x_CO2_y_Temperature_correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Data/combinedclimatedata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "      <th>population_millions</th>\n",
       "      <th>GMSL</th>\n",
       "      <th>CO2_concentration_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>15.466417</td>\n",
       "      <td>0.059167</td>\n",
       "      <td>5581597.598</td>\n",
       "      <td>90.471622</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1994</td>\n",
       "      <td>15.535000</td>\n",
       "      <td>0.058583</td>\n",
       "      <td>5663150.428</td>\n",
       "      <td>89.516216</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1995</td>\n",
       "      <td>15.637833</td>\n",
       "      <td>0.060417</td>\n",
       "      <td>5744212.930</td>\n",
       "      <td>87.334722</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1996</td>\n",
       "      <td>15.524667</td>\n",
       "      <td>0.059500</td>\n",
       "      <td>5824891.931</td>\n",
       "      <td>85.473243</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "      <td>15.713833</td>\n",
       "      <td>0.059167</td>\n",
       "      <td>5905045.647</td>\n",
       "      <td>99.442432</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year  LandAndOceanAverageTemperature  \\\n",
       "0           0  1993                       15.466417   \n",
       "1           1  1994                       15.535000   \n",
       "2           2  1995                       15.637833   \n",
       "3           3  1996                       15.524667   \n",
       "4           4  1997                       15.713833   \n",
       "\n",
       "   LandAndOceanAverageTemperatureUncertainty  population_millions       GMSL  \\\n",
       "0                                   0.059167          5581597.598  90.471622   \n",
       "1                                   0.058583          5663150.428  89.516216   \n",
       "2                                   0.060417          5744212.930  87.334722   \n",
       "3                                   0.059500          5824891.931  85.473243   \n",
       "4                                   0.059167          5905045.647  99.442432   \n",
       "\n",
       "   CO2_concentration_per_million  \n",
       "0                         357.10  \n",
       "1                         358.83  \n",
       "2                         360.82  \n",
       "3                         362.61  \n",
       "4                         363.73  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let X be CO2 ppm and y be global average yearly temperature, then Reshape X and y\n",
    "X = dataset['CO2_concentration_per_million'].values.reshape(-1,1)\n",
    "y = dataset['LandAndOceanAverageTemperature'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the train - test data to be 80% train, 20% test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the algorithm using a linear regression\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The y intercept is:   [12.36579572]\n",
      "The slope is:   [[0.00897431]]\n"
     ]
    }
   ],
   "source": [
    "#To retrieve the intercept:\n",
    "print(\"The y intercept is:  \", regressor.intercept_)\n",
    "#For retrieving the slope:\n",
    "print(\"The slope is:  \", regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.757250</td>\n",
       "      <td>15.753778</td>\n",
       "      <td>-0.003472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.826583</td>\n",
       "      <td>15.738343</td>\n",
       "      <td>-0.088241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.913000</td>\n",
       "      <td>15.943406</td>\n",
       "      <td>0.030406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.827333</td>\n",
       "      <td>15.810047</td>\n",
       "      <td>-0.017286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.854417</td>\n",
       "      <td>15.924290</td>\n",
       "      <td>0.069874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted     Error\n",
       "0  15.757250  15.753778 -0.003472\n",
       "1  15.826583  15.738343 -0.088241\n",
       "2  15.913000  15.943406  0.030406\n",
       "3  15.827333  15.810047 -0.017286\n",
       "4  15.854417  15.924290  0.069874"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare actual to predicted values\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten(), \"Error\": y_pred.flatten() - y_test.flatten()})\n",
    "# pd.DataFrame({\"Predicted\": predictions, \"Actual\": y, \"Error\": predictions - y})[[\"Predicted\", \"Actual\", \"Error\"]]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart to show comparison between actual and predicted\n",
    "df1 = df.head(23)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our straight line with the test data\n",
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.04185553049680735\n",
      "Mean Squared Error: 0.002780824359590753\n",
      "Root Mean Squared Error: 0.05273352216181613\n"
     ]
    }
   ],
   "source": [
    "# Calculate margin or error\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max X values (Population) 357.1 400.83\n"
     ]
    }
   ],
   "source": [
    "x_min = X.min()\n",
    "x_max = X.max()\n",
    "print(\"Min/Max X values (Population)\", x_min, x_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min/Max Y values (Temperature) 15.466416666666667 16.058583333333335\n"
     ]
    }
   ],
   "source": [
    "# Get y (temperature min and max actuals)\n",
    "y_min_actual = y.min()\n",
    "y_max_actual = y.max()\n",
    "print(\"Min/Max Y values (Temperature)\", y_min_actual, y_max_actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Min Value: 15.466416666666667\n",
      "Calculated Min Value: 15.570521821\n",
      "Actual Max Value: 16.058583333333335\n",
      "Calculated Max Value: 15.9629683973\n"
     ]
    }
   ],
   "source": [
    "#The y intercept is:   [12.36579572]\n",
    "#The slope is:   [[0.00897431]]\n",
    "y_min = 12.36579572 + 0.00897431 * x_min\n",
    "y_max = 12.36579572 + 0.00897431 * x_max\n",
    "print(f\"Actual Min Value: {y_min_actual}\")\n",
    "print(f\"Calculated Min Value: {y_min}\")\n",
    "print(f\"Actual Max Value: {y_max_actual}\")\n",
    "print(f\"Calculated Max Value: {y_max}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Min Value: 15.466416666666667\n",
      "Predicted Min Value: [[15.57052293]]\n",
      "Actual Max Value: 16.058583333333335\n",
      "Predicted Max Value: [[15.96296965]]\n"
     ]
    }
   ],
   "source": [
    "# Predict calculated values\n",
    "y_min_predicted = regressor.predict([[x_min]])\n",
    "y_max_predicted = regressor.predict([[x_max]])\n",
    "print(f\"Actual Min Value: {y_min_actual}\")\n",
    "print(f\"Predicted Min Value: {y_min_predicted}\")\n",
    "print(f\"Actual Max Value: {y_max_actual}\")\n",
    "print(f\"Predicted Max Value: {y_max_predicted}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "plt.scatter(X, y, c='blue')\n",
    "plt.plot([x_min, x_max], [y_min, y_max], c='red')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Global CO2 PPM\")\n",
    "plt.ylabel(\"Temperature (Celsius)\")\n",
    "plt.title(\"Global CO2 to Global Average Temperature\\n\")\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig('images/co2_to_temp.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plugging in the Numbers:\n",
    "#### Average Global Temperature = slope * CO2 + y-intercept\n",
    "#### slope = 0.00897431\n",
    "#### y-intercept =  12.36579572\n",
    "#### Global Temp. Above 1.5 degrees C from 1850 average global temperature = 16.37\n",
    "#### Population = (16.37 - 12.36579572)/0.00897431\n",
    "#### CO2 = 446.1851975\n",
    "#### Mean Squared Error: 0.002780824359590753"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "nbpresent": {
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