rain_in_australia_prediction_via_lrc_nbc_rfc.ipynb

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   "source": [
    "DMBI PROJECT - **RAIN** **IN** **AUSTRALIA** **PREDICTION** **USING** **LRC**, **NBC** **AND** **RFC** \n",
    "\n",
    "TEAM MEMBERS - \n",
    "\n",
    "**JINAL** **VYAS** (**191310132019**)\n",
    "\n",
    "**KRUPA** **BHAVSAR** (**191310132029**)"
   ]
  },
  {
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   "execution_count": 2,
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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e98ea7",
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     "start_time": "2022-01-21T22:23:13.957521",
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    "tags": []
   },
   "source": [
    "**LOADING DATA**"
   ]
  },
  {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>21.8</td>\n",
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       "      <td>No</td>\n",
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       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
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       "      <td>1007.8</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
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       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
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       "      <td>No</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "0           W           44.0          W  ...        71.0         22.0   \n",
       "1         WNW           44.0        NNW  ...        44.0         25.0   \n",
       "2         WSW           46.0          W  ...        38.0         30.0   \n",
       "3          NE           24.0         SE  ...        45.0         16.0   \n",
       "4           W           41.0        ENE  ...        82.0         33.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
       "1       1010.6       1007.8       NaN       NaN     17.2     24.3         No   \n",
       "2       1007.6       1008.7       NaN       2.0     21.0     23.2         No   \n",
       "3       1017.6       1012.8       NaN       NaN     18.1     26.5         No   \n",
       "4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "data = 'weatherAUS.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "df.head()"
   ]
  },
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   "source": [
    "**Exploratory data analysis**"
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    {
     "data": {
      "text/plain": [
       "(145460, 23)"
      ]
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     "execution_count": 7,
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   ],
   "source": [
    "# checking shape of data\n",
    "df.shape"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145460 entries, 0 to 145459\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Date           145460 non-null  object \n",
      " 1   Location       145460 non-null  object \n",
      " 2   MinTemp        143975 non-null  float64\n",
      " 3   MaxTemp        144199 non-null  float64\n",
      " 4   Rainfall       142199 non-null  float64\n",
      " 5   Evaporation    82670 non-null   float64\n",
      " 6   Sunshine       75625 non-null   float64\n",
      " 7   WindGustDir    135134 non-null  object \n",
      " 8   WindGustSpeed  135197 non-null  float64\n",
      " 9   WindDir9am     134894 non-null  object \n",
      " 10  WindDir3pm     141232 non-null  object \n",
      " 11  WindSpeed9am   143693 non-null  float64\n",
      " 12  WindSpeed3pm   142398 non-null  float64\n",
      " 13  Humidity9am    142806 non-null  float64\n",
      " 14  Humidity3pm    140953 non-null  float64\n",
      " 15  Pressure9am    130395 non-null  float64\n",
      " 16  Pressure3pm    130432 non-null  float64\n",
      " 17  Cloud9am       89572 non-null   float64\n",
      " 18  Cloud3pm       86102 non-null   float64\n",
      " 19  Temp9am        143693 non-null  float64\n",
      " 20  Temp3pm        141851 non-null  float64\n",
      " 21  RainToday      142199 non-null  object \n",
      " 22  RainTomorrow   142193 non-null  object \n",
      "dtypes: float64(16), object(7)\n",
      "memory usage: 25.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking data info\n",
    "df.info()"
   ]
  },
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     "end_time": "2022-01-21T22:23:15.276825",
     "exception": false,
     "start_time": "2022-01-21T22:23:15.203722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     110316\n",
       "Yes     31877\n",
       "Name: RainTomorrow, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking target column values counts\n",
    "df['RainTomorrow'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18a375e9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:15.363451Z",
     "iopub.status.busy": "2022-01-21T22:23:15.362754Z",
     "iopub.status.idle": "2022-01-21T22:23:16.850792Z",
     "shell.execute_reply": "2022-01-21T22:23:16.850224Z",
     "shell.execute_reply.started": "2022-01-21T22:14:41.747108Z"
    },
    "id": "18a375e9",
    "outputId": "ba4f6723-d9d1-4101-8948-16a66e4dc4e7",
    "papermill": {
     "duration": 1.532911,
     "end_time": "2022-01-21T22:23:16.850939",
     "exception": false,
     "start_time": "2022-01-21T22:23:15.318028",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Target Count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting count of target column\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.countplot('RainTomorrow',data=df)\n",
    "plt.title(\"Target Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf11c252",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:16.940862Z",
     "iopub.status.busy": "2022-01-21T22:23:16.940141Z",
     "iopub.status.idle": "2022-01-21T22:23:17.062674Z",
     "shell.execute_reply": "2022-01-21T22:23:17.061967Z",
     "shell.execute_reply.started": "2022-01-21T18:47:38.863546Z"
    },
    "id": "bf11c252",
    "outputId": "1b647064-7996-4b68-e385-d6ba8a914cb5",
    "papermill": {
     "duration": 0.16857,
     "end_time": "2022-01-21T22:23:17.062818",
     "exception": false,
     "start_time": "2022-01-21T22:23:16.894248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 0\n",
       "Location             0\n",
       "MinTemp           1485\n",
       "MaxTemp           1261\n",
       "Rainfall          3261\n",
       "Evaporation      62790\n",
       "Sunshine         69835\n",
       "WindGustDir      10326\n",
       "WindGustSpeed    10263\n",
       "WindDir9am       10566\n",
       "WindDir3pm        4228\n",
       "WindSpeed9am      1767\n",
       "WindSpeed3pm      3062\n",
       "Humidity9am       2654\n",
       "Humidity3pm       4507\n",
       "Pressure9am      15065\n",
       "Pressure3pm      15028\n",
       "Cloud9am         55888\n",
       "Cloud3pm         59358\n",
       "Temp9am           1767\n",
