FinalProject.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"H2LQqrGhIe2I"},"source":["You can also try this: Go to the path where Python is installed in your system. For me it was something like C:\\Users\\\\Local Settings\\Application Data\\Programs\\Python\\Python37 In this folder, you'll find a python executable. Just create a duplicate and rename it to python3. Works every time."]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1674961551533,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"W2aowGkRIe2R","outputId":"958c1c2b-9bea-48a3-d934-6820105fc261"},"outputs":[{"name":"stdout","output_type":"stream","text":["['c:\\\\Users\\\\ailaty\\\\Downloads\\\\CS 631 Introduction to Big Data\\\\Final Project', 'c:\\\\Users\\\\ailaty\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\python310.zip', 'c:\\\\Users\\\\ailaty\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\DLLs', 'c:\\\\Users\\\\ailaty\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib', 'c:\\\\Users\\\\ailaty\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310', '', 'C:\\\\Users\\\\ailaty\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages', 'C:\\\\Users\\\\ailaty\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\win32', 'C:\\\\Users\\\\ailaty\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Users\\\\ailaty\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\Pythonwin', 'c:\\\\Users\\\\ailaty\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages']\n"]}],"source":["import sys\n","print(sys.path)"]},{"cell_type":"markdown","metadata":{"id":"KI5rDFwNIe2V"},"source":["Tensorflow installation: https://www.tensorflow.org/install/pip"]},{"cell_type":"markdown","metadata":{"id":"MFjzAx6mIe2W"},"source":["If you get the error on path(dictionary): https://stackoverflow.com/questions/37400974/error-unicode-error-unicodeescape-codec-cant-decode-bytes-in-position-2-3"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":2820,"status":"ok","timestamp":1675026025049,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"UA-GtvX6Ie2X"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\fuzzywuzzy\\fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n","  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"]}],"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import os\n","import seaborn as sns\n","import numpy as np\n","import datetime\n","# for Box-Cox Transformation\n","from scipy import stats\n","# for min_max scaling\n","from mlxtend.preprocessing import minmax_scaling\n","# helpful character encoding module\n","import chardet\n","# incosistant data entry\n","import fuzzywuzzy\n","from fuzzywuzzy import process\n","from matplotlib import pyplot \n","from sklearn.metrics import mean_squared_error \n","from io import BytesIO\n","import requests"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":53708,"status":"ok","timestamp":1675026085754,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"vDDJJPhWManN","outputId":"e6d6b414-580d-4dd8-f587-28a19c0ef1c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["# from google.colab import drive\n","# drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1674961555438,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"0BLblQ8fIe2Y"},"outputs":[],"source":["# # specify the directory containing the csv files\n","# directory = '/content/drive/My Drive/data/your_file.csv'\n","\n","# # list all csv files in the directory\n","# csv_files = [f for f in os.listdir(directory) if f.endswith('.csv')]\n","\n","# # combine all csv files into one dataframe\n","# df = pd.concat([pd.read_csv(os.path.join(directory, csv_file)) for csv_file in csv_files])\n","\n","# # write the combined dataframe to a new csv file\n","# df.to_csv(\"combined_csv.csv\", index=False)\n"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":1207,"status":"ok","timestamp":1675026098382,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"nX_fhHSAIe2a"},"outputs":[],"source":["# data = pd.read_csv(\"/content/drive/My Drive/CS 631 Introduction to Big Data/New folder/combined_csv.csv\")"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["data = pd.read_csv(\"combined_csv.csv\")\n","np.random.seed(0) "]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["np.random.seed(0) is used to set the random seed of NumPy, which is a library in Python for the manipulation of arrays. By setting the seed, we can ensure that the results of the random number generator are reproducible, i.e., they will always be the same each time the program is run with the same seed. In this line of code, the seed value 0 is being used, which means that every time the program is run with this seed, the same set of random numbers will be generated. This is useful for debugging or for testing purposes, as it allows us to isolate the impact of other parts of the program from the random number generator."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":473,"status":"ok","timestamp":1675026105964,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"Trk9N1FzVA6k","outputId":"c911e305-86d2-485a-94e9-bcc8e225a2a1"},"outputs":[{"data":{"text/plain":["(59120, 21)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"executionInfo":{"elapsed":98,"status":"ok","timestamp":1674961556989,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"e7hjlaLUIe2b","outputId":"02354462-b7e3-4652-8dd6-4ee13a03a4d0"},"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>Province_State</th>\n","      <th>Country_Region</th>\n","      <th>Last_Update</th>\n","      <th>Lat</th>\n","      <th>Long_</th>\n","      <th>Confirmed</th>\n","      <th>Deaths</th>\n","      <th>Recovered</th>\n","      <th>Active</th>\n","      <th>FIPS</th>\n","      <th>...</th>\n","      <th>Total_Test_Results</th>\n","      <th>People_Hospitalized</th>\n","      <th>Case_Fatality_Ratio</th>\n","      <th>UID</th>\n","      <th>ISO3</th>\n","      <th>Testing_Rate</th>\n","      <th>Hospitalization_Rate</th>\n","      <th>Date</th>\n","      <th>People_Tested</th>\n","      <th>Mortality_Rate</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alabama</td>\n","      <td>US</td>\n","      <td>2021-01-02 05:30:44</td>\n","      <td>32.3182</td>\n","      <td>-86.9023</td>\n","      <td>365747</td>\n","      <td>4872</td>\n","      <td>202137.0</td>\n","      <td>158738.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.332068</td>\n","      <td>84000001.