03 Predicting Regional Deaths-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0124c7a1",
   "metadata": {},
   "source": [
    "# 0.0 Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0ddd25",
   "metadata": {},
   "source": [
    "With 628,035,553 worldwide cases reported, COVID-19 has placed global healthcare infrastructure under sustained pressure. Unanticipated case numbers overwhelmed existing provision, exacerbated by insufficient equipment to treat patients or medical supplies to protect staff. Such conditions can inadvertently lead to avoidable deaths, as seen in a study finding surges of critical patients in China resulted in up to 53.2% of patients not receiving needed ventilation due to resource limitations. These issues could be compounded due to the fragility of longer distance supply chains during the pandemic and in Indonesia specifically due to the dispersion of its population across 17,508 islands. Therefore, if anticipating COVID-19 deaths could reduce deaths through better healthcare planning, are we able to facilitate this by accurately forecast regional deaths from reported figures?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478ce3d8",
   "metadata": {},
   "source": [
    "# 1.0 Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa1e9813",
   "metadata": {},
   "source": [
    "### | 1.1 Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75700d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65cf1781",
   "metadata": {},
   "source": [
    "### | 1.2 Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "667e4dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculates and returns evaluation metrics\n",
    "def evaluate(model, X, y):  \n",
    "\n",
    "    methods = ['neg_mean_squared_error', 'neg_root_mean_squared_error', 'neg_median_absolute_error', 'r2']\n",
    "    scores = []\n",
    "\n",
    "    for method in methods:\n",
    "    \n",
    "        calculated = cross_val_score(model, X, y, cv=folds, scoring=method).mean()\n",
    "        scores.append(calculated)\n",
    "    \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc551e8e",
   "metadata": {},
   "source": [
    "### | 1.3 Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fc76efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# colors\n",
    "data_color = \"#313131\" # grey\n",
    "model_color = \"#00bbf9\" # blue\n",
    "background_color = \"#f5f5f5\" #light grey\n",
    "grid_color = \"#DADADA\" # mid grey\n",
    "\n",
    "# graphs\n",
    "graph_width = 18\n",
    "graph_height = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e3b1c5",
   "metadata": {},
   "source": [
    "### | 1.4 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34c8a4ce",
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>New Cases</th>\n",
       "      <th>New Deaths</th>\n",
       "      <th>New Recovered</th>\n",
       "      <th>New Active Cases</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>Total Active Cases</th>\n",
       "      <th>Increasing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Location  New Cases  New Deaths  New Recovered  \\\n",
       "0  2020-03-02  Indonesia        2.0         0.0            0.0   \n",
       "1  2020-03-03  Indonesia        0.0         0.0            0.0   \n",
       "2  2020-03-04  Indonesia        0.0         0.0            0.0   \n",
       "3  2020-03-05  Indonesia        0.0         0.0            0.0   \n",
       "4  2020-03-06  Indonesia        2.0         0.0            0.0   \n",
       "\n",
       "   New Active Cases  Total Cases  Total Deaths  Total Recovered  \\\n",
       "0                 2            2             0                0   \n",
       "1                 0            2             0                0   \n",
       "2                 0            2             0                0   \n",
       "3                 0            2             0                0   \n",
       "4                 2            4             0                0   \n",
       "\n",
       "   Total Active Cases  Increasing  \n",
       "0                   2           0  \n",
       "1                   2           0  \n",
       "2                   2           0  \n",
       "3                   2           0  \n",
       "4                   4           0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/national_cleaned.csv\", index_col=0)\n",
    "\n",
    "# check data is loaded as expected\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e1bb5ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(642, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of columns and rows matches the source\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d06109",
   "metadata": {},
   "source": [
    "# 3.0 Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a41106e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display settings for the plot\n",
    "plt.figure(figsize=(graph_width,graph_height))\n",
    "ax = plt.axes()\n",
    "ax.set_title(\"Test Values / Predicted Values Correlation\",fontweight='bold')\n",
    "\n",
    "# plot heatmap\n",
    "features = sns.heatmap(data.corr(), annot=True, cmap=sns.light_palette(model_color, as_cmap=True));\n",
    "\n",
    "# adds border to heatmap\n",
    "for _, spine in features.spines.items():\n",
    "    spine.set_visible(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "445dba0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove target attributes categorical attributes and attributes with weak or no correlation\n",
    "filtered = data.drop([\"Date\", \"Location\", \"New Cases\", \"New Deaths\", \"New Recovered\", \"New Active Cases\", \"Total Recovered\", \"Total Active Cases\", \"Increasing\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "480ac534",