       "Temp3pm           3609\n",
       "RainToday         3261\n",
       "RainTomorrow      3267\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce42153",
   "metadata": {
    "id": "bce42153",
    "papermill": {
     "duration": 0.042422,
     "end_time": "2022-01-21T22:23:17.148232",
     "exception": false,
     "start_time": "2022-01-21T22:23:17.105810",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see from the above output that data has lot of NAN values and we need to treat them for our model.First we will divide the data in category and integer datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e886643",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:17.256662Z",
     "iopub.status.busy": "2022-01-21T22:23:17.255936Z",
     "iopub.status.idle": "2022-01-21T22:23:17.311087Z",
     "shell.execute_reply": "2022-01-21T22:23:17.310540Z",
     "shell.execute_reply.started": "2022-01-21T22:16:55.909387Z"
    },
    "id": "8e886643",
    "outputId": "c2a1dea7-cafc-487e-bb2b-76ee3a827b31",
    "papermill": {
     "duration": 0.120336,
     "end_time": "2022-01-21T22:23:17.311239",
     "exception": false,
     "start_time": "2022-01-21T22:23:17.190903",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(145460, 23)\n",
      "(142193, 23)\n"
     ]
    }
   ],
   "source": [
    "# dropping NA values from target column\n",
    "print(df.shape)\n",
    "df.dropna(subset=['RainTomorrow'],inplace = True)\n",
    "df = df.reset_index(drop=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e4ec4c4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:17.407059Z",
     "iopub.status.busy": "2022-01-21T22:23:17.406299Z",
     "iopub.status.idle": "2022-01-21T22:23:17.409227Z",
     "shell.execute_reply": "2022-01-21T22:23:17.409766Z",
     "shell.execute_reply.started": "2022-01-21T22:16:58.870842Z"
    },
    "id": "4e4ec4c4",
    "outputId": "48db3c30-664a-4745-a942-77afc01ea3e2",
    "papermill": {
     "duration": 0.054635,
     "end_time": "2022-01-21T22:23:17.409939",
     "exception": false,
     "start_time": "2022-01-21T22:23:17.355304",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical data types columns: ['Date', 'Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday', 'RainTomorrow']\n",
      "Integer data types columns: ['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm']\n"
     ]
    }
   ],
   "source": [
    "categorical = [ i for i in df.columns if df[i].dtype =='O']\n",
    "integer = [i for i in df.columns if df[i].dtype !='O']\n",
    "print('Categorical data types columns:',categorical)\n",
    "print('Integer data types columns:',integer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f042e07a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:17.509672Z",
     "iopub.status.busy": "2022-01-21T22:23:17.508937Z",
     "iopub.status.idle": "2022-01-21T22:23:17.521731Z",
     "shell.execute_reply": "2022-01-21T22:23:17.522269Z",
     "shell.execute_reply.started": "2022-01-21T22:14:52.921489Z"
    },
    "id": "f042e07a",
    "outputId": "daab1c93-ea29-4ac9-abba-182c4beff44f",
    "papermill": {
     "duration": 0.068373,
     "end_time": "2022-01-21T22:23:17.522492",
     "exception": false,
     "start_time": "2022-01-21T22:23:17.454119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp            637\n",
       "MaxTemp            322\n",
       "Rainfall          1406\n",
       "Evaporation      60843\n",
       "Sunshine         67816\n",
       "WindGustSpeed     9270\n",
       "WindSpeed9am      1348\n",
       "WindSpeed3pm      2630\n",
       "Humidity9am       1774\n",
       "Humidity3pm       3610\n",
       "Pressure9am      14014\n",
       "Pressure3pm      13981\n",
       "Cloud9am         53657\n",
       "Cloud3pm         57094\n",
       "Temp9am            904\n",
       "Temp3pm           2726\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking NAN values in data type integer\n",
    "df[integer].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "456ab61d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:17.613529Z",
     "iopub.status.busy": "2022-01-21T22:23:17.612858Z",
     "iopub.status.idle": "2022-01-21T22:23:17.736720Z",
     "shell.execute_reply": "2022-01-21T22:23:17.736030Z",
     "shell.execute_reply.started": "2022-01-21T22:14:56.242557Z"
    },
    "id": "456ab61d",
    "outputId": "e15f9716-b33a-4d22-fb76-b56860531db0",
    "papermill": {
     "duration": 0.169904,
     "end_time": "2022-01-21T22:23:17.736907",
     "exception": false,
     "start_time": "2022-01-21T22:23:17.567003",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                0\n",
       "Location            0\n",
       "WindGustDir      9330\n",
       "WindDir9am      10013\n",
       "WindDir3pm       3778\n",
       "RainToday        1406\n",
       "RainTomorrow        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking NAN values in data type categorical\n",
    "df[categorical].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "53fbf1e0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:17.876866Z",
     "iopub.status.busy": "2022-01-21T22:23:17.876176Z",
     "iopub.status.idle": "2022-01-21T22:23:17.970746Z",
     "shell.execute_reply": "2022-01-21T22:23:17.970186Z",
     "shell.execute_reply.started": "2022-01-21T22:17:03.033925Z"
    },
    "id": "53fbf1e0",
    "outputId": "2c31f6a2-8171-401e-8a69-f2cad8da2cd2",
    "papermill": {
     "duration": 0.18872,
     "end_time": "2022-01-21T22:23:17.970889",
     "exception": false,
     "start_time": "2022-01-21T22:23:17.782169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-dfaeeac9-a4d0-4b38-b344-ca20498d4366\">\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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>WindDir3pm</th>\n",
       "      <th>...</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</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>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WNW</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>WSW</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>WSW</td>\n",
       "      <td>...</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfaeeac9-a4d0-4b38-b344-ca20498d4366')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
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       "    .colab-df-container {\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
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       "      fill: #174EA6;\n",
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       "\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",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-dfaeeac9-a4d0-4b38-b344-ca20498d4366 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-dfaeeac9-a4d0-4b38-b344-ca20498d4366');\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",
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       "  "
      ],
