Coronavirus Data - Time Series Forecasting.ipynb

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   "source": [
    "## Forecasting Model for the COVID-19 Epidemic"
   ]
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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# Import Libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from fbprophet import Prophet\n",
    "#np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true
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   "source": [
    "summary = pd.read_csv(\"covid_19_clean_complete.csv\", parse_dates = ['Date']).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# Preparing Data\n",
    "time_series_data = summary[['Date', 'Confirmed']].groupby('Date', as_index = False).sum()\n",
    "time_series_data.columns = ['ds', 'y']\n",
    "time_series_data.ds = pd.to_datetime(time_series_data.ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "# Train-Test Split\n",
    "train_range = np.random.rand(len(time_series_data)) < 0.8\n",
    "train_ts = time_series_data[train_range]\n",
    "test_ts = time_series_data[~train_range]\n",
    "test_ts = test_ts.set_index('ds')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "text": [
      "INFO:numexpr.utils:NumExpr defaulting to 4 threads.\n",
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x233a9794e88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# Training Prophet Model\n",
    "prophet_model = Prophet()\n",
    "prophet_model.fit(train_ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": true
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   "source": [
    "# Testing Model\n",
    "future = pd.DataFrame(test_ts.index)\n",
    "predict = prophet_model.predict(future)\n",
    "forecast = predict[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]\n",
    "forecast = forecast.set_index('ds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "Mean Absolute Error: 24208.100621488473\n"
     ]
    }
   ],
   "source": [
    "# Mean Absolute Error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "mae = mean_absolute_error(test_ts.y.values, predict.yhat.values)\n",
    "print(\"Mean Absolute Error:\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9996744902533652\n"
     ]
    }
   ],
   "source": [
    "# R2 Score\n",
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(test_ts.y.values, predict.yhat.values)\n",
    "print(\"R2 Score:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Mean Squared Error: 2514388032.3959646\n"
     ]
    }
   ],
   "source": [
    "# Mean Squared Error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(test_ts.y.values, predict.yhat.values)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
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   ],
   "source": [
    "# Plotting Actual vs Predicted\n",
    "test_fig = go.Figure() \n",
    "test_fig.add_trace(go.Scatter(\n",
    "                x= test_ts.index,\n",
    "                y= test_ts.y,\n",
    "                name = \"Actual\",\n",
    "                mode = \"lines+markers\",\n",
    "                opacity = 1,\n",
    "                line=dict(color='blue', width=2, dash='dash')))\n",
    "test_fig.add_trace(go.Scatter(\n",
    "                x= forecast.index,\n",
    "                y= forecast.yhat,\n",
    "                name= \"Prediction\",\n",
    "                mode = 'lines',\n",
    "                opacity = 0.7,\n",
    "                line = dict(color='red', width=2)))\n",
    "test_fig.update_layout(title='Forecast Model on Test Data', xaxis_title=\"Date\", yaxis_title=\"Number of Confirmed Cases\",)\n",
    "\n",
    "test_fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x233ab34a708>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction using Forecasting Model\n",
    "prophet_model_full = Prophet()\n",
    "prophet_model_full.fit(time_series_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_kg_hide-input": true
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   "source": [
    "future_full = prophet_model_full.make_future_dataframe(periods=150)\n",
    "forecast_full = prophet_model_full.predict(future_full)\n",
    "forecast_full = forecast_full.set_index('ds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Forecasting Model\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Number of Confirmed Cases\"}}},\n",
       "                        {\"responsive\": true}\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Forecast\n",
    "prediction_fig = go.Figure() \n",
    "prediction_fig.add_trace(go.Scatter(\n",
    "                x= time_series_data.ds,\n",
    "                y= time_series_data.y,\n",
    "                name = \"Actual\",\n",
    "                opacity= 1,\n",
    "                line=dict(color='blue', width=3, dash='dash')))\n",
    "prediction_fig.add_trace(go.Scatter(\n",
    "                x= forecast_full.index,\n",
    "                y= forecast_full.yhat,\n",
    "                name = \"Prediction\",\n",
    "                mode = \"lines\",\n",
    "                opacity= 0.7,\n",
    "                line=dict(color='red', width=2)))\n",
    "prediction_fig.update_layout(title='Forecasting Model', xaxis_title=\"Date\", yaxis_title=\"Number of Confirmed Cases\")\n",
    "\n",
    "prediction_fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_fig.write_html('testing model.html')\n",
    "prediction_fig.write_html('prediction model.html')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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