Ensemble Learning/04. Random Forest Implementation.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following steps form the basis for any machine learning workflow once we have a problem and model in mind:\n",
    "\n",
    "1. State the question and determine required data\n",
    "2. Acquire the data in an accessible format\n",
    "3. Identify and correct missing data points/anomalies as required\n",
    "4. Prepare the data for the machine learning model\n",
    "5. Establish a baseline model that you aim to exceed\n",
    "6. Train the model on the training data\n",
    "7. Make predictions on the test data\n",
    "8. Compare predictions to the known test set targets and calculate performance metrics\n",
    "9. If performance is not satisfactory, adjust the model, acquire more data, or try a different modeling technique\n",
    "10. Interpret model and report results visually and numerically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### link to download datatset : https://drive.google.com/file/d/1pko9oRmCllAxipZoa3aoztGZfPAD2iwj/view?usp=sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv('temps.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>forecast_noaa</th>\n",
       "      <th>forecast_acc</th>\n",
       "      <th>forecast_under</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fri</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
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       "      <td>44</td>\n",
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       "      <td>44</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mon</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tues</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  week  temp_2  temp_1  average  actual  forecast_noaa  \\\n",
       "0  2016      1    1   Fri      45      45     45.6      45             43   \n",
       "1  2016      1    2   Sat      44      45     45.7      44             41   \n",
       "2  2016      1    3   Sun      45      44     45.8      41             43   \n",
       "3  2016      1    4   Mon      44      41     45.9      40             44   \n",
       "4  2016      1    5  Tues      41      40     46.0      44             46   \n",
       "\n",
       "   forecast_acc  forecast_under  friend  \n",
       "0            50              44      29  \n",
       "1            50              44      61  \n",
       "2            46              47      56  \n",
       "3            48              46      53  \n",
       "4            46              46      41  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are not features. These are predicted outcomes\n",
    "raw_data.drop(['forecast_noaa','forecast_acc','forecast_under'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>348.0</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.477011</td>\n",
       "      <td>15.514368</td>\n",
       "      <td>62.652299</td>\n",
       "      <td>62.701149</td>\n",
       "      <td>59.760632</td>\n",
       "      <td>62.543103</td>\n",
       "      <td>60.034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.498380</td>\n",
       "      <td>8.772982</td>\n",
       "      <td>12.165398</td>\n",