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2021-01-01</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Alaska</td>\n","      <td>US</td>\n","      <td>2021-01-02 05:30:44</td>\n","      <td>61.3707</td>\n","      <td>-152.4044</td>\n","      <td>47019</td>\n","      <td>206</td>\n","      <td>7165.0</td>\n","      <td>39648.0</td>\n","      <td>2.0</td>\n","      <td>...</td>\n","      <td>1275750.0</td>\n","      <td>NaN</td>\n","      <td>0.438121</td>\n","      <td>84000002.0</td>\n","      <td>USA</td>\n","      <td>174391.185778</td>\n","      <td>NaN</td>\n","      <td>2021-01-01</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>American Samoa</td>\n","      <td>US</td>\n","      <td>2021-01-02 05:30:44</td>\n","      <td>-14.2710</td>\n","      <td>-170.1320</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>60.0</td>\n","      <td>...</td>\n","      <td>2140.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>16.0</td>\n","      <td>ASM</td>\n","      <td>3846.084722</td>\n","      <td>NaN</td>\n","      <td>2021-01-01</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Arizona</td>\n","      <td>US</td>\n","      <td>2021-01-02 05:30:44</td>\n","      <td>33.7298</td>\n","      <td>-111.4312</td>\n","      <td>530267</td>\n","      <td>9015</td>\n","      <td>76934.0</td>\n","      <td>444318.0</td>\n","      <td>4.0</td>\n","      <td>...</td>\n","      <td>5155330.0</td>\n","      <td>NaN</td>\n","      <td>1.700087</td>\n","      <td>84000004.0</td>\n","      <td>USA</td>\n","      <td>39551.860582</td>\n","      <td>NaN</td>\n","      <td>2021-01-01</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Arkansas</td>\n","      <td>US</td>\n","      <td>2021-01-02 05:30:44</td>\n","      <td>34.9697</td>\n","      <td>-92.3731</td>\n","      <td>229442</td>\n","      <td>3711</td>\n","      <td>199247.0</td>\n","      <td>26484.0</td>\n","      <td>5.0</td>\n","      <td>...</td>\n","      <td>2051488.0</td>\n","      <td>NaN</td>\n","      <td>1.617402</td>\n","      <td>84000005.0</td>\n","      <td>USA</td>\n","      <td>67979.497674</td>\n","      <td>NaN</td>\n","      <td>2021-01-01</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 21 columns</p>\n","</div>"],"text/plain":["   Province_State Country_Region          Last_Update      Lat     Long_  \\\n","0         Alabama             US  2021-01-02 05:30:44  32.3182  -86.9023   \n","1          Alaska             US  2021-01-02 05:30:44  61.3707 -152.4044   \n","2  American Samoa             US  2021-01-02 05:30:44 -14.2710 -170.1320   \n","3         Arizona             US  2021-01-02 05:30:44  33.7298 -111.4312   \n","4        Arkansas             US  2021-01-02 05:30:44  34.9697  -92.3731   \n","\n","   Confirmed  Deaths  Recovered    Active  FIPS  ...  Total_Test_Results  \\\n","0     365747    4872   202137.0  158738.0   1.0  ...                 NaN   \n","1      47019     206     7165.0   39648.0   2.0  ...           1275750.0   \n","2          0       0        NaN       NaN  60.0  ...              2140.0   \n","3     530267    9015    76934.0  444318.0   4.0  ...           5155330.0   \n","4     229442    3711   199247.0   26484.0   5.0  ...           2051488.0   \n","\n","   People_Hospitalized  Case_Fatality_Ratio         UID  ISO3   Testing_Rate  \\\n","0                  NaN             1.332068  84000001.0   USA            NaN   \n","1                  NaN             0.438121  84000002.0   USA  174391.185778   \n","2                  NaN                  NaN        16.0   ASM    3846.084722   \n","3                  NaN             1.700087  84000004.0   USA   39551.860582   \n","4                  NaN             1.617402  84000005.0   USA   67979.497674   \n","\n","   Hospitalization_Rate        Date People_Tested  Mortality_Rate  \n","0                   NaN  2021-01-01           NaN             NaN  \n","1                   NaN  2021-01-01           NaN             NaN  \n","2                   NaN  2021-01-01           NaN             NaN  \n","3                   NaN  2021-01-01           NaN             NaN  \n","4                   NaN  2021-01-01           NaN             NaN  \n","\n","[5 rows x 21 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["data.head()"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"executionInfo":{"elapsed":92,"status":"ok","timestamp":1674961556990,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"r-Wkd3h_Ie2c","outputId":"12989d8b-da6e-43e6-b3a6-841852fd0b23"},"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>Province_State</th>\n","      <th>Country_Region</th>\n","      <th>Last_Update</th>\n","      <th>Lat</th>\n","      <th>Long_</th>\n","      <th>Confirmed</th>\n","      <th>Deaths</th>\n","      <th>Recovered</th>\n","      <th>Active</th>\n","      <th>FIPS</th>\n","      <th>...</th>\n","      <th>Total_Test_Results</th>\n","      <th>People_Hospitalized</th>\n","      <th>Case_Fatality_Ratio</th>\n","      <th>UID</th>\n","      <th>ISO3</th>\n","      <th>Testing_Rate</th>\n","      <th>Hospitalization_Rate</th>\n","      <th>Date</th>\n","      <th>People_Tested</th>\n","      <th>Mortality_Rate</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>59115</th>\n","      <td>Virginia</td>\n","      <td>US</td>\n","      <td>2023-01-01 04:31:23</td>\n","      <td>37.7693</td>\n","      <td>-78.1700</td>\n","      <td>2199302</td>\n","      <td>22670</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>51.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.030782</td>\n","      <td>84000051.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>59116</th>\n","      <td>Washington</td>\n","      <td>US</td>\n","      <td>2023-01-01 04:31:23</td>\n","      <td>47.4009</td>\n","      <td>-121.4905</td>\n","      <td>1883676</td>\n","      <td>15038</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>53.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.798333</td>\n","      <td>84000053.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>59117</th>\n","      <td>West Virginia</td>\n","      <td>US</td>\n","      <td>2023-01-01 04:31:23</td>\n","      <td>38.4912</td>\n","      <td>-80.9545</td>\n","      <td>624721</td>\n","      <td>7672</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>54.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.228068</td>\n","      <td>84000054.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>59118</th>\n","      <td>Wisconsin</td>\n","      <td>US</td>\n","      <td>2023-01-01 04:31:23</td>\n","      <td>44.2685</td>\n","      <td>-89.6165</td>\n","      <td>1960878</td>\n","      <td>15802</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>55.