      "text/plain": [
       "  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine WindGustDir  \\\n",
       "0   Albury     13.4     22.9       0.6          NaN       NaN           W   \n",
       "1   Albury      7.4     25.1       0.0          NaN       NaN         WNW   \n",
       "2   Albury     12.9     25.7       0.0          NaN       NaN         WSW   \n",
       "3   Albury      9.2     28.0       0.0          NaN       NaN          NE   \n",
       "4   Albury     17.5     32.3       1.0          NaN       NaN           W   \n",
       "\n",
       "   WindGustSpeed WindDir9am WindDir3pm  ...  Pressure3pm  Cloud9am  Cloud3pm  \\\n",
       "0           44.0          W        WNW  ...       1007.1       8.0       NaN   \n",
       "1           44.0        NNW        WSW  ...       1007.8       NaN       NaN   \n",
       "2           46.0          W        WSW  ...       1008.7       NaN       2.0   \n",
       "3           24.0         SE          E  ...       1012.8       NaN       NaN   \n",
       "4           41.0        ENE         NW  ...       1006.0       7.0       8.0   \n",
       "\n",
       "   Temp9am  Temp3pm  RainToday  RainTomorrow  year  month  day  \n",
       "0     16.9     21.8         No            No  2008     12    1  \n",
       "1     17.2     24.3         No            No  2008     12    2  \n",
       "2     21.0     23.2         No            No  2008     12    3  \n",
       "3     18.1     26.5         No            No  2008     12    4  \n",
       "4     17.8     29.7         No            No  2008     12    5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convertig Date column in to date format and splitting it into year,month and date\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['year'] = df['Date'].dt.year\n",
    "df['month'] = df['Date'].dt.month\n",
    "df['day'] = df['Date'].dt.day\n",
    "df.drop('Date',axis = 1,inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c696249",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:18.067709Z",
     "iopub.status.busy": "2022-01-21T22:23:18.067039Z",
     "iopub.status.idle": "2022-01-21T22:23:18.615901Z",
     "shell.execute_reply": "2022-01-21T22:23:18.616639Z",
     "shell.execute_reply.started": "2022-01-21T22:17:05.002775Z"
    },
    "id": "7c696249",
    "outputId": "e778bbfd-ad45-4bef-d494-715c0e07b9c5",
    "papermill": {
     "duration": 0.601139,
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     "exception": false,
     "start_time": "2022-01-21T22:23:18.015745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "string\n"
     ]
    }
   ],
   "source": [
    "df[['year', 'month','day']] = df[['year', 'month','day']].astype('string')\n",
    "print(df.year.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5564b1ae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:18.713958Z",
     "iopub.status.busy": "2022-01-21T22:23:18.713170Z",
     "iopub.status.idle": "2022-01-21T22:23:24.503417Z",
     "shell.execute_reply": "2022-01-21T22:23:24.502487Z",
     "shell.execute_reply.started": "2022-01-21T20:15:03.544877Z"
    },
    "id": "5564b1ae",
    "outputId": "16053207-2904-4659-bcff-7032e27fb94f",
    "papermill": {
     "duration": 5.8411,
     "end_time": "2022-01-21T22:23:24.503602",
     "exception": false,
     "start_time": "2022-01-21T22:23:18.662502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mAPv8M/Bbj74RNKy7GjgV+OjUfpuT3JbkFuBFwOVT225h72X554F3VNX/HMXMWkP8RJAEDO/er6+q1xzGvm9n6o0kHVt8jUXHvCR/CbwEeOnYs2j+eaYpSQ2+pilJDUZTkhqMpiQ1GE1JajCaktRgNCWp4f8AtC003560x6wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LA9/Nnwju5mtP3M3XeEy53XxJ2tcYppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YpprR9ttvv+dNpfFyDdKM9uyzzz5vKo2XYSpJDRimmtHczVcrrkGasebMmcOsWb0n4MyaNYs5c+YMuCJNZ4O6a5Q0cNu2bdvpvDQebplqRjrooIOAF+7m72iXXirDVDPS1q1bmT179vPO5s+ePZutW7cOuDJNV+7ma0bavn070DtW+swzzzBr1qzn2qTxMEw1Yx144IHcdNNNz4XpW9/6Vp588slBl6VpyjDVjPXUU0/x5je/+bkw9Yv72hseM9WMNfbxJD6uRHvDMNWMlYQrr7ySW265hSuvvHK3z4WS9sTdfM1YBx98MEuXLmXbtm3MmTOHgw8+mCeeeGLQZWmacstUM9LcuXOZP3/+c2fwt2/fzvz585k7d+6AK9N0ZZhqRjr22GO5//77Ofvss7nhhhs4++yzuf/++zn22GMHXZqmqeyLB92Hh4dr7dq1gy5DU9gBBxzAUUcdxfr166kqkrBgwQIefPBBnnrqqUGXpykqyZ1VNbyzZW6ZakbaunUrjzzyCLNn904bzJ49m0ceecQroDRunoDSjLVlyxauuuoqFi1axH333cfFF1886JI0jbllqhlr7Feh/GqU9oZbppqxzjrrLD72sY+xdetW5s6dy1lnncVXv/rVQZelacow1Yw0e/ZsvvnNb3LLLbc8dznpeeed99wxVOmlcs3RjPS+972Pa665hgsuuIDHHnuMoaEhHn/8cd7//vcPujRNUx4z1Yx09dVXc9ppp7Fp0yYANm3axGmnncbVV1894Mo0XRmmmpFWrlzJ+vXrWbNmDatXr2bNmjWsX7+elStXDro0TVMDCdMkFyW5J8m9ST7UtZ2Q5DtJ7kqyNslJXXuSfDbJA0nuTnLiIGrWvuWyyy7j2muv5ZRTTmH27NmccsopXHvttVx22WWDLk3T1KSHaZLjgPcCJwHHA29L8lrgSuDjVXUC8Mfda4AzgQXdzxLgc5Nds/Y969at4+STT35e28knn8y6desGVJGmu0FsmS4E/qaqflFV24G/Bn4bKOAVXZ9XAo908+cAX6ye7wCvSjJ/sovWvmXhwoXcfvvtz2u7/fbbWbhw4YAq0nQ3iLP59wCXJXk18CRwFrAW+BCwKslV9EL+17v+hwMP9b1/Q9e2sX/QJEvobbkyNDTE6OjoBP4TNN29/e1v513vehcf+chHOOaYY/jMZz7Dpz71KS688ELXHY3LpIdpVa1LcgXwNWALcBfwDPD7wIer6stJzgeuBU57CeMuA5ZB70YnIyMjrUvXPmRkZIRFixZx2WWXsW7dOhYuXMinP/1pLrjggkGXpmlq4HeNSvIn9LY2Pwm8qqoqvev6Hq+qVyT5r8BoVa3s+v8AGKmqjbsa07tG6aUYHR3FP756MabcXaOSHNZNX0PveOlf0DtG+ptdl38FrO/mbwTe3Z3V/zV6IbvLIJWkQRjUFVBf7o6ZbgM+UFU/TfJe4L8kmQ08RXf8E7iZ3nHVB4BfAL87iIIlaXcGEqZV9aadtN0O/IudtBfwgcmoS5LGyyugJKkBw1SSGjBMJakBw1SSGjBMJamBgX9pfyIk+XvgwUHXoWljHvAPgy5C08JRVfUrO1uwT4ap9FIkWburq1qkF8vdfElqwDCVpAYMU6m725i0NzxmKkkNuGUqSQ0YppoSkjzTPUzxniT/I8nLBl3TziTZP8kXknw/yfeSjAy6Jk0Nhqmmiier6oSqOg54Gnhf/8Lu1oyTYg+f9V6Aqnod8Gbg00n8PZJhqinpG8Brk4wk+UaSG4H7ksxK8qkkd3SP/f53AEnmJ7mtb8v2TV3f67rX30/y4a7vaJLhbn5ekh938/82yY1Jvg6sSXJQkuVJvpvkb5Oc09W2CPg6QFVtAn4K7Bjvc91jyu9N8vEd/5gkP07yyb7HmJ+YZFWS/5vkeX80NH0N6ubQ0k51W4VnArd2TScCx1XVj7qHJj5eVW9IMhf4ZpKv0Xtaw6qquizJLOBlwAnA4d2WLkle9SI+/kTg9VW1uXuczter6j3de7+b5H8D3wN+K8lK4Eh69+A9Evgu8B+6986iF8ivr6q7u7H/X1WdkOQzwHXAvwQOoPeAyT8f/38xTRWGqaaKA5Pc1c1/g94DFX8d+G5V/ahrPx14fZJ3dK9fCSwA7gCWJ5kD/FVV3ZXkh8CvJrkauIneAxz3ZHVVbe77rN9KcnH3+gDgNcByeo8rX0vvkuVv0XsgJMD5XeDPBubT24rdEaY3dtPvAwdX1RPAE0m2JnlVVf30RdSnKcww1VTxZFWd0N/Qe64iW/qbgA9W1aqxb07yG8BbgeuS/Oeq+mKS44G30Dv+ej7wHmA7vzy8dcCYYcZ+1nlV9YOd1Prhvs/9FnB/kmOAi4E3VNVPklw3Zvyt3fTZvvkdr/093Ad4zFTTySrg97stUJIc2x3bPAp4rKr+G/B54MQk84D9qurLwB/R24UH+DG/fDzOO9i1VcAHuyflkuSfd9OXJTmom38zsL2q7gNeQS+MH08yRO9QhWYQ/yJqOvk8cDTwf7qQ+3vgXGAE+EiSbcDPgXcDhwNf6DvT/tFuehVwfbc7ftNuPutS4E+Bu7sxfgS8DTgMWJXkWeBh4HcAqup7Sf4W+DvgIeCbLf7Bmj68AkqSGnA3X5IaMEwlqQHDVJIaMEwlqQHDVJIaMEwlqQHDVJIaMEwlqYH/D/q2+g/2UEaiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HwJ2n7G8J8KvAq5o5/re536PAyr79W+uC52uO6trFTAbpoYgAmAPsOWX9/c3lV4EdmbkHICK+xWTsDgC7MvNfmvv9HfDbmfnBiHgbcG9EXAx8nsnXNwEWAJsi4loggXmn7G8sM58CnoqIHwCfOmX/P9+nf2cNAOOorgWT0XvNFOt/1FyeOOX6ydsnn7/PfOE8ATLzUeB1ABHxBuCnm/V/ymQEfyUihoFesb9n7vPU/ek5wNNqde1HwBUR8RqAiJgXET97jttYfPLxwG8AX2i2dWVzeTHwXiZPzWHyyHF3c/0d0x9ds5lxVNdOMPl64F0R8WXgS8BN57iNbwDviohx4EXAXzfLf69Z9hXgU5n5SLP8buDPIuIxPBrUFHwrjwZac1r86cxc0vEommU8cpSkgkeOklTwyFGSCsZRkgrGUZIKxlGSCsZRkgrGUZIK/wffkKEPnwsUHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# checking for outliers in numerical data type columns\n",
