model_training_simple_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "567c3dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no hyperparameter tuning because less computation power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29ad061f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "638ac7b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./datasets/preprocessed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fa65221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>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>Pressure9am</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>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>8.694688</td>\n",
       "      <td>13.589273</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.263869</td>\n",
       "      <td>57.554920</td>\n",
       "      <td>0</td>\n",
       "      <td>8.870193</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>318.255596</td>\n",
       "      <td>1.375390</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.549671</td>\n",
       "      <td>13.043088</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>5.301024</td>\n",
       "      <td>14.668806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.406140</td>\n",
       "      <td>69.201128</td>\n",
       "      <td>1</td>\n",
       "      <td>8.870193</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>319.018654</td>\n",
       "      <td>1.375395</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.705502</td>\n",
       "      <td>14.278149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>8.423478</td>\n",
       "      <td>14.960437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.496163</td>\n",
       "      <td>59.559201</td>\n",
       "      <td>2</td>\n",
       "      <td>9.074396</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>318.229277</td>\n",
       "      <td>1.375401</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.642979</td>\n",
       "      <td>13.737467</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>6.355600</td>\n",
       "      <td>16.068119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.589230</td>\n",
       "      <td>77.023050</td>\n",
       "      <td>3</td>\n",
       "      <td>6.419909</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>320.859017</td>\n",
       "      <td>1.375428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.170317</td>\n",
       "      <td>15.347521</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>10.860881</td>\n",
       "      <td>18.099591</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.819463</td>\n",
       "      <td>88.580965</td>\n",
       "      <td>0</td>\n",
       "      <td>8.553172</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>319.071265</td>\n",
       "      <td>1.375382</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.015816</td>\n",
       "      <td>16.877070</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location    MinTemp    MaxTemp  Rainfall  Evaporation   Sunshine  \\\n",
       "0        22   8.694688  13.589273       0.6     2.263869  57.554920   \n",
       "1        22   5.301024  14.668806       0.0     2.406140  69.201128   \n",
       "2        22   8.423478  14.960437       0.0     2.496163  59.559201   \n",
       "3        22   6.355600  16.068119       0.0     2.589230  77.023050   \n",
       "4        22  10.860881  18.099591       1.0     2.819463  88.580965   \n",
       "\n",
       "   WindGustDir  WindGustSpeed  WindDir9am  WindDir3pm  ...  Pressure9am  \\\n",
       "0            0       8.870193           0           1  ...   318.255596   \n",
       "1            1       8.870193           4           2  ...   319.018654   \n",
       "2            2       9.074396           0           2  ...   318.229277   \n",
       "3            3       6.419909          12          14  ...   320.859017   \n",
       "4            0       8.553172           8          11  ...   319.071265   \n",
       "\n",
       "   Pressure3pm  Cloud9am  Cloud3pm    Temp9am    Temp3pm  RainToday  \\\n",
       "0     1.375390       8.0       0.0  10.549671  13.043088          0   \n",
       "1     1.375395       7.0       1.0  10.705502  14.278149          0   \n",
       "2     1.375401       8.0       2.0  12.642979  13.737467          0   \n",
       "3     1.375428       0.0       5.0  11.170317  15.347521          0   \n",
       "4     1.375382       7.0       8.0  11.015816  16.877070          0   \n",
       "\n",
       "   RainTomorrow  Month  Day  \n",
       "0             0     12    1  \n",
       "1             0     12    2  \n",
       "2             0     12    3  \n",
       "3             0     12    4  \n",
       "4             0     12    5  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "872500d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location         0.0\n",
       "MinTemp          0.0\n",
       "MaxTemp          0.0\n",