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.805863</td>\n","      <td>84000055.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>59119</th>\n","      <td>Wyoming</td>\n","      <td>US</td>\n","      <td>2023-01-01 04:31:23</td>\n","      <td>42.7560</td>\n","      <td>-107.3025</td>\n","      <td>182847</td>\n","      <td>1958</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>56.0</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.070841</td>\n","      <td>84000056.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 21 columns</p>\n","</div>"],"text/plain":["      Province_State Country_Region          Last_Update      Lat     Long_  \\\n","59115       Virginia             US  2023-01-01 04:31:23  37.7693  -78.1700   \n","59116     Washington             US  2023-01-01 04:31:23  47.4009 -121.4905   \n","59117  West Virginia             US  2023-01-01 04:31:23  38.4912  -80.9545   \n","59118      Wisconsin             US  2023-01-01 04:31:23  44.2685  -89.6165   \n","59119        Wyoming             US  2023-01-01 04:31:23  42.7560 -107.3025   \n","\n","       Confirmed  Deaths  Recovered  Active  FIPS  ...  Total_Test_Results  \\\n","59115    2199302   22670        NaN     NaN  51.0  ...                 NaN   \n","59116    1883676   15038        NaN     NaN  53.0  ...                 NaN   \n","59117     624721    7672        NaN     NaN  54.0  ...                 NaN   \n","59118    1960878   15802        NaN     NaN  55.0  ...                 NaN   \n","59119     182847    1958        NaN     NaN  56.0  ...                 NaN   \n","\n","       People_Hospitalized  Case_Fatality_Ratio         UID  ISO3  \\\n","59115                  NaN             1.030782  84000051.0   USA   \n","59116                  NaN             0.798333  84000053.0   USA   \n","59117                  NaN             1.228068  84000054.0   USA   \n","59118                  NaN             0.805863  84000055.0   USA   \n","59119                  NaN             1.070841  84000056.0   USA   \n","\n","      Testing_Rate  Hospitalization_Rate  Date People_Tested  Mortality_Rate  \n","59115          NaN                   NaN   NaN           NaN             NaN  \n","59116          NaN                   NaN   NaN           NaN             NaN  \n","59117          NaN                   NaN   NaN           NaN             NaN  \n","59118          NaN                   NaN   NaN           NaN             NaN  \n","59119          NaN                   NaN   NaN           NaN             NaN  \n","\n","[5 rows x 21 columns]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["data.tail()"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":91,"status":"ok","timestamp":1674961556992,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"NirmYeL3Ie2d","outputId":"c88d67db-c998-4043-e58c-c63121a9be01"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 59120 entries, 0 to 59119\n","Data columns (total 21 columns):\n"," #   Column                Non-Null Count  Dtype  \n","---  ------                --------------  -----  \n"," 0   Province_State        59120 non-null  object \n"," 1   Country_Region        59120 non-null  object \n"," 2   Last_Update           59101 non-null  object \n"," 3   Lat                   57064 non-null  float64\n"," 4   Long_                 57064 non-null  float64\n"," 5   Confirmed             59120 non-null  int64  \n"," 6   Deaths                59120 non-null  int64  \n"," 7   Recovered             15122 non-null  float64\n"," 8   Active                15122 non-null  float64\n"," 9   FIPS                  59101 non-null  float64\n"," 10  Incident_Rate         57064 non-null  float64\n"," 11  Total_Test_Results    36637 non-null  float64\n"," 12  People_Hospitalized   5129 non-null   float64\n"," 13  Case_Fatality_Ratio   46533 non-null  float64\n"," 14  UID                   59120 non-null  float64\n"," 15  ISO3                  59120 non-null  object \n"," 16  Testing_Rate          45921 non-null  float64\n"," 17  Hospitalization_Rate  5129 non-null   float64\n"," 18  Date                  51754 non-null  object \n"," 19  People_Tested         11816 non-null  float64\n"," 20  Mortality_Rate        12027 non-null  float64\n","dtypes: float64(14), int64(2), object(5)\n","memory usage: 9.5+ MB\n"]}],"source":["data.info()"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":82,"status":"ok","timestamp":1674961556994,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"1YE_eI_GIe2e","outputId":"3029b306-c637-422d-e03e-a349184535e3"},"outputs":[{"data":{"text/plain":["Province_State           object\n","Country_Region           object\n","Last_Update              object\n","Lat                     float64\n","Long_                   float64\n","Confirmed                 int64\n","Deaths                    int64\n","Recovered               float64\n","Active                  float64\n","FIPS                    float64\n","Incident_Rate           float64\n","Total_Test_Results      float64\n","People_Hospitalized     float64\n","Case_Fatality_Ratio     float64\n","UID                     float64\n","ISO3                     object\n","Testing_Rate            float64\n","Hospitalization_Rate    float64\n","Date                     object\n","People_Tested           float64\n","Mortality_Rate          float64\n","dtype: object"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["data.dtypes"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"executionInfo":{"elapsed":78,"status":"ok","timestamp":1674961556996,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"JcJ--a6AIe2f","outputId":"f2fd5809-10c0-4d16-cf0f-c796ca4ca234"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-0a4b79fd-2e2e-4441-976c-21c15ca9d4c3\">\n","    <div class=\"colab-df-container\">\n","      <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>Lat</th>\n","      <th>Long_</th>\n","      <th>Confirmed</th>\n","      <th>Deaths</th>\n","      <th>Recovered</th>\n","      <th>Active</th>\n","      <th>FIPS</th>\n","      <th>Incident_Rate</th>\n","      <th>Total_Test_Results</th>\n","      <th>People_Hospitalized</th>\n","      <th>Case_Fatality_Ratio</th>\n","      <th>UID</th>\n","      <th>Testing_Rate</th>\n","      <th>Hospitalization_Rate</th>\n","      <th>People_Tested</th>\n","      <th>Mortality_Rate</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>57064.000000</td>\n","      <td>57064.000000</td>\n","      <td>5.912000e+04</td>\n","      <td>59120.000000</td>\n","      <td>1.512200e+04</td>\n","      <td>1.512200e+04</td>\n","      <td>59101.000000</td>\n","      <td>57064.000000</td>\n","      <td>3.663700e+04</td>\n","      <td>5129.000000</td>\n","      <td>46533.000000</td>\n","      <td>5.912000e+04</td>\n","      <td>4.592100e+04</td>\n","      <td>5129.000000</td>\n","      <td>1.181600e+04</td>\n","      <td>12027.