Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
       "        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>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",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>41</td>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd# Read in data and display first 5 rows\n",
    "features = pd.read_csv('temps.csv')\n",
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of our features is: (348, 12)\n"
     ]
    }
   ],
   "source": [
    "print('The shape of our features is:', features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "        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>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>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>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",
       "      <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>57.238506</td>\n",
       "      <td>62.373563</td>\n",
       "      <td>59.772989</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>10.605746</td>\n",
       "      <td>10.549381</td>\n",
       "      <td>10.705256</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>41.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>44.000000</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>48.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>50.000000</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>56.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>58.000000</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>66.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>69.000000</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>77.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>79.000000</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  forecast_noaa  forecast_acc  forecast_under      friend  \n",
       "count  348.000000     348.000000    348.000000      348.000000  348.000000  \n",
       "mean    62.543103      57.238506     62.373563       59.772989   60.034483  \n",
       "std     11.794146      10.605746     10.549381       10.705256   15.626179  \n",
       "min     35.000000      41.000000     46.000000       44.000000   28.000000  \n",
       "25%     54.000000      48.000000     53.000000       50.000000   47.750000  \n",
       "50%     62.500000      56.000000     61.000000       58.000000   60.000000  \n",
       "75%     71.000000      66.000000     72.000000       69.000000   71.000000  \n",
       "max     92.000000      77.000000     82.000000       79.000000   95.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "        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>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",
       "      <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>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>29</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>61</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <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>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>46</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>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</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": [
       "   average  actual  forecast_noaa  forecast_acc  forecast_under  friend  \\\n",
       "0     45.6      45             43            50              44      29   \n",
       "1     45.7      44             41            50              44      61   \n",
       "2     45.8      41             43            46              47      56   \n",
       "3     45.9      40             44            48              46      53   \n",
       "4     46.0      44             46            46              46      41   \n",
       "\n",
       "   week_Fri  week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
       "0         1         0         0         0           0          0         0  \n",
       "1         0         0         1         0           0          0         0  \n",
       "2         0         0         0         1           0          0         0  \n",
       "3         0         1         0         0           0          0         0  \n",
       "4         0         0         0         0           0          1         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encode the data using pandas get_dummies\n",
    "features = pd.get_dummies(features)# Display the first 5 rows of the last 12 columns\n",
    "features.iloc[:,5:].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use numpy to convert to arrays\n",
    "import numpy as np# Labels are the values we want to predict\n",
    "labels = np.array(features['actual'])# Remove the labels from the features\n",
    "# axis 1 refers to the columns\n",
    "features= features.drop('actual', axis = 1)# Saving feature names for later use\n",
    "feature_list = list(features.columns)# Convert to numpy array\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Skicit-learn to split data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split# Split the data into training and testing sets\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape: (261, 17)\n",
      "Training Labels Shape: (261,)\n",
      "Testing Features Shape: (87, 17)\n",
      "Testing Labels Shape: (87,)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features Shape:', train_features.shape)\n",
    "print('Training Labels Shape:', train_labels.shape)\n",
    "print('Testing Features Shape:', test_features.shape)\n",
    "print('Testing Labels Shape:', test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average baseline error:  5.06\n"
     ]
    }
   ],
   "source": [
    "# The baseline predictions are the historical averages\n",
