WO5A0gAfkC5BHBwRe+V69Wui+nuRLoHsS7oU8YyI2FfShaQBPRrXx/8xItqVBo6eRvoe4IvAryVdGBEvAF+OiBclbQ08KOmmXL4t6ZK9UyUJWCipLdIAFCeQBm8hIl6StJWknfN61qLc0rR1dShpAIznASKPOtMgaUfS0G2zctEkVo1S05WDyF9Cj4j5pGu9OzOIdNkjpMsu3yfpEkmH0+Hyxy7cGRG/zwG2klUjRD1CHs8zm14pfzQilkUac/MpYGi+72u5NXlfLhuey98iDXvXuKi7MdpSP9JoPP+v8jjLSSNFWQtzS9N6U3VkoL5rWrALf2isl1tq+5DGCPhr4Cjgy2t5jDcq029X5t9m9dfGG50s86flJB1CGsrugIh4TdJdlcd6PSLeqqyzptGW+uZ9shbmlqatqzuAz0vaGaBj9zxfrvaSpI/loi+RRqSBNMjCfnm6+ls+dwP/PW9vL9Llj51ZSBqQAaXf79ksIm4ifVzQ+DmQrh5jfdoReCkH5h6knzPpVET8B2lUo7OoDBqRu+7vzvW1FubQtHUSEY8C/wjMyt3TCzpZbBzpZyjmkz57PCeXnw/8TR5Jp/qjZZeRBipemJed08XD/4w0Gg+kocLuytci/wj4+7U8xvp0O6nFuZA0QPB9a1m+s9GW9gPui24Mims9y9ee2wYrn3S5EziwQxe4pXU22pKki4DpETGz92pmzXBL0zZYEfEH0pn44h+o6y1rGG1pgQNzw+CWpplZAbc0zcwKODTNzAo4NM3MCjg0zcwKODTNzAo4NM3MCvwX2Spn2v85aKgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(test_dataset['cloud (summary)'])\n",
    "ax.set_xticklabels(('1=Clear', '2=Partly Cloudy', '3=Mostly Cloudy', '4=Overcast', '5=Foggy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_9mBGdeicDKO",
    "outputId": "5bbef9be-d9e6-45d3-f057-f7eef303f74d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rain    1811\n",
      "Name: Precip Type, dtype: int64\n",
      "0    1811\n",
      "Name: Precip Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#label encoding precip type column\n",
    "print(test_dataset['Precip Type'].value_counts())\n",
    "test_dataset['Precip Type']=test_dataset['Precip Type'].replace(['rain','snow'],[0,1])\n",
    "print(test_dataset['Precip Type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "id": "PWkp0gI0cShN"
   },
   "outputs": [],
   "source": [
    "test_dataset[\"cloud (daily summary)\"] = test_dataset.apply (lambda row:cloud_categorizer2(row[\"Daily Summary\"]) , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "S9cura8rxQaO",
    "outputId": "3856fbd9-7423-49e3-e7e5-c93de71a78a9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(test_dataset['cloud (daily summary)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "id": "Jl_-wmPUyOQA"
   },
   "outputs": [],
   "source": [
    "test_dataset=test_dataset.drop('Cloud Cover',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FdlAeyBucncC",
    "outputId": "1189770f-0b37-44ac-ebb4-862a28dbf9b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1811 entries, 24 to 2184\n",
      "Data columns (total 15 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Precip Type               1811 non-null   int64  \n",
      " 1   Temperature (C)           1811 non-null   float64\n",
      " 2   Apparent Temperature (C)  1811 non-null   float64\n",
      " 3   Humidity                  1811 non-null   float64\n",
      " 4   Wind Speed (km/h)         1811 non-null   float64\n",
      " 5   Wind Bearing (degrees)    1811 non-null   int64  \n",
      " 6   Visibility (km)           1811 non-null   float64\n",
      " 7   Cloud Cover               1811 non-null   int64  \n",
      " 8   Pressure (millibars)      1811 non-null   float64\n",
      " 9   year                      1811 non-null   int64  \n",
      " 10  month                     1811 non-null   int64  \n",
      " 11  day                       1811 non-null   int64  \n",
      " 12  hour                      1811 non-null   int64  \n",
      " 13  cloud (summary)           1811 non-null   int64  \n",
      " 14  cloud (daily summary)     1811 non-null   int64  \n",
      "dtypes: float64(6), int64(9)\n",
      "memory usage: 290.9 KB\n"
     ]
    }
   ],
   "source": [
    "test_dataset = test_dataset.drop('Formatted Date' , axis = 1)\n",
    "test_dataset = test_dataset.drop('Summary' , axis = 1)\n",
    "test_dataset = test_dataset.drop('Daily Summary' , axis = 1)\n",
    "test_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "id": "wFiqp9DhenNi"
   },
   "outputs": [],
   "source": [
    "X_test_dataset = test_dataset.drop('cloud (daily summary)', axis=1)\n",
    "y_test_dataset = test_dataset['cloud (daily summary)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qKLtfVn6PF4r",
    "outputId": "ecc2529c-7a16-4434-9926-801fcf8b16e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1811 entries, 24 to 2184\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Precip Type               1811 non-null   int64  \n",
      " 1   Temperature (C)           1811 non-null   float64\n",
      " 2   Apparent Temperature (C)  1811 non-null   float64\n",
      " 3   Humidity                  1811 non-null   float64\n",
      " 4   Wind Speed (km/h)         1811 non-null   float64\n",
      " 5   Wind Bearing (degrees)    1811 non-null   int64  \n",
      " 6   Visibility (km)           1811 non-null   float64\n",
      " 7   Pressure (millibars)      1811 non-null   float64\n",
      " 8   year                      1811 non-null   int64  \n",
      " 9   month                     1811 non-null   int64  \n",
      " 10  day                       1811 non-null   int64  \n",
      " 11  hour                      1811 non-null   int64  \n",
