machinLearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(66893, 70)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Id</th>\n",
       "      <th>LocID</th>\n",
       "      <th>Location</th>\n",
       "      <th>PopMale_0_4</th>\n",
       "      <th>PopFemale_0_4</th>\n",
       "      <th>PopTotal_0_4</th>\n",
       "      <th>PopMale_5_9</th>\n",
       "      <th>PopFemale_5_9</th>\n",
       "      <th>PopTotal_5_9</th>\n",
       "      <th>PopMale_10_14</th>\n",
       "      <th>...</th>\n",
       "      <th>PopMale_95_99</th>\n",
       "      <th>PopFemale_95_99</th>\n",
       "      <th>PopTotal_95_99</th>\n",
       "      <th>PopMale_100Plus</th>\n",
       "      <th>PopFemale_100Plus</th>\n",
       "      <th>PopTotal_100Plus</th>\n",
       "      <th>PopMale</th>\n",
       "      <th>PopFemale</th>\n",
       "      <th>PopTotal</th>\n",
       "      <th>YearDataCompleted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>630.044</td>\n",
       "      <td>661.578</td>\n",
       "      <td>1291.622</td>\n",
       "      <td>516.206</td>\n",
       "      <td>487.335</td>\n",
       "      <td>1003.541</td>\n",
       "      <td>461.378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4099.243</td>\n",
       "      <td>3652.874</td>\n",
       "      <td>7752.117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>641.199</td>\n",
       "      <td>673.293</td>\n",
       "      <td>1314.492</td>\n",
       "      <td>525.302</td>\n",
       "      <td>502.402</td>\n",
       "      <td>1027.704</td>\n",
       "      <td>465.611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4134.756</td>\n",
       "      <td>3705.395</td>\n",
       "      <td>7840.151</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>650.825</td>\n",
       "      <td>669.274</td>\n",
       "      <td>1320.099</td>\n",
       "      <td>533.097</td>\n",
       "      <td>521.672</td>\n",
       "      <td>1054.769</td>\n",
       "      <td>471.928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4174.450</td>\n",
       "      <td>3761.546</td>\n",
       "      <td>7935.996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>659.896</td>\n",
       "      <td>663.606</td>\n",
       "      <td>1323.502</td>\n",
       "      <td>538.351</td>\n",
       "      <td>541.216</td>\n",
       "      <td>1079.567</td>\n",
       "      <td>480.448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4218.336</td>\n",
       "      <td>3821.348</td>\n",
       "      <td>8039.684</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>670.694</td>\n",
       "      <td>663.295</td>\n",
       "      <td>1333.989</td>\n",
       "      <td>540.820</td>\n",
       "      <td>557.196</td>\n",
       "      <td>1098.016</td>\n",
       "      <td>489.780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4266.484</td>\n",
       "      <td>3884.832</td>\n",
       "      <td>8151.316</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  LocID     Location  PopMale_0_4  PopFemale_0_4  PopTotal_0_4  \\\n",
       "Time                                                                           \n",
       "1950-01-01   1      4  Afghanistan      630.044        661.578      1291.622   \n",
       "1951-01-01   2      4  Afghanistan      641.199        673.293      1314.492   \n",
       "1952-01-01   3      4  Afghanistan      650.825        669.274      1320.099   \n",
       "1953-01-01   4      4  Afghanistan      659.896        663.606      1323.502   \n",
       "1954-01-01   5      4  Afghanistan      670.694        663.295      1333.989   \n",
       "\n",
       "            PopMale_5_9  PopFemale_5_9  PopTotal_5_9  PopMale_10_14  ...  \\\n",
       "Time                                                                 ...   \n",
       "1950-01-01      516.206        487.335      1003.541        461.378  ...   \n",
       "1951-01-01      525.302        502.402      1027.704        465.611  ...   \n",
       "1952-01-01      533.097        521.672      1054.769        471.928  ...   \n",