    "# first we will analyze outliers using box plot\n",
    "for i in integer:\n",
    "    plt.figure(figsize=(5,5))\n",
    "    fig = df.boxplot(column=i)\n",
    "    fig.set_ylabel('i')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e56728e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:24.620702Z",
     "iopub.status.busy": "2022-01-21T22:23:24.616484Z",
     "iopub.status.idle": "2022-01-21T22:23:24.675157Z",
     "shell.execute_reply": "2022-01-21T22:23:24.674626Z",
     "shell.execute_reply.started": "2022-01-21T22:17:13.378013Z"
    },
    "id": "e56728e0",
    "papermill": {
     "duration": 0.116249,
     "end_time": "2022-01-21T22:23:24.675304",
     "exception": false,
     "start_time": "2022-01-21T22:23:24.559055",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# now we will use IQR method to remove outliers from the required columns\n",
    "from scipy import stats\n",
    "q1 = df.MinTemp.quantile(0.25)\n",
    "q3 = df.MinTemp.quantile(0.75)\n",
    "iqr = q3-q1\n",
    "lowerbound = q1 - 1.5 * iqr\n",
    "upperbound = q3 + 1.5 * iqr\n",
    "df.drop(df[ (df.MinTemp > upperbound) | (df.MinTemp < lowerbound) ].index , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af8185ea",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:24.788202Z",
     "iopub.status.busy": "2022-01-21T22:23:24.787504Z",
     "iopub.status.idle": "2022-01-21T22:23:25.208488Z",
     "shell.execute_reply": "2022-01-21T22:23:25.207795Z",
     "shell.execute_reply.started": "2022-01-21T22:15:12.142491Z"
    },
    "id": "af8185ea",
    "outputId": "86c51f88-e0e7-48e5-c107-c7ad006111ef",
    "papermill": {
     "duration": 0.478593,
     "end_time": "2022-01-21T22:23:25.208645",
     "exception": false,
     "start_time": "2022-01-21T22:23:24.730052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MinTemp')"
      ]
     },
     "execution_count": 20,
     "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": [
    "# checking boxplot for MinTemp\n",
    "plt.figure(figsize=(5,5))\n",
    "fig = df.boxplot(column='MinTemp')\n",
    "fig.set_ylabel('MinTemp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2025874",
   "metadata": {
    "id": "d2025874",
    "papermill": {
     "duration": 0.055627,
     "end_time": "2022-01-21T22:23:25.320530",
     "exception": false,
     "start_time": "2022-01-21T22:23:25.264903",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see that now outliers are removed from this MinTemp, similary like this we will remove the outliers from all the other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24a5a615",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:25.441433Z",
     "iopub.status.busy": "2022-01-21T22:23:25.440727Z",
     "iopub.status.idle": "2022-01-21T22:23:25.895000Z",
     "shell.execute_reply": "2022-01-21T22:23:25.894288Z",
     "shell.execute_reply.started": "2022-01-21T22:17:17.156191Z"
    },
    "id": "24a5a615",
    "papermill": {
     "duration": 0.518017,
     "end_time": "2022-01-21T22:23:25.895144",
     "exception": false,
     "start_time": "2022-01-21T22:23:25.377127",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# removing outliers from the remaining columns\n",
    "l1 = ['MaxTemp', 'Rainfall', 'Evaporation', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Pressure9am', 'Pressure3pm', 'Temp9am', 'Temp3pm']\n",
    "for i in l1:\n",
    "    q1 = df[i].quantile(0.25)\n",
    "    q3 = df[i].quantile(0.75)\n",
    "    iqr = q3-q1\n",
    "    lowerbound = q1 - 1.5 * iqr\n",
    "    upperbound = q3 + 1.5 * iqr\n",
    "    df.drop(df[ (df[i] > upperbound) | (df[i] < lowerbound) ].index , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "61881c6e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:26.029666Z",
     "iopub.status.busy": "2022-01-21T22:23:26.014422Z",
     "iopub.status.idle": "2022-01-21T22:23:26.072953Z",
     "shell.execute_reply": "2022-01-21T22:23:26.072356Z",
     "shell.execute_reply.started": "2022-01-21T22:17:19.476938Z"
    },
    "id": "61881c6e",
    "outputId": "366686a0-d411-4bea-a469-09e8b93c6a85",
    "papermill": {
     "duration": 0.119819,
     "end_time": "2022-01-21T22:23:26.073098",
     "exception": false,
     "start_time": "2022-01-21T22:23:25.953279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "# converting them again in integer\n",
    "df[['year', 'month','day']] = df[['year', 'month','day']].astype('int')\n",
    "print(df.year.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a628409a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:26.192704Z",
     "iopub.status.busy": "2022-01-21T22:23:26.191993Z",
     "iopub.status.idle": "2022-01-21T22:23:26.194694Z",
     "shell.execute_reply": "2022-01-21T22:23:26.195190Z",
     "shell.execute_reply.started": "2022-01-21T22:17:22.176035Z"
    },
    "id": "a628409a",
    "outputId": "35c15ef5-0f80-416c-8622-6249cc9f7d9e",
    "papermill": {
     "duration": 0.065794,
     "end_time": "2022-01-21T22:23:26.195360",
     "exception": false,
     "start_time": "2022-01-21T22:23:26.129566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm', 'year', 'month', 'day']\n"
     ]
    }
   ],
   "source": [
    "# new columns with data type integer\n",
    "integer1 = [i for i in df.columns if df[i].dtype !='O']\n",
    "print(integer1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7763db55",
   "metadata": {
    "id": "7763db55",
    "papermill": {
     "duration": 0.056309,
     "end_time": "2022-01-21T22:23:26.309252",
     "exception": false,
     "start_time": "2022-01-21T22:23:26.252943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Splitting the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de177cd4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:26.426508Z",
     "iopub.status.busy": "2022-01-21T22:23:26.425830Z",
     "iopub.status.idle": "2022-01-21T22:23:26.656913Z",
     "shell.execute_reply": "2022-01-21T22:23:26.656341Z",
     "shell.execute_reply.started": "2022-01-21T22:17:24.835120Z"
    },
    "id": "de177cd4",
    "outputId": "6fb94cdd-71ae-421c-c8be-c9627782ecc1",
    "papermill": {
     "duration": 0.290739,
     "end_time": "2022-01-21T22:23:26.657064",
     "exception": false,
     "start_time": "2022-01-21T22:23:26.366325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((85016, 24), (21255, 24))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(['RainTomorrow'], axis=1)\n",
    "y = df['RainTomorrow']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d83611",
   "metadata": {
    "id": "b7d83611",
    "papermill": {
     "duration": 0.057979,
     "end_time": "2022-01-21T22:23:26.773096",
     "exception": false,
     "start_time": "2022-01-21T22:23:26.715117",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Checking NAN values in the integer data types and replacing them with the Median values of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "398e2397",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:26.891789Z",