       "Rainfall         0.0\n",
       "Evaporation      0.0\n",
       "Sunshine         0.0\n",
       "WindGustDir      0.0\n",
       "WindGustSpeed    0.0\n",
       "WindDir9am       0.0\n",
       "WindDir3pm       0.0\n",
       "WindSpeed9am     0.0\n",
       "WindSpeed3pm     0.0\n",
       "Humidity9am      0.0\n",
       "Humidity3pm      0.0\n",
       "Pressure9am      0.0\n",
       "Pressure3pm      0.0\n",
       "Cloud9am         0.0\n",
       "Cloud3pm         0.0\n",
       "Temp9am          0.0\n",
       "Temp3pm          0.0\n",
       "RainToday        0.0\n",
       "RainTomorrow     0.0\n",
       "Month            0.0\n",
       "Day              0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5fd5af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['RainTomorrow']\n",
    "X = df.drop(['RainTomorrow'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bead0f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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', 'Month', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eee94a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "141621    0\n",
       "141622    0\n",
       "141623    0\n",
       "141624    0\n",
       "141625    0\n",
       "Name: RainTomorrow, Length: 141626, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "61739595",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Acer\\anaconda3\\lib\\site-packages\\imblearn\\utils\\_validation.py:638: FutureWarning: Pass sampling_strategy=0.9 as keyword args. From version 0.9 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 110382, 1: 31244})\n",
      "The number of classes after fit Counter({0: 110382, 1: 99343})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from collections import Counter\n",
    "\n",
    "os=RandomOverSampler(0.9)\n",
    "X_ns,y_ns=os.fit_sample(X,y)\n",
    "print(\"The number of classes before fit {}\".format(Counter(y)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_ns)))\n",
    "\n",
    "X = X_ns\n",
    "y = y_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d14ca497",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c560ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05fd053b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c138383f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf2f28d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f32b811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27343  5767]\n",
      " [ 7418 22390]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.83      0.81     33110\n",
      "           1       0.80      0.75      0.77     29808\n",
      "\n",
      "    accuracy                           0.79     62918\n",
      "   macro avg       0.79      0.79      0.79     62918\n",
      "weighted avg       0.79      0.79      0.79     62918\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fee1f85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy is low because of this is just a simple training .no hyperpparmeter training \n",
    "# but it is reccomand for you to do hyperparameter tuning if you have a enough cmputation power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d0d5a1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7884818237910588"
      ]
     },
     "execution_count": 20,
     "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": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "metrics.plot_roc_curve(gb, X_test, y_test)\n",
    "metrics.roc_auc_score(y_test, y_pred, average=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "75d0789a",
   "metadata": {},
   "outputs": [],
   "source": [
    "err  =(y_pred- y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be69d16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FSX6W2W9nb2D2OxiHgf1V9cJYBybpjOEMYRVI8gfM/rmOAE8z+xhugK/5h/4+uJJ8Ytxj0PIl+Z1xj2Gl+ZTRKpDk34Erqur/5tU/Bhysqs3jGZlORZKXq+qScY9Dy3Mmfn4uGa0OPwJ+CvjBvPr6PqZVKsnvnuwQ4AxhlUvynZMdAtat5FhWAwNhdfgycCDJi8ArXbsEuAz44sk6aVX4I+CPgeNDjrkku/qtA64F3phXD/AvKz+c8TIQVoGq+maSn2H2z4BvYPYf4wzwrap6d6yD02K+DfxdVT0z/0CSL4xhPFqabwCfqKpn5x9I8sSKj2bMvIcgnYIknwReq6ofDtR+sqr+M8m6qnp1jMOTlsRAkE6zJN+uqp8f9zikpXKNUzr9hv3/HtKqZyBIp9+fj3sA0nK4ZCRJApwhSJKagSBJAgwESVIzECRJAPw/23AsYvYCL4wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "err.value_counts().plot(kind='bar',color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "157d91d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.20955847293302393\n",
      "MSE: 0.20955847293302393\n",
      "RMSE: 0.4577755704851712\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('MAE:', metrics.mean_absolute_error(y_test,y_pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "394825ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "#open a file, where you ant to store the data\n",
    "file = open('model.pkl', 'wb')\n",
    "# dump information to that file\n",
    "pickle.dump(gb, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d0ce096",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}