       "1953-01-01      538.351        541.216      1079.567        480.448  ...   \n",
       "1954-01-01      540.820        557.196      1098.016        489.780  ...   \n",
       "\n",
       "            PopMale_95_99  PopFemale_95_99  PopTotal_95_99  PopMale_100Plus  \\\n",
       "Time                                                                          \n",
       "1950-01-01          0.004            0.003           0.007              0.0   \n",
       "1951-01-01          0.007            0.005           0.012              0.0   \n",
       "1952-01-01          0.012            0.009           0.021              0.0   \n",
       "1953-01-01          0.015            0.013           0.028              0.0   \n",
       "1954-01-01          0.014            0.011           0.025              0.0   \n",
       "\n",
       "            PopFemale_100Plus  PopTotal_100Plus   PopMale  PopFemale  \\\n",
       "Time                                                                   \n",
       "1950-01-01                0.0               0.0  4099.243   3652.874   \n",
       "1951-01-01                0.0               0.0  4134.756   3705.395   \n",
       "1952-01-01                0.0               0.0  4174.450   3761.546   \n",
       "1953-01-01                0.0               0.0  4218.336   3821.348   \n",
       "1954-01-01                0.0               0.0  4266.484   3884.832   \n",
       "\n",
       "            PopTotal  YearDataCompleted  \n",
       "Time                                     \n",
       "1950-01-01  7752.117                  1  \n",
       "1951-01-01  7840.151                  1  \n",
       "1952-01-01  7935.996                  1  \n",
       "1953-01-01  8039.684                  1  \n",
       "1954-01-01  8151.316                  1  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/PopulationByAgeSex.csv\", index_col=\"Time\", parse_dates=True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7752.117]\n",
      " [ 7840.151]\n",
      " [ 7935.996]\n",
      " ...\n",
      " [30959.811]\n",
      " [30964.052]\n",
      " [30965.421]] \n",
      " le shape du targets: (66893, 1)\n",
      " le shape du features: (66893, 68)\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>Id</th>\n",
       "      <th>LocID</th>\n",
       "      <th>PopMale_0_4</th>\n",
       "      <th>PopFemale_0_4</th>\n",
       "      <th>PopTotal_0_4</th>\n",
       "      <th>PopMale_5_9</th>\n",
       "      <th>PopFemale_5_9</th>\n",
       "      <th>PopTotal_5_9</th>\n",
       "      <th>PopMale_10_14</th>\n",
       "      <th>PopFemale_10_14</th>\n",
       "      <th>...</th>\n",
       "      <th>PopTotal_90_94</th>\n",
       "      <th>PopMale_95_99</th>\n",
       "      <th>PopFemale_95_99</th>\n",
       "      <th>PopTotal_95_99</th>\n",
       "      <th>PopMale_100Plus</th>\n",
       "      <th>PopFemale_100Plus</th>\n",
       "      <th>PopTotal_100Plus</th>\n",
       "      <th>PopMale</th>\n",
       "      <th>PopFemale</th>\n",
       "      <th>YearDataCompleted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>630.044</td>\n",
       "      <td>661.578</td>\n",
       "      <td>1291.622</td>\n",
       "      <td>516.206</td>\n",
       "      <td>487.335</td>\n",
       "      <td>1003.541</td>\n",
       "      <td>461.378</td>\n",
       "      <td>423.326</td>\n",
       "      <td>...</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4099.243</td>\n",
       "      <td>3652.874</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>641.199</td>\n",
       "      <td>673.293</td>\n",
       "      <td>1314.492</td>\n",
       "      <td>525.302</td>\n",
       "      <td>502.402</td>\n",
       "      <td>1027.704</td>\n",
       "      <td>465.611</td>\n",
       "      <td>427.618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4134.756</td>\n",
       "      <td>3705.395</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952-01-01</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>650.825</td>\n",
       "      <td>669.274</td>\n",
       "      <td>1320.099</td>\n",