   "metadata": {},
   "source": [
    "# 4.0 Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b66c743",
   "metadata": {},
   "source": [
    "### | 4.1 Transforming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d855d1a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display settings for the plot\n",
    "fig, afiltered = plt.subplots(1,2)\n",
    "fig.set_figwidth(graph_width)\n",
    "fig.set_figheight(graph_height)\n",
    "\n",
    "\n",
    "k=0\n",
    "for i in range(2):\n",
    "\n",
    "    afiltered[i].set_title(filtered.columns[k],fontweight='bold')\n",
    "    afiltered[i].hist(filtered[filtered.columns[k]], color=model_color)\n",
    "    \n",
    "    afiltered[i].set_facecolor(background_color)\n",
    "    afiltered[i].grid(color=grid_color)\n",
    "        \n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "60108fbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Neil\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2612: UserWarning: n_quantiles (1000) is greater than the total number of samples (642). n_quantiles is set to n_samples.\n",
      "  warnings.warn(\"n_quantiles (%s) is greater than the total number \"\n"
     ]
    }
   ],
   "source": [
    "transformer = QuantileTransformer(output_distribution='normal')\n",
    "\n",
    "filtered = transformer.fit_transform(filtered)\n",
    "\n",
    "filtered = pd.DataFrame(filtered, columns=['Total Cases','Total Deaths'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ca8d2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display settings for the plot\n",
    "fig, afiltered = plt.subplots(1,2)\n",
    "fig.set_figwidth(graph_width)\n",
    "fig.set_figheight(graph_height)\n",
    "\n",
    "\n",
    "k=0\n",
    "for i in range(2):\n",
    "\n",
    "    afiltered[i].set_title(filtered.columns[k],fontweight='bold')\n",
    "    afiltered[i].hist(filtered[filtered.columns[k]], color=model_color)\n",
    "    \n",
    "    afiltered[i].set_facecolor(background_color)\n",
    "    afiltered[i].grid(color=grid_color)\n",
    "        \n",
    "    k+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30673845",
   "metadata": {},
   "source": [
    "### | 4.2 Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d847b813",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "\n",
    "# scaling filtered dataframe data due to distance based SVR being used\n",
    "filtered = sc.fit_transform(filtered)\n",
    "\n",
    "# reform dataframe with scaled values\n",
    "filtered = pd.DataFrame(filtered, columns=['Total Cases','Total Deaths'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c700594",
   "metadata": {},
   "source": [
    "### | 4.3 Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e43aadc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove target attributes categorical attributes and attributes with weak or no correlation\n",
    "X = filtered[\"Total Cases\"].values.reshape(-1, 1) \n",
    "\n",
    "# target attribute\n",
    "y = filtered['Total Deaths'].values.reshape(-1, 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163a619d",
   "metadata": {},
   "source": [
    "### | 4.3 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "46ae71a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of folds for cross validation\n",
    "folds = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bf3aef",
   "metadata": {},
   "source": [
    "#  5.0 Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff527f6",
   "metadata": {},
   "source": [
    "### | 5.1 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3c892516",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model and use cross validation data\n",
    "lnr = LinearRegression()\n",
    "lnr_predictions = cross_val_predict(lnr, X, y, cv=folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38975ba8",
   "metadata": {},
   "source": [
    "### | 5.2 Test / Prediction Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6fe08189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display settings for the plot\n",
    "plt.figure(figsize=(graph_width,graph_height))\n",
    "ax = plt.axes()\n",
    "ax.axes.set_facecolor(background_color)\n",
    "ax.grid(color=grid_color, linestyle='-', axis='x')\n",
    "ax.set_title(\"Linear Regression: Test Values / Predicted Values Correlation\",fontweight='bold')\n",
    "ax.set(xlabel='Test', ylabel='Prediction')\n",
    "\n",
    "# plots the test and prediction values\n",
    "plt.plot(y,lnr_predictions,'.',label = 'Values', color=data_color)\n",
    "\n",
    "# plot y = x line\n",
    "x_line = np.linspace(0,500)\n",
    "y_line = x_line\n",
    "plt.plot(x_line,y_line,'-',label = 'y=x line', color=model_color)\n",
    "\n",
    "# legend location and appearance\n",
    "plt.legend(loc='upper left', frameon=False);\n",
    "\n",
    "# puts 0,0 in bottom left\n",
    "plt.xlim([0, max(y)+0.5])\n",
    "plt.ylim([0, max(y)+0.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d89f15d",
   "metadata": {},
   "source": [
    "### | 5.3 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4c93d118",
   "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>Model</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>-0.049057</td>\n",
       "      <td>-0.102871</td>\n",
       "      <td>-0.032553</td>\n",
       "      <td>0.804703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model       MSE      RMSE       MAE        R2\n",
       "0  Linear Regression -0.049057 -0.102871 -0.032553  0.804703"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'MAE', 'R2'])\n",
    "\n",
    "lnr_scores = evaluate(lnr, X, y)\n",
    "\n",
    "results.loc[len(results.index)] = [\"Linear Regression\", lnr_scores[0], lnr_scores[1], lnr_scores[2], lnr_scores[3]]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff8bc4e",