     "iopub.status.busy": "2022-01-21T22:23:26.891019Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# replacing integer NAN values with Median \n",
    "for df in [X_train, X_test]:\n",
    "    for col in integer1:\n",
    "        col_median=X_train[col].median()\n",
    "        df[col].fillna(col_median, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ac95d37",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:27.077712Z",
     "iopub.status.busy": "2022-01-21T22:23:27.076971Z",
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     "shell.execute_reply.started": "2022-01-21T22:17:34.999762Z"
    },
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    "outputId": "13719968-f03c-4496-9313-77fd8dc84714",
    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "Evaporation      0\n",
       "Sunshine         0\n",
       "WindGustSpeed    0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Cloud9am         0\n",
       "Cloud3pm         0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "year             0\n",
       "month            0\n",
       "day              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking values in X_train\n",
    "X_train[integer1].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ecdcd84d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:27.210672Z",
     "iopub.status.busy": "2022-01-21T22:23:27.209966Z",
     "iopub.status.idle": "2022-01-21T22:23:27.219476Z",
     "shell.execute_reply": "2022-01-21T22:23:27.219975Z",
     "shell.execute_reply.started": "2022-01-21T22:17:38.552017Z"
    },
    "id": "ecdcd84d",
    "outputId": "7bda307d-5206-4f7e-ada4-d963ad6c4205",
    "papermill": {
     "duration": 0.070767,
     "end_time": "2022-01-21T22:23:27.220164",
     "exception": false,
     "start_time": "2022-01-21T22:23:27.149397",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinTemp          0\n",
       "MaxTemp          0\n",
       "Rainfall         0\n",
       "Evaporation      0\n",
       "Sunshine         0\n",
       "WindGustSpeed    0\n",
       "WindSpeed9am     0\n",
       "WindSpeed3pm     0\n",
       "Humidity9am      0\n",
       "Humidity3pm      0\n",
       "Pressure9am      0\n",
       "Pressure3pm      0\n",
       "Cloud9am         0\n",
       "Cloud3pm         0\n",
       "Temp9am          0\n",
       "Temp3pm          0\n",
       "year             0\n",
       "month            0\n",
       "day              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking values in X_test\n",
    "X_test[integer1].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bce30c50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:27.340260Z",
     "iopub.status.busy": "2022-01-21T22:23:27.339596Z",
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     "shell.execute_reply": "2022-01-21T22:23:27.496605Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# replcaing NAN values with the mode in categorical data types\n",
    "for df1 in [X_train, X_test]:\n",
    "    df1['WindGustDir'].fillna(X_train['WindGustDir'].mode()[0], inplace=True)\n",
    "    df1['WindDir9am'].fillna(X_train['WindDir9am'].mode()[0], inplace=True)\n",
    "    df1['WindDir3pm'].fillna(X_train['WindDir3pm'].mode()[0], inplace=True)\n",
    "    df1['RainToday'].fillna(X_train['RainToday'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7c6fdcb4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:27.616570Z",
     "iopub.status.busy": "2022-01-21T22:23:27.615872Z",
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     "shell.execute_reply.started": "2022-01-21T22:17:46.186704Z"
    },
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindGustDir    0\n",
       "WindDir9am     0\n",
       "WindDir3pm     0\n",
       "RainToday      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical1 = [i for i in X_train.columns if X_train[i].dtype == 'O']\n",
    "# As Location column don't have any NAN values\n",
    "categorical1.remove('Location')\n",
    "# checking NAN values in X_train\n",
    "X_train[categorical1].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "96b70e08",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:27.786073Z",
     "iopub.status.busy": "2022-01-21T22:23:27.785374Z",
     "iopub.status.idle": "2022-01-21T22:23:27.803497Z",
     "shell.execute_reply": "2022-01-21T22:23:27.803999Z",
     "shell.execute_reply.started": "2022-01-21T22:17:49.822025Z"
    },
    "id": "96b70e08",
    "outputId": "cae988ce-de67-41d4-a0d5-c5448efe0cb0",
    "papermill": {
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     "end_time": "2022-01-21T22:23:27.804187",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindGustDir    0\n",
       "WindDir9am     0\n",
       "WindDir3pm     0\n",
       "RainToday      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking NAN values in X_test\n",
    "X_test[categorical1].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "71cc6aca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:27.926668Z",
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     "shell.execute_reply.started": "2022-01-21T22:17:54.987693Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0]\n",
      "Index(['Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine',\n",
      "       'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',\n",
      "       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',\n",
      "       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',\n",
      "       'Temp3pm', 'RainToday', 'year', 'month', 'day'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Encoding Rain Today column\n",
    "from sklearn import preprocessing\n",
    " \n",
    "# label_encoder object knows how to understand word labels.\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    " \n",
    "# Encode labels in column 'species'.\n",
    "X_train['RainToday'] = label_encoder.fit_transform(X_train['RainToday'])\n",
    "\n",
    "X_test['RainToday'] = label_encoder.transform(X_test['RainToday'])\n",
    " \n",
    "print(X_train['RainToday'].unique())\n",
    "print(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "395caadb",
   "metadata": {
    "colab": {
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    "execution": {
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    {
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       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
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       "      <th>Evaporation</th>\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\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",
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       "        }\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
       "22781      21.1     25.0       1.2          5.2       4.8           33.0   \n",