      " 12  cloud (summary)           1811 non-null   int64  \n",
      "dtypes: float64(6), int64(7)\n",
      "memory usage: 262.6 KB\n"
     ]
    }
   ],
   "source": [
    "X_test_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ftVINsznHxHf"
   },
   "source": [
    "**ACCURACY PREDICTION ON GIVEN TEST DATA**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1isWHC2oetP0",
    "outputId": "8eede2eb-5a35-4ab5-8c1e-f59797c151f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score 0.18277194919933737\n"
     ]
    }
   ],
   "source": [
    "y_pred_test_dataset = dt1.predict(X_test_dataset)\n",
    "print(\"accuracy score\",accuracy_score(y_test_dataset,y_pred_test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OGTzHWNQfoZk",
    "outputId": "09c2cfe1-d5d8-498e-bd76-fcac2014bb0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  5 18 ...  2 13  2]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred_test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E1MA0soMIDRY"
   },
   "source": [
    "EVALUATE SAMPLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ug2Stt74IKef",
    "outputId": "a895e626-2157-4d09-9a75-90578745046b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 719 entries, 0 to 718\n",
      "Data columns (total 11 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Formatted Date            719 non-null    object \n",
      " 1   Summary                   719 non-null    object \n",
      " 2   Precip Type               719 non-null    object \n",
      " 3   Temperature (C)           719 non-null    float64\n",
      " 4   Apparent Temperature (C)  719 non-null    float64\n",
      " 5   Humidity                  719 non-null    float64\n",
      " 6   Wind Speed (km/h)         719 non-null    float64\n",
      " 7   Wind Bearing (degrees)    719 non-null    int64  \n",
      " 8   Visibility (km)           719 non-null    float64\n",
      " 9   Cloud Cover               719 non-null    int64  \n",
      " 10  Pressure (millibars)      719 non-null    float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 61.9+ KB\n"
     ]
    }
   ],
   "source": [
    "evaluate = pd.read_csv('evaluate.csv')\n",
    "evaluate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O2UvSe1sIW7A",
    "outputId": "14c141a9-6d42-491a-ed47-cfa9ad46dda4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 719 entries, 0 to 718\n",
      "Data columns (total 15 columns):\n",
      " #   Column                    Non-Null Count  Dtype              \n",
      "---  ------                    --------------  -----              \n",
      " 0   Formatted Date            719 non-null    datetime64[ns, UTC]\n",
      " 1   Summary                   719 non-null    object             \n",
      " 2   Precip Type               719 non-null    object             \n",
      " 3   Temperature (C)           719 non-null    float64            \n",
      " 4   Apparent Temperature (C)  719 non-null    float64            \n",
      " 5   Humidity                  719 non-null    float64            \n",
      " 6   Wind Speed (km/h)         719 non-null    float64            \n",
      " 7   Wind Bearing (degrees)    719 non-null    int64              \n",
      " 8   Visibility (km)           719 non-null    float64            \n",
      " 9   Cloud Cover               719 non-null    int64              \n",
      " 10  Pressure (millibars)      719 non-null    float64            \n",
      " 11  year                      719 non-null    int64              \n",
      " 12  month                     719 non-null    int64              \n",
      " 13  day                       719 non-null    int64              \n",
      " 14  hour                      719 non-null    int64              \n",
      "dtypes: datetime64[ns, UTC](1), float64(6), int64(6), object(2)\n",
      "memory usage: 84.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#'Formatted Date' transformation:\n",
    "evaluate['Formatted Date']=pd.to_datetime(evaluate['Formatted Date'],utc=True)\n",
    "evaluate['year'] = evaluate['Formatted Date'].dt.year\n",
    "evaluate['month'] = evaluate['Formatted Date'].dt.month\n",
    "evaluate['day'] = evaluate['Formatted Date'].dt.day\n",
    "evaluate['hour'] = evaluate['Formatted Date'].dt.hour\n",
    "\n",
    "evaluate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "GxpKuZcvMHG4",
    "outputId": "bbde484e-c1b7-476d-c128-c3c53cd6eb6d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '1=Clear'),\n",
       " Text(0, 0, '2=Partly Cloudy'),\n",
       " Text(0, 0, '3=Mostly Cloudy'),\n",
       " Text(0, 0, '4=Overcast'),\n",
       " Text(0, 0, '5=Foggy')]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate[\"cloud (summary)\"] = evaluate.apply (lambda row:cloud_categorizer(row[\"Summary\"]) , axis = 1)\n",
    "ax=sns.countplot(evaluate['cloud (summary)'])\n",
    "ax.set_xticklabels(('1=Clear', '2=Partly Cloudy', '3=Mostly Cloudy', '4=Overcast', '5=Foggy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8-zknoQrMjEB",