   "metadata": {},
   "source": [
    "# 6.0 SVR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a222ddf",
   "metadata": {},
   "source": [
    "### | 6.1 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ccc34a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        1.91692091e+00,  1.94757663e+00,  1.98048954e+00,  2.01606690e+00,\n",
       "        2.05484037e+00,  2.09752335e+00,  2.14510577e+00,  2.19902105e+00,\n",
       "        2.26146296e+00,  2.33605228e+00,  2.42945659e+00,  2.55630958e+00,\n",
       "        2.76180562e+00,  4.85030582e+00])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X.ravel()\n",
    "y=y.ravel().reshape(-1,1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "997ad389",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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",
      "C:\\Users\\Neil\\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"
     ]
    }
   ],
   "source": [
    "svr = SVR(C=1, kernel='linear')\n",
    "svr_predictions = cross_val_predict(svr, X, y, cv=folds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d836d6",
   "metadata": {},
   "source": [
    "### | 6.2 Test / Prediction Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37baeaff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display settings for the plot\n",
    "plt.figure(figsize=(graph_width,graph_height))\n",
    "ax = plt.axes()\n",
    "ax.axes.set_facecolor(background_color)\n",
    "ax.grid(color=grid_color, linestyle='-', axis='x')\n",
    "ax.set_title(\"SVR Regression: Test Values / Predicted Values Correlation\",fontweight='bold')\n",
    "ax.set(xlabel='Test', ylabel='Prediction')\n",
    "\n",
    "# plots the test and prediction values\n",
    "plt.plot(y,svr_predictions,'.',label = 'Values', color=data_color)\n",
    "\n",
    "# plot y = x line\n",
    "x_line = np.linspace(0,500)\n",
    "y_line = x_line\n",
    "plt.plot(x_line,y_line,'-',label = 'y=x line', color=model_color)\n",
    "\n",
    "# legend location and appearance\n",
    "plt.legend(loc='upper left', frameon=False);\n",
    "\n",
    "# puts 0,0 in bottom left\n",
    "plt.xlim([0, max(y)+0.5])\n",
    "plt.ylim([0, max(y)+0.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c8bd65",
   "metadata": {},
   "source": [
    "### | 6.3 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b75b8377",
   "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>Model</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVR</td>\n",
       "      <td>-0.949168</td>\n",
       "      <td>-0.973929</td>\n",
       "      <td>-0.443309</td>\n",
       "      <td>0.050891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model       MSE      RMSE       MAE        R2\n",
       "1   SVR -0.949168 -0.973929 -0.443309  0.050891"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_scores = evaluate(svr, X, y)\n",
    "\n",
    "results.loc[len(results.index)] = [\"SVR\", svr_scores[0], svr_scores[1], svr_scores[2], svr_scores[3]]\n",
    "\n",
    "results[results['Model'] == \"SVR\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72e32a6",
   "metadata": {},
   "source": [
    "#  7.0 Evaluation Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad76f1c5",
   "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>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>-0.953267</td>\n",
       "      <td>-0.975156</td>\n",
       "      <td>-0.456212</td>\n",
       "      <td>0.048085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>-0.949168</td>\n",
       "      <td>-0.973929</td>\n",
       "      <td>-0.443309</td>\n",
       "      <td>0.050891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        MSE      RMSE       MAE        R2\n",
       "Linear Regression -0.953267 -0.975156 -0.456212  0.048085\n",
       "SVR               -0.949168 -0.973929 -0.443309  0.050891"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.set_index('Model').rename_axis(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "147aac07",
   "metadata": {},
   "source": [
    "# 8.0 Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "674400f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({'Linear Real':y, 'Linear Predicted':lnr_predictions, 'SVR Real':y, 'SVR Predicted':svr_predictions})\n",
    "\n",
    "# plot values using a step for clarity\n",
    "step=500\n",
    "\n",
    "# display settings for the plot\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "fig.set_figwidth(graph_width)\n",
    "fig.set_figheight(graph_height)\n",
    "\n",
    "# plots the linear regression graph real and prediction values\n",
    "ax[0].set_title(\"Linear Regression\",fontweight='bold')\n",
    "ax[0].plot(predictions['Linear Real'].values[::step], label = 'Real Values', color=data_color)\n",
    "ax[0].plot(predictions['Linear Predicted'].values[::step], label = 'Predicted Values', color=model_color)\n",
    "\n",
    "# plots the svr real and prediction values\n",
    "ax[1].set_title(\"SVR\",fontweight='bold')\n",
    "ax[1].plot(predictions['SVR Real'].values[::step], label = 'Real Values', color=data_color)\n",
    "ax[1].plot(predictions['SVR Predicted'].values[::step], label = 'Predicted Values', color=model_color)\n",
    "ax[1].set(xlabel='Test', ylabel='Prediction')\n",
    "\n",
    "# sub plot appearance settings\n",
    "for i in range(2):\n",
    "    ax[i].set_facecolor(background_color)\n",
    "    ax[i].grid(color=grid_color, linestyle='-', linewidth=1, axis='x')\n",
    "    ax[i].legend(frameon=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "756d76f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f51f6ddf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": 5
}