       "20511      17.3     25.4       0.2          5.0       9.2           22.0   \n",
       "139813     19.2     36.0       0.0          8.0       9.2           33.0   \n",
       "17454      13.8     27.5       0.0          5.0       9.2           37.0   \n",
       "46383      -0.5     21.8       0.0          5.0       9.2           22.0   \n",
       "\n",
       "        WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...  NNW  NW  S  \\\n",
       "22781           17.0          22.0         89.0         84.0  ...    0   0  1   \n",
       "20511            7.0          19.0         74.0         58.0  ...    0   0  0   \n",
       "139813           6.0          19.0         50.0         23.0  ...    0   0  0   \n",
       "17454            0.0          17.0         72.0         49.0  ...    0   0  0   \n",
       "46383            6.0          11.0         72.0         33.0  ...    1   0  0   \n",
       "\n",
       "        SE  SSE  SSW  SW  W  WNW  WSW  \n",
       "22781    0    0    0   0  0    0    0  \n",
       "20511    0    1    0   0  0    0    0  \n",
       "139813   0    0    1   0  0    0    0  \n",
       "17454    1    0    0   0  0    0    0  \n",
       "46383    0    0    0   0  0    0    0  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode categorical variables\n",
    "X_train = pd.concat([X_train[integer1], X_train['RainToday'],\n",
    "                     pd.get_dummies(X_train.Location), \n",
    "                     pd.get_dummies(X_train.WindGustDir),\n",
    "                     pd.get_dummies(X_train.WindDir9am),\n",
    "                     pd.get_dummies(X_train.WindDir3pm)], axis=1)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0a07b2e1",
   "metadata": {
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     "height": 233
    },
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
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       "      <th>62691</th>\n",
       "      <td>2.6</td>\n",
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       "      <th>87872</th>\n",
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       "      <th>96717</th>\n",
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       "      <td>66.0</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>9.2</td>\n",
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       "      <td>90.0</td>\n",
       "      <td>67.0</td>\n",
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       "      <th>37794</th>\n",
       "      <td>2.1</td>\n",
       "      <td>14.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>81.0</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "       MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
       "62691      2.6     17.8       0.0          5.0       9.2           37.0   \n",
       "87872     20.1     30.3       0.0          5.0       9.2           24.0   \n",
       "96717     11.1     25.1       0.0          5.0       9.2           26.0   \n",
       "40766     13.4     20.0       0.0          5.0       9.2           31.0   \n",
       "37794      2.1     14.1       0.0          1.4       5.9           28.0   \n",
       "\n",
       "       WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  ...  NNW  NW  S  \\\n",
       "62691           6.0           9.0         99.0         54.0  ...    0   0  0   \n",
       "87872          11.0          19.0         65.0         72.0  ...    0   0  0   \n",
       "96717          11.0           7.0         66.0         27.0  ...    0   0  0   \n",
       "40766          13.0          15.0         90.0         67.0  ...    0   0  0   \n",
       "37794          11.0          11.0         81.0         57.0  ...    0   0  0   \n",
       "\n",
       "       SE  SSE  SSW  SW  W  WNW  WSW  \n",
       "62691   0    0    0   0  0    0    0  \n",
       "87872   0    0    0   0  0    0    0  \n",
       "96717   0    0    0   0  0    1    0  \n",
       "40766   0    0    0   0  0    0    0  \n",
       "37794   0    0    0   0  0    0    1  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode categorical variables\n",
    "X_test = pd.concat([X_test[integer1], X_test['RainToday'],\n",
    "                     pd.get_dummies(X_test.Location), \n",
    "                     pd.get_dummies(X_test.WindGustDir),\n",
    "                     pd.get_dummies(X_test.WindDir9am),\n",
    "                     pd.get_dummies(X_test.WindDir3pm)], axis=1)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d75dec",
   "metadata": {
    "id": "16d75dec",
    "papermill": {
     "duration": 0.059678,
     "end_time": "2022-01-21T22:23:28.480631",
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     "start_time": "2022-01-21T22:23:28.420953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Data Normalization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9ae9dc7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:28.612253Z",
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     "shell.execute_reply": "2022-01-21T22:23:28.795639Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# data Normalization\n",
    "cols = X_train.columns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)\n",
    "X_train = pd.DataFrame(X_train, columns=[cols])\n",
    "X_test = pd.DataFrame(X_test, columns=[cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f4d9f50",
   "metadata": {
    "id": "9f4d9f50",
    "papermill": {
     "duration": 0.05974,
     "end_time": "2022-01-21T22:23:28.916142",
     "exception": false,
     "start_time": "2022-01-21T22:23:28.856402",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Model Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "58a4d8a9",
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    "execution": {
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     "exception": false,
     "start_time": "2022-01-21T22:23:28.978927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fit_and_evaluate(model):\n",
    "    # Train the model\n",
    "    model.fit(X_train, y_train)\n",
    "    score = model.score(X_test,y_test)\n",
    "    \n",
    "    # Return the performance metric\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6e4409",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Checking Logistic Regression Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab856823",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:29.298622Z",
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    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Performance on the test set: score = 0.8711\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(solver='liblinear', random_state=0)\n",
    "log_score = fit_and_evaluate(logreg)\n",
    "print('Logistic Regression Performance on the test set: score = %0.4f' % log_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2bf2fa1",
   "metadata": {
    "id": "d2bf2fa1",