       "      <td>12.120542</td>\n",
       "      <td>10.527306</td>\n",
       "      <td>11.794146</td>\n",
       "      <td>15.626179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>49.975000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>58.200000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>69.025000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>77.400000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year       month         day      temp_2      temp_1     average  \\\n",
       "count   348.0  348.000000  348.000000  348.000000  348.000000  348.000000   \n",
       "mean   2016.0    6.477011   15.514368   62.652299   62.701149   59.760632   \n",
       "std       0.0    3.498380    8.772982   12.165398   12.120542   10.527306   \n",
       "min    2016.0    1.000000    1.000000   35.000000   35.000000   45.100000   \n",
       "25%    2016.0    3.000000    8.000000   54.000000   54.000000   49.975000   \n",
       "50%    2016.0    6.000000   15.000000   62.500000   62.500000   58.200000   \n",
       "75%    2016.0   10.000000   23.000000   71.000000   71.000000   69.025000   \n",
       "max    2016.0   12.000000   31.000000  117.000000  117.000000   77.400000   \n",
       "\n",
       "           actual      friend  \n",
       "count  348.000000  348.000000  \n",
       "mean    62.543103   60.034483  \n",
       "std     11.794146   15.626179  \n",
       "min     35.000000   28.000000  \n",
       "25%     54.000000   47.750000  \n",
       "50%     62.500000   60.000000  \n",
       "75%     71.000000   71.000000  \n",
       "max     92.000000   95.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistic for each columns\n",
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 348 entries, 0 to 347\n",
      "Data columns (total 9 columns):\n",
      "year       348 non-null int64\n",
      "month      348 non-null int64\n",
      "day        348 non-null int64\n",
      "week       348 non-null object\n",
      "temp_2     348 non-null int64\n",
      "temp_1     348 non-null int64\n",
      "average    348 non-null float64\n",
      "actual     348 non-null int64\n",
      "friend     348 non-null int64\n",
      "dtypes: float64(1), int64(7), object(1)\n",
      "memory usage: 24.5+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    31\n",
       "7     31\n",
       "5     31\n",
       "3     31\n",
       "1     31\n",
       "11    30\n",
       "10    30\n",
       "6     30\n",
       "4     30\n",
       "9     28\n",
       "2     26\n",
       "8     19\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In August entries are less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tues     52\n",
       "Fri      50\n",
       "Sat      50\n",
       "Wed      49\n",
       "Mon      49\n",
       "Sun      49\n",
       "Thurs    49\n",
       "Name: week, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.week.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>year</th>\n",
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       "      <th>week</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
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       "      <th>friend</th>\n",
       "      <th>week_Fri</th>\n",