    "outputId": "730fe149-a05b-4e09-c7ed-14435327868e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rain    719\n",
      "Name: Precip Type, dtype: int64\n",
      "0    719\n",
      "Name: Precip Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#label encoding precip type column\n",
    "print(evaluate['Precip Type'].value_counts())\n",
    "evaluate['Precip Type']=evaluate['Precip Type'].replace(['rain','snow'],[0,1])\n",
    "print(evaluate['Precip Type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uAj4jpNrNfMy",
    "outputId": "25bfb11a-6b48-4f66-c501-a1fb6883a617"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 719 entries, 0 to 718\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype              \n",
      "---  ------                    --------------  -----              \n",
      " 0   Formatted Date            719 non-null    datetime64[ns, UTC]\n",
      " 1   Summary                   719 non-null    object             \n",
      " 2   Precip Type               719 non-null    int64              \n",
      " 3   Temperature (C)           719 non-null    float64            \n",
      " 4   Apparent Temperature (C)  719 non-null    float64            \n",
      " 5   Humidity                  719 non-null    float64            \n",
      " 6   Wind Speed (km/h)         719 non-null    float64            \n",
      " 7   Wind Bearing (degrees)    719 non-null    int64              \n",
      " 8   Visibility (km)           719 non-null    float64            \n",
      " 9   Cloud Cover               719 non-null    int64              \n",
      " 10  Pressure (millibars)      719 non-null    float64            \n",
      " 11  year                      719 non-null    int64              \n",
      " 12  month                     719 non-null    int64              \n",
      " 13  day                       719 non-null    int64              \n",
      " 14  hour                      719 non-null    int64              \n",
      " 15  cloud (summary)           719 non-null    int64              \n",
      "dtypes: datetime64[ns, UTC](1), float64(6), int64(8), object(1)\n",
      "memory usage: 90.0+ KB\n"
     ]
    }
   ],
   "source": [
    "evaluate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KFWIq3sbNiY2",
    "outputId": "7981442d-0e87-4737-a027-5ef054380b73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 719 entries, 0 to 718\n",
      "Data columns (total 14 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Precip Type               719 non-null    int64  \n",
      " 1   Temperature (C)           719 non-null    float64\n",
      " 2   Apparent Temperature (C)  719 non-null    float64\n",
      " 3   Humidity                  719 non-null    float64\n",
      " 4   Wind Speed (km/h)         719 non-null    float64\n",
      " 5   Wind Bearing (degrees)    719 non-null    int64  \n",
      " 6   Visibility (km)           719 non-null    float64\n",
      " 7   Cloud Cover               719 non-null    int64  \n",
      " 8   Pressure (millibars)      719 non-null    float64\n",
      " 9   year                      719 non-null    int64  \n",
      " 10  month                     719 non-null    int64  \n",
      " 11  day                       719 non-null    int64  \n",
      " 12  hour                      719 non-null    int64  \n",
      " 13  cloud (summary)           719 non-null    int64  \n",
      "dtypes: float64(6), int64(8)\n",
      "memory usage: 78.8 KB\n"
     ]
    }
   ],
   "source": [
    "evaluate = evaluate.drop('Formatted Date' , axis = 1)\n",
    "evaluate = evaluate.drop('Summary' , axis = 1)\n",
    "evaluate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "id": "fTe98yw-PZB-"
   },
   "outputs": [],
   "source": [
    "evaluate = evaluate.drop('Cloud Cover' , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "id": "xlj_R_k2N53s"
   },
   "outputs": [],
   "source": [
    "X_evaluate = evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NvFwq5jfOHW2",
    "outputId": "f0fe0edd-8b0d-4258-f614-98bb6ae682e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "719\n"
     ]
    }
   ],
   "source": [
    "y_pred_evaluate = dt1.predict(X_evaluate)\n",
    "print(len(y_pred_evaluate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BlHAurtRWJNX",
    "outputId": "9fbdbc1e-22b8-4eaf-9b21-d1abe28318df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 719 entries, 0 to 718\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   0       719 non-null    int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 5.7 KB\n"
     ]
    }
   ],
   "source": [
    "evaluate_ypred = pd.DataFrame(y_pred_evaluate)\n",
    "evaluate_ypred.info()\n",
    "evaluate_ypred.to_csv('evaluate_ypred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Kwb1aIjCQM3O"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "DS_EICT1142.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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