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>36.840089</td>\n","      <td>-85.206614</td>\n","      <td>8.353134e+05</td>\n","      <td>11257.083187</td>\n","      <td>8.578908e+04</td>\n","      <td>5.936916e+04</td>\n","      <td>3284.976447</td>\n","      <td>14243.265003</td>\n","      <td>1.144235e+07</td>\n","      <td>6151.226555</td>\n","      <td>1.346480</td>\n","      <td>7.676417e+07</td>\n","      <td>1.161119e+05</td>\n","      <td>12.143891</td>\n","      <td>1.111017e+06</td>\n","      <td>3.322792</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>10.789486</td>\n","      <td>49.308644</td>\n","      <td>1.403547e+06</td>\n","      <td>16793.134320</td>\n","      <td>1.933303e+05</td>\n","      <td>1.190129e+05</td>\n","      <td>17249.739134</td>\n","      <td>11016.750051</td>\n","      <td>2.018320e+07</td>\n","      <td>13791.089316</td>\n","      <td>0.599868</td>\n","      <td>2.357406e+07</td>\n","      <td>1.307875e+05</td>\n","      <td>5.245396</td>\n","      <td>2.059628e+06</td>\n","      <td>2.487746</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-14.271000</td>\n","      <td>-170.132200</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.768000e+03</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>1.600000e+01</td>\n","      <td>5.973514e-08</td>\n","      <td>1.418440</td>\n","      <td>3.000000e+00</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>34.594600</td>\n","      <td>-101.165775</td>\n","      <td>6.065075e+04</td>\n","      <td>1040.000000</td>\n","      <td>3.872000e+03</td>\n","      <td>3.166000e+03</td>\n","      <td>18.000000</td>\n","      <td>3705.303547</td>\n","      <td>1.771434e+06</td>\n","      <td>558.000000</td>\n","      <td>1.027350</td>\n","      <td>8.400001e+07</td>\n","      <td>7.422100e+03</td>\n","      <td>8.359942</td>\n","      <td>1.132178e+05</td>\n","      <td>1.667964</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>39.061850</td>\n","      <td>-87.944200</td>\n","      <td>3.230550e+05</td>\n","      <td>4714.000000</td>\n","      <td>1.754850e+04</td>\n","      <td>1.475250e+04</td>\n","      <td>32.000000</td>\n","      <td>11624.900228</td>\n","      <td>4.784927e+06</td>\n","      <td>2014.000000</td>\n","      <td>1.306293</td>\n","      <td>8.400003e+07</td>\n","      <td>7.692253e+04</td>\n","      <td>11.282093</td>\n","      <td>4.153255e+05</td>\n","      <td>2.850501</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>42.361650</td>\n","      <td>-76.970625</td>\n","      <td>9.811215e+05</td>\n","      <td>14320.000000</td>\n","      <td>8.425575e+04</td>\n","      <td>6.111500e+04</td>\n","      <td>48.000000</td>\n","      <td>24626.484923</td>\n","      <td>1.296786e+07</td>\n","      <td>6001.000000</td>\n","      <td>1.613501</td>\n","      <td>8.400004e+07</td>\n","      <td>1.787269e+05</td>\n","      <td>15.268243</td>\n","      <td>1.152972e+06</td>\n","      <td>4.473480</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>61.370700</td>\n","      <td>145.673900</td>\n","      <td>1.197312e+07</td>\n","      <td>99817.000000</td>\n","      <td>2.470308e+06</td>\n","      <td>1.408516e+06</td>\n","      <td>99999.000000</td>\n","      <td>58927.992268</td>\n","      <td>1.844461e+08</td>\n","      <td>89995.000000</td>\n","      <td>8.803612</td>\n","      <td>8.410000e+07</td>\n","      <td>7.901759e+05</td>\n","      <td>38.501190</td>\n","      <td>1.956515e+07</td>\n","      <td>70.370370</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a4b79fd-2e2e-4441-976c-21c15ca9d4c3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["                Lat         Long_     Confirmed        Deaths     Recovered  \\\n","count  57064.000000  57064.000000  5.912000e+04  59120.000000  1.512200e+04   \n","mean      36.840089    -85.206614  8.353134e+05  11257.083187  8.578908e+04   \n","std       10.789486     49.308644  1.403547e+06  16793.134320  1.933303e+05   \n","min      -14.271000   -170.132200  0.000000e+00      0.000000  0.000000e+00   \n","25%       34.594600   -101.165775  6.065075e+04   1040.000000  3.872000e+03   \n","50%       39.061850    -87.944200  3.230550e+05   4714.000000  1.754850e+04   \n","75%       42.361650    -76.970625  9.811215e+05  14320.000000  8.425575e+04   \n","max       61.370700    145.673900  1.197312e+07  99817.000000  2.470308e+06   \n","\n","             Active          FIPS  Incident_Rate  Total_Test_Results  \\\n","count  1.512200e+04  59101.000000   57064.000000        3.663700e+04   \n","mean   5.936916e+04   3284.976447   14243.265003        1.144235e+07   \n","std    1.190129e+05  17249.739134   11016.750051        2.018320e+07   \n","min    0.000000e+00      1.000000       0.000000        1.768000e+03   \n","25%    3.166000e+03     18.000000    3705.303547        1.771434e+06   \n","50%    1.475250e+04     32.000000   11624.900228        4.784927e+06   \n","75%    6.111500e+04     48.000000   24626.484923        1.296786e+07   \n","max    1.408516e+06  99999.000000   58927.992268        1.844461e+08   \n","\n","       People_Hospitalized  Case_Fatality_Ratio           UID  Testing_Rate  \\\n","count          5129.000000         46533.000000  5.912000e+04  4.592100e+04   \n","mean           6151.226555             1.346480  7.676417e+07  1.161119e+05   \n","std           13791.089316             0.599868  2.357406e+07  1.307875e+05   \n","min               2.000000             0.000000  1.600000e+01  5.973514e-08   \n","25%             558.000000             1.027350  8.400001e+07  7.422100e+03   \n","50%            2014.000000             1.306293  8.400003e+07  7.692253e+04   \n","75%            6001.000000             1.613501  8.400004e+07  1.787269e+05   \n","max           89995.000000             8.803612  8.410000e+07  7.901759e+05   \n","\n","       Hospitalization_Rate  People_Tested  Mortality_Rate  \n","count           5129.000000   1.181600e+04    12027.000000  \n","mean              12.143891   1.111017e+06        3.322792  \n","std                5.245396   