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     "start_time": "2022-01-21T22:23:31.092799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Checking Naive Bayes Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eb743944",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:31.280330Z",
     "iopub.status.busy": "2022-01-21T22:23:31.279207Z",
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     "shell.execute_reply.started": "2022-01-21T22:18:55.251309Z"
    },
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    "outputId": "7c5959dd-674f-4585-9d1b-c40ddebb0e20",
    "papermill": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Performance on the test set: score = 0.6317\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "naive = GaussianNB()\n",
    "naive_score = fit_and_evaluate(naive)\n",
    "print('Naive Bayes Performance on the test set: score = %0.4f' % naive_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ea7b0e",
   "metadata": {
    "id": "98ea7b0e",
    "papermill": {
     "duration": 0.062763,
     "end_time": "2022-01-21T22:23:31.959578",
     "exception": false,
     "start_time": "2022-01-21T22:23:31.896815",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Checking Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b88aae41",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:32.087298Z",
     "iopub.status.busy": "2022-01-21T22:23:32.086613Z",
     "iopub.status.idle": "2022-01-21T22:23:35.025526Z",
     "shell.execute_reply": "2022-01-21T22:23:35.024890Z",
     "shell.execute_reply.started": "2022-01-21T22:18:58.594890Z"
    },
    "id": "b88aae41",
    "outputId": "664a6dc1-d6e7-4cc3-80c0-e4f919de62ac",
    "papermill": {
     "duration": 3.004777,
     "end_time": "2022-01-21T22:23:35.025748",
     "exception": false,
     "start_time": "2022-01-21T22:23:32.020971",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Performance on the test set: Score = 0.8673\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random = RandomForestClassifier(n_estimators = 10, criterion = 'entropy')\n",
    "random_score = fit_and_evaluate(random)\n",
    "\n",
    "print('Random Forest Performance on the test set: Score = %0.4f' % random_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1db8fc",
   "metadata": {
    "id": "3c1db8fc",
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     "duration": 0.062936,
     "end_time": "2022-01-21T22:23:35.163712",
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     "start_time": "2022-01-21T22:23:35.100776",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Since Logistic Regression gives the best accuracy score so we will calculate confusion matrix, sensitivity,specificity and auc_score on the Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d3cbc27e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:35.290062Z",
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     "shell.execute_reply": "2022-01-21T22:23:35.544049Z",
     "shell.execute_reply.started": "2022-01-21T22:19:08.853048Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17466   531]\n",
      " [ 2209  1049]]\n"
     ]
    }
   ],
   "source": [
    "# calculating confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_head_logreg = logreg.predict(X_test)\n",
    "cm_logreg = confusion_matrix(y_test,y_head_logreg)\n",
    "print(cm_logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4f504edf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:35.671807Z",
     "iopub.status.busy": "2022-01-21T22:23:35.670720Z",
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     "shell.execute_reply": "2022-01-21T22:23:35.678408Z",
     "shell.execute_reply.started": "2022-01-21T22:19:13.794948Z"
    },
    "id": "4f504edf",
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     "exception": false,
     "start_time": "2022-01-21T22:23:35.606655",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sensitivity: 0.8877255400254129\n",
      "specificity: 0.6639240506329114\n"
     ]
    }
   ],
   "source": [
    "# calculating sensitivity and specificity\n",
    "sensitivity = (cm_logreg[0,0]/(cm_logreg[0,0] + cm_logreg[1,0]))\n",
    "print(\"sensitivity:\",sensitivity)\n",
    "specificity = (cm_logreg[1,1]/(cm_logreg[1,1]+cm_logreg[0,1]))\n",
    "print(\"specificity:\",specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f94ca8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:35.807416Z",
     "iopub.status.busy": "2022-01-21T22:23:35.806397Z",
     "iopub.status.idle": "2022-01-21T22:23:35.810849Z",
     "shell.execute_reply": "2022-01-21T22:23:35.811333Z",
     "shell.execute_reply.started": "2022-01-21T22:19:17.405676Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "TP = cm_logreg[0,0]\n",
    "TN = cm_logreg[1,1]\n",
    "FP = cm_logreg[0,1]\n",
    "FN = cm_logreg[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2feccf01",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:35.938697Z",
     "iopub.status.busy": "2022-01-21T22:23:35.937674Z",
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     "shell.execute_reply.started": "2022-01-21T22:19:20.329792Z"
    },
    "id": "2feccf01",
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    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision : 0.9705\n"
     ]
    }
   ],
   "source": [
    "# print precision score\n",
    "\n",
    "precision = TP / float(TP + FP)\n",
    "\n",
    "\n",
    "print('Precision : {0:0.4f}'.format(precision))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d5249fe1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:36.073686Z",
     "iopub.status.busy": "2022-01-21T22:23:36.072995Z",
     "iopub.status.idle": "2022-01-21T22:23:36.077582Z",
     "shell.execute_reply": "2022-01-21T22:23:36.078031Z",
     "shell.execute_reply.started": "2022-01-21T22:19:25.851400Z"
    },
    "id": "d5249fe1",
    "outputId": "7625bf2a-bf2b-4dd4-bc62-4034bf50d951",
    "papermill": {
     "duration": 0.071802,
     "end_time": "2022-01-21T22:23:36.078204",
     "exception": false,
     "start_time": "2022-01-21T22:23:36.006402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification error : 0.1289\n"
     ]
    }
   ],
   "source": [
    "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
    "\n",
    "print('Classification error : {0:0.4f}'.format(classification_error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2cc9045c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-21T22:23:36.206090Z",
     "iopub.status.busy": "2022-01-21T22:23:36.205439Z",
     "iopub.status.idle": "2022-01-21T22:23:36.415001Z",
     "shell.execute_reply": "2022-01-21T22:23:36.415962Z",
     "shell.execute_reply.started": "2022-01-21T22:19:29.058822Z"
    },
    "id": "2cc9045c",
    "outputId": "6c6c4313-f6d4-4638-c738-614b549db52a",
    "papermill": {
     "duration": 0.275511,
     "end_time": "2022-01-21T22:23:36.416275",
     "exception": false,