       "      <td>533.097</td>\n",
       "      <td>521.672</td>\n",
       "      <td>1054.769</td>\n",
       "      <td>471.928</td>\n",
       "      <td>434.828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4174.450</td>\n",
       "      <td>3761.546</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953-01-01</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>659.896</td>\n",
       "      <td>663.606</td>\n",
       "      <td>1323.502</td>\n",
       "      <td>538.351</td>\n",
       "      <td>541.216</td>\n",
       "      <td>1079.567</td>\n",
       "      <td>480.448</td>\n",
       "      <td>443.818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4218.336</td>\n",
       "      <td>3821.348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>670.694</td>\n",
       "      <td>663.295</td>\n",
       "      <td>1333.989</td>\n",
       "      <td>540.820</td>\n",
       "      <td>557.196</td>\n",
       "      <td>1098.016</td>\n",
       "      <td>489.780</td>\n",
       "      <td>454.597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4266.484</td>\n",
       "      <td>3884.832</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  LocID  PopMale_0_4  PopFemale_0_4  PopTotal_0_4  PopMale_5_9  \\\n",
       "Time                                                                           \n",
       "1950-01-01   1      4      630.044        661.578      1291.622      516.206   \n",
       "1951-01-01   2      4      641.199        673.293      1314.492      525.302   \n",
       "1952-01-01   3      4      650.825        669.274      1320.099      533.097   \n",
       "1953-01-01   4      4      659.896        663.606      1323.502      538.351   \n",
       "1954-01-01   5      4      670.694        663.295      1333.989      540.820   \n",
       "\n",
       "            PopFemale_5_9  PopTotal_5_9  PopMale_10_14  PopFemale_10_14  ...  \\\n",
       "Time                                                                     ...   \n",
       "1950-01-01        487.335      1003.541        461.378          423.326  ...   \n",
       "1951-01-01        502.402      1027.704        465.611          427.618  ...   \n",
       "1952-01-01        521.672      1054.769        471.928          434.828  ...   \n",
       "1953-01-01        541.216      1079.567        480.448          443.818  ...   \n",
       "1954-01-01        557.196      1098.016        489.780          454.597  ...   \n",
       "\n",
       "            PopTotal_90_94  PopMale_95_99  PopFemale_95_99  PopTotal_95_99  \\\n",
       "Time                                                                         \n",
       "1950-01-01           0.139          0.004            0.003           0.007   \n",
       "1951-01-01           0.172          0.007            0.005           0.012   \n",
       "1952-01-01           0.231          0.012            0.009           0.021   \n",
       "1953-01-01           0.297          0.015            0.013           0.028   \n",
       "1954-01-01           0.315          0.014            0.011           0.025   \n",
       "\n",
       "            PopMale_100Plus  PopFemale_100Plus  PopTotal_100Plus   PopMale  \\\n",
       "Time                                                                         \n",
       "1950-01-01              0.0                0.0               0.0  4099.243   \n",
       "1951-01-01              0.0                0.0               0.0  4134.756   \n",
       "1952-01-01              0.0                0.0               0.0  4174.450   \n",
       "1953-01-01              0.0                0.0               0.0  4218.336   \n",
       "1954-01-01              0.0                0.0               0.0  4266.484   \n",
       "\n",
       "            PopFemale  YearDataCompleted  \n",
       "Time                                      \n",
       "1950-01-01   3652.874                  1  \n",
       "1951-01-01   3705.395                  1  \n",
       "1952-01-01   3761.546                  1  \n",
       "1953-01-01   3821.348                  1  \n",