       "      <th>week_Mon</th>\n",
       "      <th>week_Sat</th>\n",
       "      <th>week_Sun</th>\n",
       "      <th>week_Thurs</th>\n",
       "      <th>week_Tues</th>\n",
       "      <th>week_Wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fri</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sat</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mon</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tues</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  week  temp_2  temp_1  average  actual  friend  week_Fri  \\\n",
       "0  2016      1    1   Fri      45      45     45.6      45      29         1   \n",
       "1  2016      1    2   Sat      44      45     45.7      44      61         0   \n",
       "2  2016      1    3   Sun      45      44     45.8      41      56         0   \n",
       "3  2016      1    4   Mon      44      41     45.9      40      53         0   \n",
       "4  2016      1    5  Tues      41      40     46.0      44      41         0   \n",
       "\n",
       "   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
       "0         0         0         0           0          0         0  \n",
       "1         0         1         0           0          0         0  \n",
       "2         0         0         1           0          0         0  \n",
       "3         1         0         0           0          0         0  \n",
       "4         0         0         0           0          1         0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.concat([raw_data,pd.get_dummies(raw_data.week,prefix='week')],axis=1)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data.drop('week',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'friend')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "plt.subplot(3,2, 1)\n",
    "plt.plot(raw_data.temp_1, color='red')\n",
    "plt.title(\"Temp_1\")\n",
    "\n",
    "plt.subplot(3,2, 2)\n",
    "plt.plot(raw_data.temp_2, color='blue')\n",
    "plt.title(\"Temp_2\")\n",
    "\n",
    "plt.subplot(3,2, 3)\n",
    "plt.plot(raw_data.average, color='black')\n",
    "plt.title(\"average\")\n",
    "\n",
    "plt.subplot(3,2, 4)\n",
    "plt.plot(raw_data.friend, color='black')\n",
    "plt.title(\"friend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[286]\n",
      "[287]\n"
     ]
    }
   ],
   "source": [
    "print(raw_data[raw_data.temp_1>100].index.tolist())\n",
    "print(raw_data[raw_data.temp_2>100].index.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop outliers \n",
    "raw_data.drop(index=[286,287],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating labels from datasets\n",
    "raw_label = raw_data.actual\n",
    "del raw_data['actual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble        import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(raw_data,\n",
    "                                                 raw_label,\n",
    "                                                 test_size=0.25,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "our baseline is the error we would get if we simply predicted the average max temperature for all days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.83"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The baseline predictions are the historical averages\n",