2.059628e+06        2.487746  \n","min                1.418440   3.000000e+00        0.000000  \n","25%                8.359942   1.132178e+05        1.667964  \n","50%               11.282093   4.153255e+05        2.850501  \n","75%               15.268243   1.152972e+06        4.473480  \n","max               38.501190   1.956515e+07       70.370370  "]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["data.describe()"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":78,"status":"ok","timestamp":1674961556998,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"1RmDMwHjIe2i","outputId":"5a22645c-a171-4a5a-ed3b-6735a035f15a"},"outputs":[{"data":{"text/plain":["Index(['Province_State', 'Country_Region', 'Last_Update', 'Lat', 'Long_',\n","       'Confirmed', 'Deaths', 'Recovered', 'Active', 'FIPS', 'Incident_Rate',\n","       'Total_Test_Results', 'People_Hospitalized', 'Case_Fatality_Ratio',\n","       'UID', 'ISO3', 'Testing_Rate', 'Hospitalization_Rate', 'Date',\n","       'People_Tested', 'Mortality_Rate'],\n","      dtype='object')"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["data.columns"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":75,"status":"ok","timestamp":1674961557000,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"NYidxbwDIe2j","outputId":"ff7555f9-d372-413f-8666-0d7f51b598ea"},"outputs":[{"data":{"text/plain":["Province_State              0\n","Country_Region              0\n","Last_Update                19\n","Lat                      2056\n","Long_                    2056\n","Confirmed                   0\n","Deaths                      0\n","Recovered               43998\n","Active                  43998\n","FIPS                       19\n","Incident_Rate            2056\n","Total_Test_Results      22483\n","People_Hospitalized     53991\n","Case_Fatality_Ratio     12587\n","UID                         0\n","ISO3                        0\n","Testing_Rate            13199\n","Hospitalization_Rate    53991\n","Date                     7366\n","People_Tested           47304\n","Mortality_Rate          47093\n","dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Check for missing values\n","missing_values_count = data.isnull().sum()\n","missing_values_count"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["28.36974031831948\n"]}],"source":["# how many total missing values do we have?\n","total_cells = np.product(data.shape)\n","total_missing = missing_values_count.sum()\n","\n","# percent of data that is missing\n","percent_missing = (total_missing/total_cells) * 100\n","print(percent_missing)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Wow, more than a quarter of the cells in this dataset are empty!"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Use this code if you want to remove all the rows that contain a missing value\n","data.dropna()\n","But it is not logical in our dataset, because every row in our dataset had at least one missing value. We might have better luck removing all the columns that have at least one missing value instead.\n","Use this code if you want to remove all columns with at least one missing value\n","columns_with_na_dropped = data.dropna(axis=1)\n","Again, it is not logical"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Data Engineering\n","\n","Before we apply LSTM, we need to clean our data and do some feature engineering and data pre-processing. Time series data is different from regular numerical data such as housing price data. The time-series data will change by the time and also be affected by other variables, so we cannot simply use mean, median, or mode to fill out the missing data. Two ways can fill out the missing data: one is the forward fill method and one is the backward fill method."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["<ipython-input-9-119d0df497a4>:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  df[col]=Backward_Fill(df,col)\n"]}],"source":["def Backward_Fill(data,col):\n","    return data[col].bfill()\n","\n","#  using loop to fill the missing data \n","# obtain unique countries\n","unique_states=list(set(data.Province_State))\n","for state in unique_states:\n","    df=data[data.Province_State==state]\n","    for col in df:\n","        df[col]=Backward_Fill(df,col)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["# data = data.fillna(method='ffill').fillna(method='bfill')"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["Province_State          0\n","Country_Region          0\n","Last_Update             0\n","Lat                     0\n","Long_                   0\n","Confirmed               0\n","Deaths                  0\n","Recovered               0\n","Active                  0\n","FIPS                    0\n","Incident_Rate           0\n","Total_Test_Results      0\n","People_Hospitalized     0\n","Case_Fatality_Ratio     0\n","UID                     0\n","ISO3                    0\n","Testing_Rate            0\n","Hospitalization_Rate    0\n","Date                    0\n","People_Tested           0\n","Mortality_Rate          0\n","dtype: int64"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["# Check for missing values\n","missing_values_count = data.isnull().sum()\n","missing_values_count"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":73,"status":"ok","timestamp":1674961557002,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"9AXw5NrjIe2k","outputId":"8225d645-1a37-43e8-fbea-fc3c44021521"},"outputs":[{"name":"stdout","output_type":"stream","text":["Duplicated rows: 0\n"]}],"source":["# Check for duplicate rows\n","print(\"Duplicated rows:\", data.duplicated().sum())"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["If you want to replace all NA's with 0, use this code:\n","data.fillna(0)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# replace all NA's the value that comes directly after it in the same column, \n","# then replace all the remaining na's with 0\n","data.fillna(method='bfill', axis=0).fillna(0)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Scaling vs. Normalization: What's the difference?\n","One of the reasons that it's easy to get confused between scaling and normalization is because the terms are sometimes used interchangeably and, to make it even more confusing, they are very similar! In both cases, you're transforming the values of numeric variables so that the transformed data points have specific helpful properties. The difference is that:\n","\n","in scaling, you're changing the range of your data, while\n","in normalization, you're changing the shape of the distribution of your data."