     "start_time": "2022-01-21T22:23:36.140764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8389364430256143\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "y_pred1 = logreg.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred1, pos_label = 'Yes')\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "ROC_AUC = roc_auc_score(y_test, y_pred1)\n",
    "print(ROC_AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5984818",
   "metadata": {
    "id": "d5984818",
    "papermill": {
     "duration": 0.061942,
     "end_time": "2022-01-21T22:23:36.542177",
     "exception": false,
     "start_time": "2022-01-21T22:23:36.480235",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Permutation importance is the first tool for understanding a machine-learning model, and it involves shuffling individual variables in the validation data (after a model has been fit) and seeing the effect on accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eNdgrADlMsqz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eNdgrADlMsqz",
    "outputId": "d2446059-8b99-4043-e661-c5eb8e65b7ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting eli5\n",
      "  Downloading eli5-0.11.0-py2.py3-none-any.whl (106 kB)\n",
      "\u001b[K     |████████████████████████████████| 106 kB 5.4 MB/s \n",
      "\u001b[?25hRequirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from eli5) (2.11.3)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from eli5) (1.4.1)\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from eli5) (0.10.1)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from eli5) (1.21.5)\n",
      "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from eli5) (0.8.9)\n",
      "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/dist-packages (from eli5) (1.0.2)\n",
      "Requirement already satisfied: attrs>16.0.0 in /usr/local/lib/python3.7/dist-packages (from eli5) (21.4.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from eli5) (1.15.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->eli5) (1.1.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->eli5) (3.1.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->eli5) (2.0.1)\n",
      "Installing collected packages: eli5\n",
      "Successfully installed eli5-0.11.0\n"
     ]
    }
   ],
   "source": [
    "pip install eli5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "de82a9e1",
   "metadata": {
    "colab": {
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     "height": 391
    },
    "execution": {
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     "shell.execute_reply": "2022-01-21T22:25:13.075616Z",
     "shell.execute_reply.started": "2022-01-21T22:19:35.589423Z"
    },
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    "papermill": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
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       "                    &plusmn; 0.0055\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Pressure9am\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0116\n",
       "                \n",
       "                    &plusmn; 0.0018\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WindGustSpeed\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.03%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0023\n",
       "                \n",
       "                    &plusmn; 0.0018\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WindSpeed3pm\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.50%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0016\n",
       "                \n",
       "                    &plusmn; 0.0014\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Sunshine\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.52%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0016\n",
       "                \n",
       "                    &plusmn; 0.0010\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MaxTemp\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.74%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0012\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Wollongong\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.91%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0010\n",
       "                \n",
       "                    &plusmn; 0.0014\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Cloud3pm\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.03%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0009\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                month\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.08%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0008\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                N\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.13%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MinTemp\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.13%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MountGinini\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.16%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                N\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.18%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0006\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                E\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.23%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                MelbourneAirport\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.23%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Hobart\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.24%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                WindSpeed9am\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.28%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                SE\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.28%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Temp3pm\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 99.28%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 97 more &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weightage to the column in Logistic Regression Algorithm\n",
    "import eli5 #for permutation importance\n",
    "from eli5.sklearn import PermutationImportance\n",
    "l2=[]\n",
    "l1 = X_train.columns.tolist()\n",
    "for i in range(len(l1)):\n",
    "    l2.append(l1[i][0])\n",
    "    \n",
    "perm = PermutationImportance(logreg, random_state=1).fit(X_test, y_test)\n",
    "eli5.show_weights(perm, feature_names = l2)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "rain-in-australia-prediction-via-lrc-nbc-rfc.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 132.904965,
   "end_time": "2022-01-21T22:25:16.019678",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-01-21T22:23:03.114713",
   "version": "2.3.3"
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 },
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