    "round(np.mean(abs(X_test['average'] - y_test)),2) # in degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(259, 14)\n",
      "(87, 14)\n",
      "(259,)\n",
      "(87,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=1000 , random_state = 42)\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.88 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predict = model.predict(X_test)\n",
    "\n",
    "# Calculate the absolute errors\n",
    "errors = abs(predict - y_test)\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### according to dataset this error should be come to near 5.88 but it show 3.88 which is 25% more better than dataset. But it is not sufficient when the data in quantitative format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.97 %.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.836331762084538"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / y_test)\n",
    "# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')\n",
    "model.score(X_test,y_test) # this score should be near to 1 {this is R^2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = X_train.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: temp_1          Importance: 0.6\n",
      "Variable: average         Importance: 0.31\n",
      "Variable: temp_2          Importance: 0.03\n",
      "Variable: day             Importance: 0.02\n",
      "Variable: friend          Importance: 0.02\n",
      "Variable: month           Importance: 0.01\n",
      "Variable: year            Importance: 0.0\n",
      "Variable: week_Fri        Importance: 0.0\n",
      "Variable: week_Mon        Importance: 0.0\n",
      "Variable: week_Sat        Importance: 0.0\n",
      "Variable: week_Sun        Importance: 0.0\n",
      "Variable: week_Thurs      Importance: 0.0\n",
      "Variable: week_Tues       Importance: 0.0\n",
      "Variable: week_Wed        Importance: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = list(model.feature_importances_) # important features\n",
    "\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(features_names, importances)]\n",
    "\n",
    "# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "\n",
    "[print('Variable: {:15} Importance: {}'.format(*pair)) for pair in feature_importances]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additionally, if we are using a different model, say a support vector machine, we could use the random forest feature importances as a kind of feature selection method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train on a model with most important features only i.e temp_1 and average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.46 degrees.\n",
      "Accuracy: 93.02 %.\n"
     ]
    }
   ],
   "source": [
    "rf_most_important = RandomForestRegressor(n_estimators= 1000, random_state=42)\n",
    "\n",
    "rf_most_important.fit(X_train.loc[:,['temp_1','average']], y_train)\n",
    "\n",
    "predictions = rf_most_important.predict(X_test.loc[:,['temp_1','average']])\n",
    "\n",
    "errors = abs(predictions - y_test)\n",
    "\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n",