       "1954-01-01   3884.832                  1  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select Target and Features\n",
    "#Targets\n",
    "y = df['PopTotal']\n",
    "y= y.values.reshape(-1,1)\n",
    "print(f\"{y} \\n le shape du targets: {y.shape}\")\n",
    "\n",
    "#Features\n",
    "x = df.drop(['PopTotal', 'Location'], axis =1)\n",
    "print(f\" le shape du features: {x.shape}\")\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduct = PCA(n_components=50)\n",
    "#x = reduct.fit_transform(x)\n",
    "#x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0000e+00 0.0000e+00 2.0790e+04 ... 1.7997e+04 1.7173e+04 0.0000e+00]\n",
      " [1.0000e+00 0.0000e+00 2.0900e+04 ... 1.8085e+04 1.7276e+04 0.0000e+00]\n",
      " [2.0000e+00 0.0000e+00 2.1008e+04 ... 1.8179e+04 1.7372e+04 0.0000e+00]\n",
      " ...\n",
      " [6.6890e+04 1.6600e+02 2.3518e+04 ... 2.6797e+04 2.7079e+04 0.0000e+00]\n",
      " [6.6891e+04 1.6600e+02 2.3471e+04 ... 2.6795e+04 2.7080e+04 0.0000e+00]\n",
      " [6.6892e+04 1.6600e+02 2.3418e+04 ... 2.6791e+04 2.7083e+04 0.0000e+00]]\n"
     ]
    }
   ],
   "source": [
    "#preprocessing:\n",
    "#encoder features\n",
    "encoder_x = OrdinalEncoder()\n",
    "encoder_x = encoder_x.fit_transform(x)\n",
    "print(encoder_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17634.]\n",
      " [17705.]\n",
      " [17788.]\n",
      " ...\n",
      " [26980.]\n",
      " [26981.]\n",
      " [26982.]]\n"
     ]
    }
   ],
   "source": [
    "#encoder targets\n",
    "encoder_y = OrdinalEncoder()\n",
    "encoder_y= encoder_y.fit_transform(y)\n",
    "print(encoder_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pour le train: (53514, 68), (53514, 1)\n",
      "pour le test: (13379, 68), (13379, 1)\n"
     ]
    }
   ],
   "source": [
    "#create a train set and test set\n",
    "x_train, x_test, y_train, y_test = train_test_split(encoder_x, encoder_y, test_size=.2)\n",
    "print(f'pour le train: {x_train.shape}, {y_train.shape}')\n",
    "print(f'pour le test: {x_test.shape}, {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MOHAM\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('sgdregressor', SGDRegressor())])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#modeling and normalisation\n",
    "model = make_pipeline(StandardScaler(), SGDRegressor())\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999928469334789"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate models\n",
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: [17670.44473442 17765.39797589 17854.58029726 17942.74005004\n",
      " 18046.1303702  18137.38063335 18261.51368429 18404.7644845\n",
      " 18533.36590681 18673.86648995 18823.80490078 19008.16047889\n",
      " 19185.50654553 19346.38719983 19500.36729716 19664.86594785\n",
      " 19833.03780298 20027.84642917 20198.31693169 20400.34974586\n",
      " 20600.96171562 20775.66205995 20945.67338333 21122.51736034\n",
      " 21268.94569647 21416.09757723 21534.46708692 21635.91734825\n",
      " 21712.55844398], targets: [[17634.]\n",
      " [17705.]\n",
      " [17788.]\n",
      " [17877.]\n",
      " [17996.]\n",
      " [18101.]\n",
      " [18209.]\n",
      " [18349.]\n",
      " [18521.]\n",
      " [18671.]\n",
      " [18847.]\n",
      " [18989.]\n",
      " [19155.]\n",
      " [19330.]\n",
      " [19489.]\n",
      " [19677.]\n",
      " [19875.]\n",
      " [20093.]\n",
      " [20308.]\n",
      " [20517.]\n",
      " [20706.]\n",
      " [20869.]\n",
      " [21025.]\n",
      " [21189.]\n",
      " [21396.]\n",
      " [21534.]\n",
      " [21649.]\n",
      " [21737.]\n",
      " [21797.]]\n"
     ]
    }
   ],
   "source": [
    "# voir la prediction du model\n",
    "predict = model.predict(encoder_x[0:29])\n",
    "targets = encoder_y[0:29]\n",
    "print(f\"prediction: {predict}, targets: {targets}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#graphic\n",
    "plt.plot(predict, label=\"prediction\")\n",
    "plt.plot(targets, label=\"Targets\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}