mean-air-temperature-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "common-switzerland",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "yellow-albert",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('mean-air-temperature.csv') #load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "correct-competition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                            datetime64[ns]\n",
       "total_precipitation                    float64\n",
       "mean_wind_speed                        float64\n",
       "mean_relative_humidity                 float64\n",
       "percentage_possible_sunshine           float64\n",
       "total_sunshine_duration                float64\n",
       "solar_radiation                        float64\n",
       "mean_air_temperature                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to explicitly convert the date column to type DATETIME\n",
    "data['date'] = pd.to_datetime(data['date'], dayfirst=True)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "placed-market",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index('date') #set the index of the dataset as the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "right-charge",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1597: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_air_temperature</th>\n",
       "      <th>last_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>18.3</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>19.9</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>21.2</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>23.8</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>26.7</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>24.5</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>25.5</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>24.3</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>25.9</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>25.7</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean_air_temperature  last_month\n",
       "date                                        \n",
       "2010-02-01                  18.3        16.8\n",
       "2010-03-01                  19.9        18.3\n",
       "2010-04-01                  21.2        19.9\n",
       "2010-05-01                  23.8        21.2\n",
       "2010-06-01                  26.7        23.8\n",
       "...                          ...         ...\n",
       "2021-07-01                  24.5        21.9\n",
       "2021-08-01                  25.5        24.5\n",
       "2021-09-01                  24.3        25.5\n",
       "2021-10-01                  25.9        24.3\n",
       "2021-11-01                  25.7        25.9\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean_air_temperature = data[['mean_air_temperature']] # creating new dataframe from total_sunshine_duration column\n",
    "data_mean_air_temperature.loc[:,'last_month'] = data_mean_air_temperature.loc[:,'mean_air_temperature'].shift() # inserting new column with last month's mean_air_temperature values\n",
    "data_mean_air_temperature = data_mean_air_temperature.dropna() # dropping NAs\n",
    "data_mean_air_temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "stopped-prompt",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(actual, predict):\n",
    "    predict = np.array(predict)\n",
    "    actual = np.array(actual)\n",
    "    distance = predict - actual\n",
    "    square_distance = distance ** 2\n",
    "    mean_square_distance = square_distance.mean()\n",
    "    score = np.sqrt(mean_square_distance)\n",
    "    return score\n",
    "rmse_score = make_scorer(rmse, greater_is_better = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "swedish-acrobat",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_mean_air_temperature.drop(['mean_air_temperature'], axis = 1)\n",
    "y_train = data_mean_air_temperature.loc[:'2021', 'mean_air_temperature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bacterial-syndrome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2010-02-01    18.3\n",
       "2010-03-01    19.9\n",
       "2010-04-01    21.2\n",
       "2010-05-01    23.8\n",
       "2010-06-01    26.7\n",
       "              ... \n",
       "2021-07-01    24.5\n",
       "2021-08-01    25.5\n",
       "2021-09-01    24.3\n",
       "2021-10-01    25.9\n",
       "2021-11-01    25.7\n",
       "Name: mean_air_temperature, Length: 142, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "specialized-deviation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25.00474537]\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('predicted-mean-air-temperature.csv')\n",
    "test_data = test_data.set_index('date')\n",
    "X_test = test_data.drop(['mean_air_temperature'], axis = 1)\n",
    "model = RandomForestRegressor()\n",
    "param_search = { \n",
    "    'n_estimators': [20, 50, 100],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [i for i in range(5,15)]\n",
    "}\n",
    "tscv = TimeSeriesSplit(n_splits=10)\n",
    "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_search, scoring = rmse_score)\n",
    "gsearch.fit(X_train, y_train)\n",
    "best_model = gsearch.best_estimator_\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "greater-optics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>last_month</th>\n",
       "      <th>mean_air_temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date  last_month  mean_air_temperature\n",
       "0  Jan-22        25.0                  27.4\n",
       "1  Feb-22         NaN                  25.3\n",
       "2  Mar-22         NaN                  23.2\n",
       "3  Apr-22         NaN                  21.1\n",
       "4  May-22         NaN                  20.8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_data = pd.read_csv('predicted-mean-air-temperature.csv')\n",
    "predicted_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "invalid-discovery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create an empty figure\n",
    "plt.bar(predicted_data['date'], predicted_data['mean_air_temperature']) #set the dates as the x-axis and the mean_air_temperature as the y-axis\n",
    "plt.ylabel(\"Mean air temperature\") # set the label for the y-axis\n",
    "plt.xlabel(\"Dates\") # set the label for the x-axis\n",
    "fig.set_size_inches(20, 5) #set the size of the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "guided-adams",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}