    "\n",
    "mape = np.mean(100 * (errors / y_test))\n",
    "accuracy = 100 - mape\n",
    "print('Accuracy:', round(accuracy, 2), '%.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that we actually do not need all the data we collected to make accurate predictions! If we were to continue using this model, we could only collect the two variables and achieve nearly the same performance. In a production setting, we would need to weigh the decrease in accuracy versus the extra time required to obtain more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6AAOZJEnSKhq3hWwhsENVuV2SJEnSajbuLMvvA/edZCGSJEnz1bgtZJsCFyb5LnDT1Mmq2m0iVUmSJM0j4waygyZZhCRJ0nw27rIXp026EEmSpPlqrDFkSXZJcmaS3ya5OcltSa6ddHGSJEnzwbiD+t8P7AX8mG5j8Vf35yRJkrSKxl4YtqouTrJ2Vd0GfCzJ6ROsS5Ikad4YN5DdkGRd4Nwk/wL8L7DB5MqSJEmaP8btsnxZf+1+wPXAFsDzJlWUJEnSfDJuIPvzqrqxqq6tqoOr6gDg2ZMsTJIkab4YN5C9YoZze6/GOiRJkuatFY4hS7IX8GLgAUlOGLlpI+CqSRYmSZI0X8w2qP90ugH8mwKHjpy/DjhvUkVJkiTNJysMZFV1WZLFwPWu1i9JkjQZsy57UVW3JbkhycZVdc3KPHiSXYH3AmsDH6mqd0y7/bXA64HbgN8C+1bVhSvzHJJm9szPzTT0c8354u5HNn1+SZpLxl2H7Ebg/CRfplv2AoCq2n95d0iyNnAY8HRgMXBmkhOmBa5PVNUH+ut3A/4N2HXlvgVJkqS5bdxA9oX+z8rYGbi4qi4BSHIMsDuwNJBV1eh+mBsAtZLPIUmSNOeNFciq6sh+pf4H9acuqqpbZrnbZsDlI8eLgUdPvyjJ64EDgHWBp870QEn2BfYF2HLLLccpWZIkac4Yax2yJE+m21j8MOA/gB8leeJsd5vh3B1awKrqsKraFvhb4K0zPVBVfaiqFlbVwgULFoxTsiRJ0pwxbpflocCfVtVFAEkeBHwS2GkF91lMt8XSlM2BK1Zw/THAf45ZjyRJ0l3GuCv1320qjAFU1Y+Au81ynzOB7ZJs03d37gmMLi5Lku1GDp9F1wonSZI0r4zbQrYoyUeBo/rjlwBnregOVXVrkv2Ak+mWvTi8qi5IcgiwqKpOAPZL8jTgFuBqZt6iSZIk6S5t3ED2Orr1wvanGxv2DbqxZCtUVScBJ007d+DI1381dqWSJEl3UePOsrwpyfuBrwK/p5tlefNEK5MkSZonxgpkSZ4FfAD4CV0L2TZJXlNVX5xkcZIkSfPBysyyfEpVXQyQZFu6hWINZJIkSato3FmWV06Fsd4lwJUTqEeSJGneGbeF7IIkJwHH0i3u+gK6vSmfB1BVn55QfZIkSXd54way9YBfAk/qj5cA9wKeQxfQDGSSJEl30rizLF856UIkSZLmq3FnWW4D/CWw9eh9qmq3yZQlSZI0f4zbZflZ4KPAiXTrkEmSJGk1GTeQ3VhV75toJZIkSfPUuIHsvUneBpwC3DR1sqrOnkhVkiRJ88i4geyPgZcBT2VZl2X1x5IkSVoF4way5wIPcP9KSZKk1W/clfq/B2wyyUIkSZLmq3FbyO4D/DDJmdx+DJnLXkiSJK2icQPZ2yZahSRJ0jw27kr9p026EEmSpPlqhYEsyXV0synvcBNQVXXPiVQlSZI0j6wwkFXVRmuqEEmSpPlq3FmWkiRJmhADmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1NhEA1mSXZNclOTiJG+a4fYDklyY5LwkX02y1STrkSRJGqKJBbIkawOHAc8EdgD2SrLDtMvOARZW1cOA44F/mVQ9kiRJQzXJFrKdgYur6pKquhk4Bth99IKq+npV3dAfngFsPsF6JEmSBmmSgWwz4PKR48X9ueX5C+CLM92QZN8ki5IsWrJkyWosUZIkqb1JBrLMcK5mvDB5KbAQeNdMt1fVh6pqYVUtXLBgwWosUZIkqb11JvjYi4EtRo43B66YflGSpwF/Bzypqm6aYD2SJEmDNMkWsjOB7ZJsk2RdYE/ghNELkjwS+CCwW1VdOcFaJEmSBmtigayqbgX2A04GfgAcW1UXJDkkyW79Ze8CNgSOS3JukhOW83CSJEl3WZPssqSqTgJOmnbuwJGvnzbJ55ckSZoLXKlfkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGJhrIkuya5KIkFyd50wy3PzHJ2UluTbLHJGuRJEkaqokFsiRrA4cBzwR2APZKssO0y34G7A18YlJ1SJIkDd06E3zsnYGLq+oSgCTHALsDF05dUFWX9rf9foJ1SJIkDdokuyw3Ay4fOV7cn1tpSfZNsijJoiVLlqyW4iRJkoZikoEsM5yrO/NAVfWhqlpYVQsXLFiwimVJkiQNyyQD2WJgi5HjzYErJvh8kiRJc9IkA9mZwHZJtkmyLrAncMIEn0+SJGlOmlggq6pbgf2Ak4EfAMdW1QVJDkmyG0CSRyVZDLwA+GCSCyZVjyRJ0lBNcpYlVXUScNK0cweOfH0mXVemJEnSvOVK/ZIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNrdO6AGlFPnnEM5o+/157n9z0+SVJ84MtZJIkSY3ZQjbPffPDz276/E/Y5/NNn1/tPOvT72n6/F943huaPr8kjbKFTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWpsouuQJdkVeC+wNvCRqnrHtNvvDnwc2Am4CnhRVV06yZrWtJ8f9vqmz7/Z6w9r+vySJGl2E2shS7I2cBjwTGAHYK8kO0y77C+Aq6vqgcC7gXdOqh5JkqShmmQL2c7AxVV1CUCSY4DdgQtHrtkdOKj/+njg/UlSVTXBuiRpVs8+/uimz//5PV6ywtt3P77tPquf22PF+8y+8L9/uIYqmdmxz3/wCm8/8tNL1lAlM3vF8xY0ff5V8Yt3Xdb0+e/7xq1WePsv3/eNNVTJzO6z/xPv1P0mGcg2Ay4fOV4MPHp511TVrUmuAf4Q+NUE65IkabDOOOLKps+/y973bvr881Um1RiV5AXAM6rq1f3xy4Cdq+ovR665oL9mcX/8k/6aq6Y91r7Avv3h9sBFq7HUTRl2ALS+VTPk+oZcG1jfqrK+VWN9d96Qa4P5V99WVTVrk+gkW8gWA1uMHG8OXLGcaxYnWQfYGPj19Aeqqg8BH5pEkUkWVdXCSTz26mB9q2bI9Q25NrC+VWV9q8b67rwh1wbWtzyTXPbiTGC7JNskWRfYEzhh2jUnAK/ov94D+JrjxyRJ0nwzsRayfkzYfsDJdMteHF5VFyQ5BFhUVScAHwWOSnIxXcvYnpOqR5Ikaagmug5ZVZ0EnDTt3IEjX98IvGCSNYxhIl2hq5H1rZoh1zfk2sD6VpX1rRrru/OGXBtY34wmNqhfkiRJ43HrJEmSpMYMZJIkSY0ZyCRJkhqb6KD+oUmyFrBLVZ3eupa5KsnaVXVb6zo0OUm2Ararqq8kWR9Yp6qua12XVo8kmwFbMfL+X1Vt95rpJdmmqn4627lWhvzaDV3/XnJjVVWSbekWeT+lqm5tXNpgzLtB/Um+XVWPaV3HbJI8Ftia2//gf7xZQb0kP6Xbd/RjVXXhbNevSUnuCSyoqp9MO/+wqjqvUVmzSvL0qvpy6zoAkuxDtyvGvapq2yTbAR+oqj9pWNO/A8t9o6qq/ddgOTNK8j9V9fgk13H7WgNUVd2zUWm3k+SdwIvo9hSe+mBVVbVbu6qWSXJ2Ve047dxZVbVTq5pG6hj6a/cC4EtVdV2StwI7Av9YVWc3Lg3oFlsFnki3APyZwDnA1VX18sZ1Deb9ZV61kPVOSfJ84NNDXYQ2yVHAtsC5jPzgA80DGfAwuvXiPtK3OB4OHFNV17YsKskLgfcAVya5G7B3VZ3Z33wE3ZvTUH0U2LJ1Eb3XAzsD3wGoqh8nab2x3aL+78cBOwCf6o9fAJzVpKJpqurx/d8bta5lFn8ObF9VN7UuZFSSBwMPATZO8ryRm+4JrNemqjsY5Gs34u+r6rgkjweeAfwr8J/ccQ/pVtaqqhuSvAp4f1W9I8m5rYtiQO8v8zGQHQBsANya5EYG9gm2txDYYYiBse+6+jDw4SRPBD4JvDvJ8cA/VNXFjUp7C7BTVf1vkp3pFhx+S1V9mu7fuKkk03epWHoT8IdrspZZ3FRVNyfdS9Zvadb0/2FVHdnXsjfwlKq6pT/+AHBKw9Jup/+Acl5VPbR1LStwCXA3YGihYnvg2cAmwHNGzl8H7NOkojsa6ms3ZerD+7OA/6yqzyU5qGE9062V5FHAi1m2N/XaDesBhvX+Mu8C2Rz4BAvwfeC+wP+2LmS6JGvT/cC/kq5L9VDgaOAJdIsAP6hRaWtX1f8CVNV3kzwF+HySzWkcKHpPAF4K/Hba+dC1SA3FaUneAqyf5OnA/wFObFzTlPsDG7Fsv9sN+3ODUFW/T/K9JFtW1c9a17McNwDnJvkqI8GidbdvVX0O+FySx1TVt1vWsgKDfO1G/DzJB4GnAe9McneGNXHvAOBg4AtV9f0kDwC+2bimUc3fX+ZdIANI8gfAdow0hQ9hYGaSE+nCw0bAhUm+y+1/8IcwVuHHwNeBd02bHHF832LWynVJtp0aP9a3lD0Z+CxdV0hrZwA3VNVp029IclGDepbnTcBfAOcDr6EL2R9pWtEy7wDOSfL1/vhJwEHtypnR/YAL+p/d66dODuRnF7r9g5fXWjsE5yR5Pd3P7Oj786valbTU0F+7FwK7Av9aVb9Jcj/gjY1rWqqqvgZ8rQ+KVNUldB/4hqL5+8t8HNT/auCvgM3pxmjtAny7qp7atDAgyZNWdPtMv8zXtCQbVtX0Vp7mkjwcuH56l2k/nuyFVXV0m8q0OiW5L8vGxHynqn7Rsp7plvczPISf3SlJ1mVZS/ZFU100Q5DkOOCHdN1ahwAvAX5QVX/VtLCBmwvd5f1Qko8CG1fVlv179qur6i8bl7ZU6/eX+RjIzgceBZxRVY/oB5MeXFUvalzaUkneWVV/O9u5FpKsR9eCMsRPsLMa+izb1vX1Px/T3xSuoRv4+o9VddWar2qZoS47kGSXqjqjdR2z6VuNjwQupesu3wJ4xRBeQ4Ak51TVI5OcV1UP6z9QnTyQD8w/ZYbhD1X1gAbl3EGSo4E3D7W7PMkZdLNUP1tVj+zPfX8oITLdwNmXAA+oqkOSbAnct6q+u6ZqmI9dljdW1Y1JSHL3qvphku1bFzXN04Hp4euZM5xr4Si6T7DPYOQTbNOKVs5QZmwtT+v6vkg3OPgT/fGe/d/X0s1Wfc4M91kjRpYduAD4fX+6gCGEif+gn8nbOlTP4lDgT6vqIoAkD6KbmNN8WYneVGvdb5I8FPgF3VjVIVg48vV6dLPw7tWolpkMvbt8raq6bGrCUG9Ia1r+B937ylPpfrddB/w3XQPOGjEfA9niJJvQjS36cpKrgSsa1wRAktfR9ak/IMnoulkbAUNZzPaBVfWCJLtX1ZFJPgGc3LqolTD0JuHW9T2uqh43cnx+km9V1eOSvLRZVZ0hLzsw+lumdahekbtNhTGAqvpR3wo1FB/qx/j+Pd14rQ37r5uboXX4PUn+BziwRT0zOLh1AbO4vO+2rH5BVgOqAAAS+klEQVRy2F8CP2pc06hHV9WOSc4BqKqr++79NWbeBbKqem7/5UH94L2NgS81LGnUJ+haKP6ZbnD1lOuq6tcz32WNG/InWK26DZM8uqq+A0vHfWzY39Z6Re0hLzuwVh8k1hr5emlIG9DP76IkH6Vr6YauhXsQa7kBVNXUBJLTgEF0BU5JMrqW4Vp0LWaDmbU/pHGKy/E64H10ay7+EvhKf24obumDYgEkWcCylvg1Yt4FMoB+4bztqupj/Yu+GdB8a46quoZuvM5e/X+M+9D9G23YD6YfwtiAqU+wb2Vgn2DH1HxNslm0ru/VwOFJNuxruRZ4dZIN6D4otDTkZQc2pgs2U/9+o6ujF8MJF6+jW/x3f7pav0HXVdNUkufQDUq/rD8+EHg+cBnwVzWMrZMOHfn6