ML-For-Beginners/translations/fa/3-Web-App/1-Web-App/solution/notebook.ipynb

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  {
   "source": [
    "## ساخت یک برنامه وب با استفاده از مدل رگرسیون برای بررسی مشاهده بشقاب پرنده\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           datetime                  city state country     shape  \\\n",
       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
       "\n",
       "   duration (seconds) duration (hours/min)  \\\n",
       "0              2700.0           45 minutes   \n",
       "1              7200.0              1-2 hrs   \n",
       "2                20.0           20 seconds   \n",
       "3                20.0             1/2 hour   \n",
       "4               900.0           15 minutes   \n",
       "\n",
       "                                            comments date posted   latitude  \\\n",
       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
       "\n",
       "    longitude  \n",
       "0  -97.941111  \n",
       "1  -98.581082  \n",
       "2   -2.916667  \n",
       "3  -96.645833  \n",
       "4 -157.803611  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "ufos = pd.read_csv('../data/ufos.csv')\n",
    "ufos.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "\n",
    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
    "\n",
    "ufos.Country.unique()\n",
    "\n",
    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
     ]
    }
   ],
   "source": [
    "ufos.dropna(inplace=True)\n",
    "\n",
    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
    "\n",
    "ufos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Seconds  Country   Latitude   Longitude\n",
       "2      20.0        3  53.200000   -2.916667\n",
       "3      20.0        4  28.978333  -96.645833\n",
       "14     30.0        4  35.823889  -80.253611\n",
       "23     60.0        4  45.582778 -122.352222\n",
       "24      3.0        3  51.783333   -0.783333"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
    "\n",
    "ufos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Selected_features = ['Seconds','Latitude','Longitude']\n",
    "\n",
    "X = ufos[Selected_features]\n",
    "y = ufos['Country']\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        41\n",
      "           1       1.00      0.02      0.05       250\n",
      "           2       0.00      0.00      0.00         8\n",
      "           3       0.94      1.00      0.97       131\n",
      "           4       0.95      1.00      0.97      4743\n",
      "\n",
      "    accuracy                           0.95      5173\n",
      "   macro avg       0.78      0.60      0.60      5173\n",
      "weighted avg       0.95      0.95      0.93      5173\n",
      "\n",
      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
      "Accuracy:  0.9512855209742895\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, predictions))\n",
    "print('Predicted labels: ', predictions)\n",
    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[3]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "model_filename = 'ufo-model.pkl'\n",
    "pickle.dump(model, open(model_filename,'wb'))\n",
    "\n",
    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
    "print(model.predict([[50,44,-12]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**سلب مسئولیت**:  \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما برای دقت تلاش می‌کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادقتی‌هایی باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما هیچ مسئولیتی در قبال سوءتفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n"
   ]
  }
 ]
}