    "baseline_preds = test_features[:, feature_list.index('average')]# Baseline errors, and display average baseline error\n",
    "baseline_errors = abs(baseline_preds - test_labels)\n",
    "print('Average baseline error: ', round(np.mean(baseline_errors), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor# Instantiate model with 1000 decision trees\n",
    "rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)# Train the model on training data\n",
    "rf.fit(train_features, train_labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.87 degrees.\n"
     ]
    }
   ],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions = rf.predict(test_features)# Calculate the absolute errors\n",
    "errors = abs(predictions - test_labels)# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.93 %.\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean absolute percentage error (MAPE)\n",
    "mape = 100 * (errors / test_labels)# Calculate and display accuracy\n",
    "accuracy = 100 - np.mean(mape)\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: temp_1               Importance: 0.66\n",
      "Variable: average              Importance: 0.15\n",
      "Variable: forecast_noaa        Importance: 0.05\n",
      "Variable: forecast_acc         Importance: 0.03\n",
      "Variable: day                  Importance: 0.02\n",
      "Variable: temp_2               Importance: 0.02\n",
      "Variable: forecast_under       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"
     ]
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances = list(rf.feature_importances_)# List of tuples with variable and importance\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)# Print out the feature and importances \n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.92 degrees.\n",
      "Accuracy: 93.76 %.\n"
     ]
    }
   ],
   "source": [
    "# New random forest with only the two most important variables\n",
    "rf_most_important = RandomForestRegressor(n_estimators= 1000, random_state=42)# Extract the two most important features\n",
    "important_indices = [feature_list.index('temp_1'), feature_list.index('average')]\n",
    "train_important = train_features[:, important_indices]\n",
    "test_important = test_features[:, important_indices]# Train the random forest\n",
    "rf_most_important.fit(train_important, train_labels)# Make predictions and determine the error\n",
    "predictions = rf_most_important.predict(test_important)\n",
    "errors = abs(predictions - test_labels)# Display the performance metrics\n",
    "print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')\n",
    "mape = np.mean(100 * (errors / test_labels))\n",
    "accuracy = 100 - mape\n",
    "print('Accuracy:', round(accuracy, 2), '%.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "# Import matplotlib for plotting and use magic command for Jupyter Notebooks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "x_values = list(range(len(importances)))# Make a bar chart\n",
    "plt.bar(x_values, importances, orientation = 'vertical')# Tick labels for x axis\n",
    "plt.xticks(x_values, feature_list, rotation='vertical')# Axis labels and title\n",
    "plt.ylabel('Importance');\n",
    "plt.xlabel('Variable');\n",
    "plt.title('Variable Importances');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kei0s/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "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": [
    "# Use datetime for creating date objects for plotting\n",
    "import datetime# Dates of training values\n",
    "months = features[:, feature_list.index('month')]\n",
    "days = features[:, feature_list.index('day')]\n",
    "years = features[:, feature_list.index('year')]# List and then convert to datetime object\n",
    "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]# Dataframe with true values and dates\n",
    "true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})# Dates of predictions\n",
    "months = test_features[:, feature_list.index('month')]\n",
    "days = test_features[:, feature_list.index('day')]\n",
    "years = test_features[:, feature_list.index('year')]# Column of dates\n",
    "test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]# Convert to datetime objects\n",
    "test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]# Dataframe with predictions and dates\n",
    "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions})# Plot the actual values\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')# Plot the predicted values\n",
    "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')\n",
    "plt.xticks(rotation = '60'); \n",
    "plt.legend()# Graph labels\n",
    "plt.xlabel('Date');\n",
    "plt.ylabel('Maximum Temperature (F)');\n",
    "plt.title('Actual and Predicted Values');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "# Make the data accessible for plotting\n",
    "true_data['temp_1'] = features[:, feature_list.index('temp_1')]\n",
    "true_data['average'] = features[:, feature_list.index('average')]\n",
    "true_data['friend'] = features[:, feature_list.index('friend')]# Plot all the data as lines\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label  = 'actual', alpha = 1.0)\n",
    "plt.plot(true_data['date'], true_data['temp_1'], 'y-', label  = 'temp_1', alpha = 1.0)\n",
    "plt.plot(true_data['date'], true_data['average'], 'k-', label = 'average', alpha = 0.8)\n",
    "plt.plot(true_data['date'], true_data['friend'], 'r-', label = 'friend', alpha = 0.3)# Formatting plot\n",
    "plt.legend(); \n",
    "plt.xticks(rotation = '60');# Lables and title\n",
    "plt.xlabel('Date'); \n",
    "plt.ylabel('Maximum Temperature (F)');\n",
    "plt.title('Actual Max Temp and Variables');"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}