VrpxeC9sU8od5fa7RKxL9+Hl+qGssVlVV7JsCMQQvQ/4DHDvJG8H9qD7PbfGzMdB/W+j+2SzfVU9KMn9geOmddM0lWQ/uum2v2RkrExVPaxhTQfMdLr/u6rq39ZkPSvSz5TZme7N6czRmTJJHlpV329WHMOvr69jY7r3h9+0rmVKklfMdH5qYce5IMlDquqC1nUMTT9EY5d+JfdnA/8G7AU8EnhBVT2jaYFzUJI/B3auqre0rgUgyYeZeVLEvjNc3kQ/ye9P6H63fbWq1uj46PkYyM6l+yE/e2Smx3ktw850SS6m689uOqNtVB9koVtR+1EsW4/nOcA3qurVTQqbpl/W5EDga3Q/VE8CDqmqw5sW1ht6fQBJnsUdZ9Ee0q6iZdJtULzl6DiouSQz7NW4hp53hXu5tn7/S/K9qnp4//XhdMtxvLM/bvKajdQ2F1rvZpTkjKrapXUdAElGVzJYD3gucHnrZS+SvAf4FnB6Vf28ZS3zscvy5qqqJFP9xBu0LmgGl9N1XQ5GVR0MkOQUYMfqtlAi3dYcxzUsbbo3Ao+cCrNJ/pBuQsRQAs+g60u3Xcg9gKfQLQi7B7DGpn2vSP+L8V/pumO2SfIIujA7lFlk42jVJf17utaJT9DtvPC7RnUsT/pu8hvoWihGu1FbT5J4O916lfStdy9lWevdB+hmnDeX2+8BOjXGbTAtLlX1qdHjdHs2f7lROaMupguH7+pngJ5OH9CA71XVGhtHNh8D2bHptpfYJMk+wKvo9mYckkuAU5N8gduPlRlCt+CWwM0jxzczrEH9i+mmK0+5ji7gDsXQ63tsv/7TeVV1cJJDgU+3Lqp3EF1X76kAVXVukm1aFnQnNPkFObLm4l50oezC/u9Tqqr1ZA2A99At1H0t3UKwiwCSPJL2W8hVVd3Qf/084KNVdRZwVpIhrTQ/uiTN1Bi33duUMpZt6NYUbKqq3g+8HyDd7gaPAx4L/F/g3nQb3K8R8zGQ3UQ3u+Nauu63A6tqCCl91M/6P+v2f4bkKOC7ST5D98vluXQLTQ7Fz4HvJPkcXX2709V7AAwi1A69vhv7v2/ox1deRffGOQS3VtU109YxGkwLwNBV1Q+BtwFv67uPPg68E3hX08KAqjo8ycl0vwC/N3LTL+j2zQWajcEbcuvdUlX1ytmvaqdfYmrq53Utuj0j37T8e6w5/aKwf0wXxB4H7EDXcnbUiu63us3HQHYfuq2TzqbrJvpK23LuaKR7cKPucDhbFVXV25N8kW6zbIBXVtU5LWua5if9nymf6/8eyvT0odd3Yr9O37vofkaK4bQgfz/Ji4G1k2xHN1NwKOvzjevm2S+ZjHS7HOxJ9yHqaroWgM+0qme6fvzOz6edm946dhT9Arxr0JBb75bqVwzYh67HYnQni6a7qCTZsl8hYNOR07+vgQxgT/Jlulawc+n2HP6nNT2Yf2ktA3lN1qg+Df8p3SevhcCxdM3QP1nhHdeQfn2vo1i2CvSvgJc7O0uTlG4/vF2q3zQ+3SbA6/XLsTSX5B7A39H97IZuQeJ/qKobV3jHNSjJIVV14Mjx2sDHq+olDcsiyWl0of9Y4Hi61omlBrRO2gql31qpwfNuRt96NzWmqO/eutvUckStZ9AmOR34Jt3yK0tXwK+q/25VE7SflDGbfgjTw+laQM8Avk23v/Wv1ngt8zGQwdLNqF8J7Ap8nW7Q5per6v81LYylP1h/V1Vf74+fTJfaH9u0sDkgyUK6X9rT9zscxCzaOVDfkLf9GbwkR9DNEPznPtAeRzej+6DGdV3Ksu6i0Tf90LXCD2WdtBUa8i/31rUlObeqHtHq+ZenVYheWUnuSZcDHtv/vQD4flXNuNzOJMy7Lssk+wOvoGt1+gjwxqq6pW8d+DHQPJABG0yFMYCqOnWgs0GH6Gi6mYzns4ZXWR7T0Os7JcnzgU8PqEvhPVX1hiQnMvM6RkOaZflK4Ogkb6abqfrFqnp345qoqq3Hua51K88c13pR588n+bOqOqlxHdNtluR9y7txIAs7Qze+/Aa6Gcg3AZuzhsdwz7tARteP/bypNWWmVNXv+ynNQ3BJkr9n2YDClzKAnQTmiCVVdcLslzUz9PoOADYAbkvyO5a1oLRc7Xvq5+BfG9awQrn9tjrvBT5IN3X+tCQ7VtXZM99zcFqM0VoZzcbgjaHJB5gsW6E/wFuS3ES3xd0QfnahCziD2Z5ruiTvpmsV245uHNnpdD+/r1jTC2PP2y7LIUu3NdHBdLM9prY3OWhIq6YPVZI/oZvaP317nUEs3TD0+oaqH4t1ZFW13uB8Run2xV2eqqqnrrFiVkHr7qWhjsEbR8NFf7ea3sAwJK27cmfT95qdDpxTVbet4LqJtx7PxxayuWBbYAu6qcHr0E21fiowiHFGA/dK4MF0+7gt3XaK4aylNej6+gkvLwG2qap/SLIFcL+qaro4bFXdlmRBknWranCtJFX1lNY1rCatP6FvmeTN08fgNa5pXK3+X36Gu0CrZqvu8qpabnfqNBNvPbaFbICSXAT8DfB9RsYZDflT0FAkOb+q/rh1HcszB+r7T7r/c0+tqj/qW2tPqapHNS5tajbUjnTbdl0/dX4Aa7ctleQ+wD8B96+qZybZAXhMVX20cWljad2a0X8gOJpujOVgxuDBcFvvWrdqri6t/+/NZk28zraQDdOSqjqxdRFz1BlJdqiqC1sXshxDr+/RVbVjknMAqurqJE0XJ05yVFW9DHgR8G66luOhrNs23RHAx+hm0gL8CPgUMCcCGY1aeebIGLyhtt7NlUHzs2k9KWI2E2+9MpAN09uSfATHGd0ZjwdekeSndK/d1MDWoXT3Dr2+W/pP/lN7vS6g/WzQnZJsRbd7xb83rmU2m1bVsf0sS6rq1iTLHZeyps3WylPtNqI+dNrx1XSrpR9K939xCGPwBjmDloEPml8J8767zkA2TIMeZzRwu7YuYBZDr+99dGNS7p3k7XSbi7+1bUl8APgS3RZOi0bOh+7nYkhraF2fbsP4qUC7CzCIhXV7g2zlGfIYvDnQendVVQ1p+7q7qom3HjuGbICGPs5o6JI8Htiuqj7Wt/BsWFWDWTZkDtT3YLqJJAG+2mobkemS/GdVva51HSvS//L+d+ChdGNAFwB7VNV5TQvrDXmMFgxzDN7QZ9AmOWOcls2hrzE37vcxwedvPkbQQDZAST4MvHvA44wGK8nb6LbD2r6qHpRug+zjqupxjUsD5kR97wU+Vf32SVp5SdYBtqcLtBdV1S2NS5reynM3lrXyfBRgAK08AKTbJ/djdDuVPLx/Lc/xA+qqaz1ofgiBZ0WGsMvGWmvqibRSHg+cm+SiJOclOT/JID5hzwHPBXajn4VXVVcwrAHgQ6/vbOCtSS5O8q50Wz1pTOn223wT8Iaq+j6w9UAWnD505M87uP0YrSEtuLtpVR1LP1Sjqm5lZF/GlpLcJ8lH+9BIkh2S/EXrulZC60HzW06NrewDz2fodscZilcCf9zXeCLw9TUZxsAxZEM19HFGQ3ZzVVWSqTE8Q9tyatD19WNRjkxyL+D5wDuTbFlV2zUuba74GN0A66n9QBfTfdL+fLOKGPYYrWmGPAbvCOb2DNrW3WGDnBQxpDGCBrIBcr2xVXJsv17VJkn2AV4FfLhxTaOGXt+UB9JNLNkasOt8fNtW1YuS7AVQVb/rx20NwhDHaE1zAN06c9sm+Rb9GLy2JS016Bm0QzWkwLMcg5nhayDTXc0C4HjgWrpxPAcCT2ta0e0Nur4k7wSeB/yE7tP/P7hl10q5Ocn6LGvh2ZaRpWsG4AgG3MpTVWcneRIDG4PXG3Lr3Tha7SQwmMAzkyG1HjuoX3cpMw1cTXLeUNb5mgP1vRb4b7qlJO4+db6qvtGsqDkkydPplgnZATiFbj/avavq1JZ1TUlyZlU9anTV8STnVtUjWtcGS8fgHQBsVVX7JNmObgJM0y5fmBMzaAc9aH7ohtB67KB+3SUkeV2S84Ht+4kQU39+CjR/wxx6fSNuA75Gt+7XwcDJwEEtC5pjXg58ATgE+ASwcChhrDf0Vp6P0bXkjI7B+8d25SzTd609CXgs8BrgIUMJY71BD5qfA5MijqB7v7t/f/wj4A1rsgBbyHSXkGRj4A+Af6ab5Tbluqr6dZuqlhl6fVP60Pgo4IyqekS/JtnBVfWixqXNCUmeSjdL+gl0rYznAt+oqvc2Law3B1p5FlXVwmkteN+rqocPoLbBtt7BnFhjbtBLmgyh9dgxZLpLqKpr6D7p79W6lpkMvb4RN1bVjUlIcveq+mGS7VsXNVdU1deSnEYXap8CvBZ4CN1g5uYGPkYLhj0Gb5AzaOfAoPkpQ58U0bz12EAmadTiJJsAnwW+nORq4IrGNc0ZSb4KbAB8G/gm8KiqurJtVcvM1MqTZDCtPMDb6LrLt0hyNP0YvKYVLTPUGbSDHjQ/onngmUXzGb52WUqaUd+SsjHwpapqNUNrTknybmAnuladbwHfAL5dVb9rWlgvyafoWnleXlUP7Vujvj2gQf1H0XW5/Q64BPhOVf2qbVWdJKfTbSn2rarasW+9+2RV7dy4tDlh6N3l0H6XDQOZJK1mSTakWwjzb4D7VtXdZ7nLGjHkMVow7DF4c2AGbfNZgrNpHXhWZAhjBA1kkrSaJNmPLkzsBFxG10L2zar6WtPCenOhladfrmF0DN7vqurBbasadusdzIlB880Dz4oMofXYMWSStPqsD/wbcFa/D+PQDHmM1tDH4H2MrvXu6fStd0kG0XrXG/qg+UFOihjRfIyggUySVpOqelfrGmYxtU7a8XStPH81pFYeujX5dqIbZ3QN8JskgxiDN/QZtAx/0HzzwDOL5jN8DWSSNH8MupWnqv4v3G4M3seA+zKya0QrA2+9gwHMEpxF88Azi+atx44hk6R5ZKhjtGDYY/CGPoMWBj9ofuiTIpqPETSQSdI8MUMrz/8MqZUnyRvpgs5Qx+ANeQbt0AfNNw88KzKEGb4GMkmaJ+ZCK89QDbn1DoYxS3BFhhB4ZtO69dhAJknzzFBbeYZs6K13Q19jDtoHnhUZQuuxg/olaZ6YoZXncLpfPprFHJhBO+hB83NgUkTzGb4GMkmaP4a+TpruvOazBGfRPPCsyBBm+NplKUnSHDf0QfNThtpdPoQxgraQSZI09w16jbk50F3evPXYFjJJku4CBj5oftCTIobAQCZJ0hw3hFmCWjVrtS5AkiStsvOAm+kGzT8MmFqLTHOELWSSJN1FDHXQvGbnoH5Jkua4OTBoXrMwkEmSNPc1nyWoVWOXpSRJUmMO6pckSWrMQCZJktSYgUzSnJPk1CTPmHbuDUn+YyUe46Qkm8xyzW+Xc/6IJHuM+1ySNBsDmaS56JPAntPO7dmfX6F01qqqP6uq30ykOklaSQYySXPR8cCzk9wdIMnWwP3p9u/7apKzk5yfZPep25P8oG9BOxvYIsmlSTbtb/9skrOSXJBk39EnSnJo/3hfTbJgeiFJdkpyWn//k5Pcb6LfuaS7JAOZpDmnqq4Cvgvs2p/aE/gU8DvguVW1I91+focmSX/N9sDHq+qRVXXZtId8VVXtBCwE9k/yh/35DYCz+8c7DXjb6J2S3A34d2CP/v6HA29fjd+qpHnCdcgkzVVT3Zaf6/9+FRDgn5I8Efg9sBlwn/76y6rqjOU81v5Jntt/vQWwHXBV/xif6s//F/Dpaffbnm6rmi/3uW9t4H9X7duSNB8ZyCTNVZ8F/i3JjsD6VXV2kr2BBcBOVXVLkkuB9frrr5/pQZI8GXga8JiquiHJqSP3mW76wo0BLqiqx6zKNyJJdllKmpOq6rfAqXTdhFOD+TcGruzD2FOArcZ4qI2Bq/sw9mBgl5Hb1gKmZlO+GPifafe9CFiQ5DHQdWEmecid+X4kzW+2kEmayz5J1404NePyaODEJIuAc4EfjvEYXwJem+Q8uoA12q15PfCQJGcB1wAvGr1jVd3cL3/xviQb072nvge44M5/S5LmI7dOkiRJaswuS0mSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKmx/w8uQIX9VcvZTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "x_values = list(range(len(importances)))\n",
    "sns.barplot(x_values, importances,orientation='vertical')\n",
    "plt.xticks(x_values, features_names, rotation='vertical')\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Variable')\n",
    "plt.title('Variable Importances')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}