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Scaling\n","This means that you're transforming your data so that it fits within a specific scale, like 0-100 or 0-1. You want to scale data when you're using methods based on measures of how far apart data points are, like support vector machines (SVM) or k-nearest neighbors (KNN). With these algorithms, a change of \"1\" in any numeric feature is given the same importance."]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"image/png":"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size 1500x300 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# generate 1000 data points randomly drawn from an exponential distribution\n","original_data = np.random.exponential(size=1000)\n","\n","# mix-max scale the data between 0 and 1\n","scaled_data = minmax_scaling(original_data, columns=[0])\n","\n","# plot both together to compare\n","fig, ax = plt.subplots(1, 2, figsize=(15, 3))\n","sns.histplot(original_data, ax=ax[0], kde=True, legend=False)\n","ax[0].set_title(\"Original Data\")\n","sns.histplot(scaled_data, ax=ax[1], kde=True, legend=False)\n","ax[1].set_title(\"Scaled data\")\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Notice that the shape of the data doesn't change, but that instead of ranging from 0 to 8ish, it now ranges from 0 to 1."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Normalization\n","Scaling just changes the range of your data. Normalization is a more radical transformation. The point of normalization is to change your observations so that they can be described as a normal distribution.\n","\n","Normal distribution: Also known as the \"bell curve\", this is a specific statistical distribution where a roughly equal observations fall above and below the mean, the mean and the median are the same, and there are more observations closer to the mean. The normal distribution is also known as the Gaussian distribution.\n","\n","In general, you'll normalize your data if you're going to be using a machine learning or statistics technique that assumes your data is normally distributed. Some examples of these include linear discriminant analysis (LDA) and Gaussian naive Bayes. (Pro tip: any method with \"Gaussian\" in the name probably assumes normality.)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x300 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# normalize the exponential data with boxcox\n","normalized_data = stats.boxcox(original_data)\n","\n","# plot both together to compare\n","fig, ax=plt.subplots(1, 2, figsize=(15, 3))\n","sns.histplot(original_data, ax=ax[0], kde=True, legend=False)\n","ax[0].set_title(\"Original Data\")\n","sns.histplot(normalized_data[0], ax=ax[1], kde=True, legend=False)\n","ax[1].set_title(\"Normalized data\")\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Notice that the shape of our data has changed. Before normalizing it was almost L-shaped. But after normalizing it looks more like the outline of a bell (hence \"bell curve\")."]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Convert our date columns to datetime¶\n","Now that we know that our date column isn't being recognized as a date, it's time to convert it so that it is recognized as a date. This is called \"parsing dates\" because we're taking in a string and identifying its component parts.\n","\n","The basic idea is that you need to point out which parts of the date are where and what punctuation is between them. There are lots of possible parts of a date, but the most common are %d for day, %m for month, %y for a two-digit year and %Y for a four digit year."]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"text/plain":["dtype('O')"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["data['Date'].dtype"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["The letter code to the dtype of the object. \"O\" is the code for \"object\""]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"text/plain":["0   2021-01-01\n","1   2021-01-01\n","2   2021-01-01\n","3   2021-01-01\n","4   2021-01-01\n","Name: date_parsed, dtype: datetime64[ns]"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["data['date_parsed'] = pd.to_datetime(data['Date'], format=\"%Y-%m-%d\")\n","data['date_parsed'].head()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["What if I run into an error with multiple date formats? While we're specifying the date format here, sometimes you'll run into an error when there are multiple date formats in a single column. If that happens, you can have pandas try to infer what the right date format should be. You can do that like so:\n","landslides['date_parsed'] = pd.to_datetime(landslides['Date'], infer_datetime_format=True)\n","\n","Why don't you always use infer_datetime_format = True? There are two big reasons not to always have pandas guess the time format. The first is that pandas won't always been able to figure out the correct date format, especially if someone has gotten creative with data entry. The second is that it's much slower than specifying the exact format of the dates."]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["0    1.0\n","1    1.0\n","2    1.0\n","3    1.0\n","4    1.0\n","Name: date_parsed, dtype: float64"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["# get the day of the month from the date_parsed column\n","day_of_month = data['date_parsed'].dt.day\n","day_of_month.head()"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"text/plain":["0    1.0\n","1    1.0\n","2    1.0\n","3    1.0\n","4    1.0\n","Name: date_parsed, dtype: float64"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["month = data['date_parsed'].dt.month\n","month.head()"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\ailaty\\AppData\\Local\\Temp\\ipykernel_29736\\1692280773.py:7: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n","  sns.distplot(day_of_month, kde=False, bins=31)\n"]},{"data":{"text/plain":["<AxesSubplot:xlabel='date_parsed'>"]},"execution_count":26,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# remove na's\n","day_of_month = day_of_month.dropna()\n","\n","# month = month.dropna()\n","\n","# plot the day of the month\n","sns.distplot(day_of_month, kde=False, bins=31)\n","# sns.distplot(month, kde=False, bins=12)"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"text/plain":["<AxesSubplot:xlabel='Date', ylabel='Confirmed'>"]},"execution_count":25,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["data1 = data.groupby('Date').sum() \n","s=data1.diff(periods=1)\n","sns.lineplot(data=s, x='Date', y='Confirmed')\n","# s.map(sns.lineplot, x='Date', y='Confirmed',color ='#FF2700')"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["<AxesSubplot:xlabel='Deaths', ylabel='Density'>"]},"execution_count":4,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXgAAAERCAYAAABxZrw0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAl0UlEQVR4nO3deZykVX3v8c+v9qquXmd6FmaAmYGBATfQUVTUICjua2Ki0USNCb40GNFsoPeqyb03l0TjK0ZvVIL6MohiVOJCNC4IRlCEYR8YRgZmgJlhmO6e7p6uqu5az/3jeRqboZfq7nq6luf7fr361V1PLc85PfDtU6fO8zvmnENERDpPpNkNEBGRYCjgRUQ6lAJeRKRDKeBFRDqUAl5EpEMp4EVEOlTLBbyZfdHMDpvZzga9XtXM7vC/vtuI1xQRaQfWauvgzexFQA74N+fcUxvwejnnXHb5LRMRaS8tN4J3zv03cGTmMTM7ycz+y8xuNbOfm9m2JjVPRKRttFzAz+Ey4H3OuWcBfwH8yyKemzKzHWZ2k5m9PpDWiYi0oFizG7AQM8sCzwe+YWbTh5P+fW8E/naWpx1wzr3M//kE59xBM9sC/NTM7nbOPRB0u0VEmq3lAx7vXcaYc+6MY+9wzl0NXD3fk51zB/3vD5rZ9cCZgAJeRDpey0/ROOeOAnvN7E0A5nlGPc81s34zmx7trwbOBu4NrLEiIi2k5QLezL4G/BI41cz2m9m7gLcC7zKzO4F7gNfV+XKnATv8510HXOqcU8CLSCi03DJJERFpjJYbwYuISGO01Iesq1evdps2bWp2M0RE2satt9467JwbnO2+lgr4TZs2sWPHjmY3Q0SkbZjZQ3PdpykaEZEOpYAXEelQCngRkQ6lgBcR6VAKeBGRDqWAFxHpUAp4EZEOpYAXEelQCvhjHBybRPV5RKQTKOBncM7x6k/fwA92Hmp2U0RElk0BP8OBsUlG8yX+5fo9GsWLSNtTwM+w88A4zzyxn7FCmZv3Hln4CSIiLUwBP8Nd+8fZtCrDS09fyxU3zVm/R0SkLSjgZ7hz/xibVnexZXWWB4ZyzW6OiMiyKOB9zjnuOXCULauzDHYnOTg21ewmiYgsS0vVg2+mg+NTmEF/Jg7AVLlKrlghm9SvSETak0bwvp0HxjlpMIuZYWas7UlxYHSy2c0SEVkyBbxv/+gkg93Jx28PdifZP1poYotERJZHAe8byRWfMB2zuivBfo3gRaSNKeB9w7kS3an447cHuhI8ohG8iLQxBbxvJF+kJ/2bEfxgd5KHjyjgRaR9KeB9R3IlemaM4Ae7k+w/oikaEWlfCnjfkUKJ7tSMOfhskoNjCngRaV+BBryZfcDM7jGznWb2NTNLBXm+5RjNP3EE35uOM1muki9WmtgqEZGlCyzgzWwD8GfAdufcU4Eo8OagzrcclWqNfLH6hFU0ZuYvldQoXkTaU9BTNDEgbWYxIAMcDPh8SzJaKJNNxYhE7AnHB7oSDE0Um9QqEZHlCSzgnXMHgE8ADwOPAuPOuR8Fdb7lOJIv0ZuOP+l4TyrOSF4BLyLtKcgpmn7gdcBm4Digy8zeNsvjLjCzHWa2Y2hoKKjmzGskX6Qn9eSaM92pGCO5UhNaJCKyfEFO0bwE2OucG3LOlYGrgecf+yDn3GXOue3Oue2Dg4MBNmduo/ky3bOM4LPJGMM5jeBFpD0FGfAPA881s4yZGXAesCvA8y3ZkXyR7lmqRvam45qDF5G2FeQc/K+AbwK3AXf757osqPMtx0i+RNcsAd+TjmsELyJtK9Bi5865jwIfDfIcjTA8MfscfG86rjl4EWlbupIVGM4/sdDYNG8VjQJeRNqTAh6vVHDPbMsk0zFGCwp4EWlPCni8C51mm6JJx6OUqzWmytUmtEpEZHkU8Hh1aGabojEz+tIJTdOISFtSwAO5YoWuZHTW+3ozcUa0kkZE2lDoA75crVGpOhLR2X8VvSmtpBGR9hT6gM8XK2SSUbxrsZ6sO62rWUWkPYU+4CemKmQSs0/PAHQnY5qDF5G2pICfqpBJzH29V3dK5QpEpD2FPuBzxflH8D3pOMMKeBFpQwr4Ypl0fJ6AT2kOXkTaU+gDfmKqQmqegO9OxTmiq1lFpA0p4KcqpBNz/xq6UzHGCuUVbJGISGOEPuBzxQqp2Hwj+Bjjkwp4EWk/oQ/4ianyvFM06XiUUqVGqVJbwVaJiCxf6AP+6OT8q2jMzJ+m0Ty8iLQXBfxUmfQ8AQ9eXXh90Coi7Sb0AZ+bqpCOz7+xVXcqxmhe8/Ai0l5CH/ATC1zoBJDVFI2ItCEFfB1TNNlkTFM0ItJ2Qh/w+WJ13itZAbqSWgsvIu0n9AGfK1bqGsFr0w8RaTehD/h8HXPw3SmVDBaR9hPqgF9oN6dp2WScUU3RiEibCXXA56bm381pmrdMUiN4EWkv4Q74OqZnwNvVScskRaTdhDrgF9rNaVo2FWNMBcdEpM2EOuDrHcF3JWIUilUqVRUcE5H2EeqAn5iafzenaZGI0ZWKqmywiLSVUAd8PWvgp/Wm4oxqHl5E2kioA97brq++X0E2FeeICo6JSBsJdcDnihWS8+zmNFN3KqYRvIi0lVAHfH4RAZ9Nai28iLSX0Ad83VM0yagqSopIWwl1wOeKlXn3Y50pm4wzklPAi0j7CHXA54vVugPeKzimipIi0j7CHfCl+qdourWKRkTaTLgDvlghtZhVNPqQVUTaSMgDfnFTNFomKSLtJNCAN7M+M/ummd1nZrvM7HlBnm+xCoucotG2fSLSThYupbg8nwL+yzn3O2aWADIBn29RCqX6R/CZRJTJcpVytUZ8gQ1CRERaQWBJZWY9wIuALwA450rOubGgzrcUk+Vq3XPwETN6NE0jIm0kyKHoFmAI+JKZ3W5ml5tZ17EPMrMLzGyHme0YGhoKsDlP5JzzR/D1/wp6UnFGtZJGRNpEkAEfA54JfNY5dyaQBy4+9kHOucucc9udc9sHBwcDbM4Tlao1IgaxRUy3dKdiHNFKGhFpE0EG/H5gv3PuV/7tb+IFfksoFKt11YKfqVslg0WkjQQW8M65Q8AjZnaqf+g84N6gzrdY3kVOiwv4bCqqEbyItI2gV9G8D7jSX0HzIPDOgM9Xt/wSRvBdCV3sJCLtI9CAd87dAWwP8hxLtZQRfHcqrno0ItI2Qrugu1Bc3Aoa8AuOqaKkiLSJ0Ab80kbwWkUjIu0jtAFfKFVIxhY7go8r4EWkbYQ24BdTaGxaTyrOiAJeRNpEaAO+UKqQWOQIvjftrYN3zgXUKhGRxgltwHsbbi+u+4lYhHg0wtGpSkCtEhFpnNAGfG4JUzQA/Zk4IzktlRSR1hfegJ9a/CoagJ50gmEtlRSRNhDagF/KMkmA3nSMYY3gRaQNhDfgixVSi5yDB38ljQJeRNpAeAN+Ebs5zdSdijE0oYAXkdZXV8Cb2bfM7FVm1jF/EArF+vdjnaknHWdII3gRaQP1Jtxngd8H7jezS81sW4BtWhGL2Y91pt5UXCN4EWkLdQW8c+4nzrm34m3YsQ/4sZn9wszeaWbxIBsYlCUHfDrO8IRW0YhI66t7jsLMVgHvAP4YuB34FF7g/ziQlgUsX6rUveH2TL1plQwWkfZQVz14M7sa2AZcAbzGOfeof9fXzWxHUI0L0mSpSnKJc/CqRyMi7aDeDT8ud859f+YBM0s654rOuZbc0GM+pUoNgPgiNtyelklEKVdrTJWXNsUjIrJS6k24/z3LsV82siErqVCqkE4sLZzNjL50Qhc7iUjLm3cEb2brgA1A2szOBMy/qwfIBNy2wCx1Dfy0vkyc4VyJjf1t+ysQkRBYaIrmZXgfrG4EPjnj+ATwoYDaFLhCsbLoDbdn8lbSaAQvIq1t3oB3zn0Z+LKZ/bZz7lsr1KbA5UvVZQV8XybOYxNTDWyRiEjjLTRF8zbn3FeATWb2wWPvd859cpantbx8sbKkFTTTetNxDo0r4EWktS00RdPlf88G3ZCVlC8urZLktIGuJAfGJhvYIhGRxltoiubz/ve/WZnmrIxCqbqkSpLTBroS7Dww3sAWiYg0Xr3Fxv7BzHrMLG5m15rZsJm9LejGBSVfWt4UzUBXgseOaopGRFpbvSl3vnPuKPBqYD9wCvCXgbUqYIVileQSyhRMG+hK6ENWEWl59Qb8dEGxVwJfc84dCag9KyJfqpBYxhRNVyJKperIFbX5toi0rnpT7ntmdh+wHbjWzAaBth3C5opLKzQ2zcxYnU1qJY2ItLR6ywVfDDwP2O6cKwN54HVBNixI+SVu9jHTqmxCAS8iLa3eYmMAp+Gth5/5nH9rcHtWRG6qQn8msazX6M8kOKQPWkWkhdVbLvgK4CTgDqDqH3a0acAvtxYNeFezHhrXWngRaV31juC3A6c751yQjVkpy73QCWAgk+DgmEbwItK66p2I3gmsC7IhK2m5FzoB9HclOKgRvIi0sHpH8KuBe83sZuDxMorOudcG0qqAFUoNGMF36UNWEWlt9Qb8x4JsxErzNtxe3gh+MJvkoOrRiEgLqyvgnXM/M7MTga3OuZ+YWQZo2/3qCqUqyWWO4LtTMao1x1ihRN8yV+SIiASh3lo0fwJ8E/i8f2gD8O2A2hS4Qml5FzqBd7HT+r40D40UGtQqEZHGqnee4k+Bs4GjAM65+4E1QTUqSKVKjVoN4lFb+MELWNuTZN9IvgGtEhFpvHoDvuicK03f8C92qmvJpJlFzex2M7tmKQ1stMlSlVQigtnyA34wm2TfsAJeRFpTvQH/MzP7EN7m2y8FvgF8r87nvh/YtZTGBSFfWt5+rDOt6UmxVyN4EWlR9Qb8xcAQcDfwbuD7wP9Y6ElmthF4FXD5UhvYaIVShXSiMQG/rifFvmHNwYtIa6p3FU3NzL4NfNs5N7SI1/8n4K+A7rkeYGYXABcAnHDCCYt46aXJF5dfpmDa2p4UDx9RwItIa5p3BG+ej5nZMHAfsNvMhszsIwu9sJm9GjjsnLt1vsc55y5zzm13zm0fHBxcVOOXIt+AFTTT+jJx8sUKedWFF5EWtNAUzUV4q2ee7Zxb5ZwbAM4CzjazDyzw3LOB15rZPuAq4Fwz+8oy27tsheLyL3KaFjFjfW9KSyVFpCUtlHR/CLzFObd3+oBz7kHgbf59c3LOXeKc2+ic2wS8Gfipc67p+7jmG1CmYKZ1vSktlRSRlrRQwMedc8PHHvTn4eOzPL7l5YtVksssNDbTup4U9z820bDXExFplIU+ZC0t8b4ncM5dD1xf7+ODVChVlrXh9rFOGOji7gPjDXs9EZFGWSjgn2FmR2c5bkAqgPYELl+sLmvD7WNtWpXhm7ftb9jriYg0yrwB75xr24Jic5kolht2oRN4SyVH80XGJ8v0ptty1kpEOlTjhrJtYmKysR+yRiLGiau6uO/R2d7oiIg0T/gCvlgm06ArWaedMJDhXgW8iLSY8AX8VONq0Uw7fiCjD1pFpOWELuDzxcbVopl24kCGew9qBC8irSV0AZ8rNnYOHuCEVRkeGimoZIGItJQQBny14XPwyViULYNd3LLvSENfV0RkOUIX8IVi4+fgAU5b180vHhhp+OuKiCxV6AK+0bVopp12XC837HlSVQcRkaYJVcAXK1Wca8x+rMfauibL3qE845Plhr+2iMhShCrgc1MVMsloQ/ZjPVY8GuGUtVlu3qt5eBFpDaEK+HyxSiZR1yZWS/LUDb386J5Dgb2+iMhihCrgG12H5ljP3bKKH95ziFKlFtg5RETqFaqAz01VGr5EcqbV2SQb+zPcsGcx29aKiAQjVAGfLwWzRHKm52we4Nu3Hwz0HCIi9QhVwE9MNb5MwbHO2jzAdfcdJqerWkWkyUIV8LlipaHb9c2mL5PgaRt7+caORwI9j4jIQsIV8FPBXOR0rPNPX8cXbthLreYCP5eIyFzCFfABFBqbzSlrs6TjUa6973Dg5xIRmUuoAn5iKthlktPMjFc8dT2f+smvcU6jeBFpjlAF/NEANvuYy1lbBsgVK1y7S6N4EWmOUAV8LoDNPuYSMeONz9zIx3+0W3PxItIU4Qr4FRzBA2w/sR/nHN+9U+viRWTlhS/gV2gED95c/FuecwKX/uA+psrVFTuviAiELeAD2uxjPtvW9bBpdYZ//e8HV/S8IiKhCvh8aWVH8NPe8uwT+NefP8iBsckVP7eIhFe4Ar5YXfERPMCanhTnn76Wv/3ePSt+bhEJr9AEvHOOyVJzAh7gNc/YwF37x7lut5ZNisjKCE3A50tVEjEjEmn8bk71SMQivOP5m/jQ1XeTVyEyEVkBoQn48cky2VS8qW14+sY+Tlmb5eM/vK+p7RCRcAhNwI8VSmSTwW3XV6+3nnUi19z1KDfuGW52U0Skw4Um4Mcny2STzZl/n6k7FeddL9jCB//9DsYL5WY3R0Q6WHgCvlCmqwVG8ABnHN/Hs07s56Kv364yBiISmPAE/GSZTKI1Ah68tfEHx6b43M8eaHZTRKRDhSrgu5pwkdNcYtEI7zv3ZL5ww16u19JJEQlAaAJ+tFBqqRE8wKpskgvPPZkPfP0OHhjKNbs5ItJhQhPwYy00Bz/TtnU9/O7243nHl25mNF9qdnNEpIMEFvBmdryZXWdmu8zsHjN7f1DnqsdYoTVW0czmnFPXcMbxfbz7ih2Uq7VmN0dEOkSQI/gK8OfOudOA5wJ/amanB3i+eY1NllpyBD/tzdtPoOrgI9/Z2eymiEiHCCzgnXOPOudu83+eAHYBG4I630JaaZnkbCIR473nnMSNe0a48qaHmt0cEekAKzIHb2abgDOBX63E+WYzPlVuiStZ55NJxLjovK18/Ee7ue3h0WY3R0TaXOABb2ZZ4FvARc65o7Pcf4GZ7TCzHUNDQ4G14+hkpaVH8NPW96V51ws2856v3MoRfegqIssQaMCbWRwv3K90zl0922Occ5c557Y757YPDg4G0o5qzSsVnGmhdfDz2X7iAGdtXsX7r9KVriKydEGuojHgC8Au59wngzpPPY5Olskko0SsOaWCl+JN2zcynCvyWV3pKiJLFOQI/mzgD4BzzewO/+uVAZ5vTl6hsdafnpkpFonw3nNO5vKfP6j5eBFZkiBX0dzgnDPn3NOdc2f4X98P6nzzGZssk021V8ADrM4meefZm7nwq7cxPqnKkyKyOKG4knV8sky2xcoU1OvZmwZ42oZeLv7WXTin+XgRqV8oAn6s0NoXOS3k959zIrsePcrXbn642U0RkTYSioA/OllumxU0s0nEIlx47lb+/r92s/vQRLObIyJtIhQBP1Zo74AH2NCX5i3POZ73XHkrhZI27RaRhYUj4Cdbu0xBvV60dZATBjJccvXdmo8XkQWFIuAPH52iNx1vdjOWzcx4+/M2cfvDY1x1yyPNbo6ItLhQBPxjE8WOCHiAVDzKn523lUt/cB937R9rdnNEpIWFIuCHJor0ZxLNbkbDbOhL886zN/HuK25lJFdsdnNEpEWFIuBHckX6Mp0xgp921uZVnLV5Fe++4lZKFW0SIiJP1vEBP1WuMlmutl2pgnq8aftGzODD/6EPXUXkyTo+4IcmivRlElgbFRqrV8SM9/zWydz28CifvnZPs5sjIi2m8wM+V2Sgg+bfj5VORPnz80/lqzc/zL/9Yl+zmyMiLaTjA/7w0SK9HTb/fqz+TIJLXrGNT1+3R9v9icjjOj7gh3JF+jpkieR81vSk+NArTuMz1+3hn378a83Ji0jnB/zho1N0t2Gp4KVY15viI68+nWvufpQLv3qbShqIhFzHB/yh8Sn6OngO/lh9mQT/81WnUyhVefWnb+C+Q0/aBldEQqLjA/7wROetgV9IIhbhT164hZc/ZR2/9/mbuOKX+zRlIxJCHT93cXhiir50eEbw08yMF24d5OTBLP/v+j3csGeYf/zdMzryegARmV3Hj+CHJ0r0h2wEP9P6vjQffc1TqNYcr/30DTw0km92k0RkhXR0wNdqjtFCqWMKjS1VPBrhXS/YwjnbBnnjv/yC27WJt0godHTAj+RLZJJRYtGO7mbdXnraOv7o7M2840u38N+/Hmp2c0QkYB2dfPtG8qzvSTe7GS3lmSf2c9FLtvL+q27nmrsONrs5IhKgjg74vUN51velmt2MlrNtXQ9//fJtfPQ79/DlX+xtdnNEJCAdHfB7hnKs6VbAz+bEVV38z1efzuU/38uH/+NuylWVHBbpNB0d8A8M5TiuVwE/l7U9KT722qew+9AEr/2MLooS6TQdHfAPDuVZp4CfVyYR44MvPYUXbh3k9z5/ExdddTs7D4zrwiiRDtCxV71Ua44Do5Os79WHrAsxM1586hrO2jzAj+55jD/+8g6SsQjnnDrIC7cO8pwtA/Skwr3UVKQddWzA7x8t0JeJk4h19JuUhsokYrz+zA287ozjeHA4z84D43z6p/dz/1U5nrahl9efuYHXPOM4XQ0r0iY69v/UB4fzbOjT6H0pzIyTBrOcNJjldWdsoFipctf+cb59+wH+7vu7+P3nnMAFL9rCqmyy2U0VkXl0bMDvHcqztkcB1AjJWJRnbxrg2ZsGGM4VuebOg5z7jz/jj87exJ+8aAuZRMf+ZyTS1jp2/uL+wxOs1UVODbc6m+QdZ2/mb177FG7ed4Tf+ofr+cpN+yhWqs1umogco2OHXrfsHeXtz9/U7GZ0rLU9KS588VYeGMrxH7cd4J+v3cNbzzqB337WRjb2ZxpyjslSlVsfGmXHviPsfmyC0UKJVDzK047r5dzT1nDmCf0NOY9Ip+rIgB+aKPLo0Uk2r+5qdlM63kmDWf7iZaeydzjPdbsP88Ubb2CgK8HTN/Zyytpu1nQn6UnHySZjpOJR0vEomYT3lYxHSfh1ggqlCiP5EnuH8+zcP85Ne0fYeeAom1ZnOGVtN5tXd/GMVB/FSo29wznee+VtrOtJ8ZHXnK6gF5mDtdJ65+3bt7sdO3Ys+3W+e+dBvvLLh/jAS09pQKtkMWrO8dBIgX0jeR4bn2J8qkyhVGWqXKVYqVGq1Jgqe7dLlRrlqgODVDxCbyrO2p4Ux/en2bq2m23rekgnorOep1pz/OKBYa665RHecOYG/vrl27RiSkLJzG51zm2f7b6OHMHfcP8wp63vbnYzQilixubVXYG/e4pGvA1NnnF8H//68wf57c/+gs/9wbO0ckpkho4c8ty4Z5inHNfb7GbICuhJxfngS07hGRt7ec2nb+D63Yeb3SSRltFxI/gHhnIUShU29mskFxZmxquefhxbBrP85Tfu5FVPP46LX7GNVHz26R2RsOi4Efxnr3uA87atwcya3RRZYaet7+Hv3vB07j+c45yPX8/Vt+2nVFGVTAmvjhrBP3KkwI93HeITbzqj2U2RJsmmYlz44pPZ9ehRvnTjPv7XNfdy3mlrefamfo7vz9CbiZOIRqg574Pamr/IwAySsQiZRIyedJyuRDS0g4RSpUahVGFiqkKuWKFQqlAs16jUHNGIkU5E6c8kWNeTmvND8FZQqtSYLFdxzpGMRUnFI6H7Nw004M3s5cCngChwuXPu0qDOVa05/s9/7uK8bWtVK0U4bX0Pp63v4dD4FHftH+MHOw8xkiuSL1Yp12pEMMy8D2sBag4qVS8QcsUKlaqjNx2nLxOnJxUnk4ySikeJmlGtOUrVGpOlKoVSlWKlSrnqMPP2v80mY/Rn4qzpTnFcX5r1vSnW96VY051idTZBXybx+HnnUq05ckUvYPPFCpMlbxVSteaIGMSiEdLxKF3JKF3JGJlElFQsSiRiOOcoVmpMTFUYLZQYnihyeKLIY0eneOzoFEMTRcYmy4wXyuRLFSbLNYr+KqfJsnfBWibhLWlNJ7x+J6IRohGv78VKlYmpCiO5EtlUjC2ruzh1XTenre9h27puTl6TpS+TCPzf2DnHSL7EnsM5/2uCB4byPHKkwOGJIqVKjWQ8gmGU/P0OBrNJThjIsHVtlm3rejh1XTenrM3S3aHF9AJbJmlmUeDXwEuB/cAtwFucc/fO9ZylLpOs1hwf/Pc7eGAox5+/9FTNvcqylSo1csUKE1Nl8qUqxXKVUrWGcxA1Ixo1krEIyViURMwLPxyUazWmSl4Ajk2WOJIvMT5Z5ki+xGihzFihRL5YJRn3AjoRixDxR5XT4TlZrlIs1/zrBWKkE1GS8QjxSAT/NFSq3mOn/NH2VNlbgmp498ej5r8bidGbitOXSdCXidOb9v5gdadidCVjJGMRUvEo8WjE70+k7j2Ma84xmi9xcHyKR44UODA2yf7RAvtHJ4lHI2zoT7OmO8mqrgTdqRjxaBQzr58zl8sWKzWKlRqVmhfCETMS0QiJWGRGv42qc0yWqowVShyeKHJwfJKIGRv70hzXl2Zdb4p1vSnWdqdYlU2Qjj/xXVixUuVIvsRjR6c4MDrFgTGvrY+MFsgmY2zsz7C+N8XqbJKedIxMwvv9xKfb4r/D607F6E3HWZVNMNCVIBlrbt7Mt0wyyIB/HvAx59zL/NuXADjn/u9cz1lqwO88MM5bL/8VF523VWuhpeXVHI8HW7lae7z2fjQSIT79hyMeZbGTCQ5wzptuauZEhAPGJ8sM54qMFcpMTJWZLNeo1rw/kBEz4lF7PDjj0QixqBG137ybqta8ayTK1Zo/lQYRg2Q8SlciSm8mzmA22ZB36w44kisxlCsyki8xMVn2/sj6f3QqVUfF/6NUrHjv2gqlJ5fm6EnFGOxOsjqbpD+ToDcdJ5vy3l0lH+9nhOj0O0ezx/+djutL8eJTl/bZYbPWwW8AHplxez9w1rEPMrMLgAv8mzkz273oM0Xj8Xj/+pPf/Zml/Xddm8zFIulsZSnPbXXqW3vq1L51Xr8MIpGYWSS2f/KoRTNLX55deuzBO3C1pRR1OnGuO4IM+NnC9klvF5xzlwGXBdiOBZnZjkpuZNa/gO1OfWtPndq3Tu0X+H0bP9xSfQtyPmM/cPyM2xuBgwGeT0REZggy4G8BtprZZjNLAG8Gvhvg+UREZIbApmiccxUzuxD4Id4yyS865+4J6nzL1NQpooCpb+2pU/vWqf2CFuxbS1WTFBGRxtGaQhGRDqWAFxHpUKEPeDN7uZntNrM9ZnZxs9szGzM73syuM7NdZnaPmb3fPz5gZj82s/v97/0znnOJ36fdZvayGcefZWZ3+/f9s/lXVphZ0sy+7h//lZltWsH+Rc3sdjO7psP61Wdm3zSz+/x/u+d1UN8+4P+3uNPMvmZmqXbtm5l90cwOm9nOGcdWpC9m9nb/HPeb2dsb3jnnXGi/8D78fQDYAiSAO4HTm92uWdq5Hnim/3M3XgmI04F/AC72j18M/L3/8+l+X5LAZr+PUf++m4Hn4V2n8APgFf7x9wKf839+M/D1FezfB4GvAtf4tzulX18G/tj/OQH0dULf8C5i3Auk/dv/DryjXfsGvAh4JrBzxrHA+wIMAA/63/v9n/sb2reV+o+9Fb/8f4wfzrh9CXBJs9tVR7u/g1fjZzew3j+2Htg9Wz/wVjI9z3/MfTOOvwX4/MzH+D/HgGH8D+ED7stG4FrgXH4T8J3Qrx68ELRjjndC36avUh/wz3sNcH479w3YxBMDPvC+zHyMf9/n8ep1NaxfYZ+ima2cwoYmtaUu/tu7M4FfAWudc48C+N/X+A+bq18b/J+PPf6E5zjnKsA4sCqQTjzRPwF/Bcws3N4J/doCDAFf8qefLjezLjqgb865A8AngIeBR4Fx59yP6IC+zbASfQk8f8Ie8HWVU2gVZpYFvgVc5Jw7Ot9DZznm5jk+33MCY2avBg47526t9ymzHGu5fvlieG/7P+ucOxPI473Vn0vb9M2fj34d3hTFcUCXmb1tvqfMcqwl+1aHRvYl8D6GPeDbppyCmcXxwv1K59zV/uHHzGy9f/96YHpD0rn6td//+djjT3iOmcWAXuBI43vyBGcDrzWzfcBVwLlm9hXav1/T593vnPuVf/ubeIHfCX17CbDXOTfknCsDVwPPpzP6Nm0l+hJ4/oQ94NuinIL/afwXgF3OuU/OuOu7wPQn72/Hm5ufPv5m/9P7zcBW4Gb/reaEmT3Xf80/POY506/1O8BPnT8xGBTn3CXOuY3OuU14v/ufOufe1u798vt2CHjEzE71D50H3EsH9A1vaua5Zpbx23QesIvO6Nu0lejLD4Hzzazff1d0vn+scYL60KJdvoBX4q1KeQD4cLPbM0cbX4D31u0u4A7/65V483jXAvf73wdmPOfDfp9243+a7x/fDuz07/sMv7maOQV8A9iDtxpgywr38Rx+8yFrR/QLOAPY4f+7fRtvpUSn9O1vgPv8dl2Bt6qkLfsGfA3vs4Qy3qj6XSvVF+CP/ON7gHc2um8qVSAi0qHCPkUjItKxFPAiIh1KAS8i0qEU8CIiHUoBLyLSoRTw0vHMrGpmd/jVD+80sw+a2ZL+2zevQuR7Z9w+x/wqmCKtRgEvYTDpnDvDOfcUvCJtrwQ+usTX6sOrDijS8hTwEirOucPABcCF5oma2cfN7BYzu8vM3g1e3R8zu9bMbvNrfL/Of4lLgZP8dwQf949l7Td136+cUQf8UjO713/dT6x4ZyX0Att0W6RVOece9Kdo1uAVzRp3zj3bzJLAjWb2I7wqf29wzh01s9XATWb2XbyCYU91zp0B3hQNXnXPp+DVEbkRONvM7gXeAGxzzjkz61vJPoqARvASXtOV/M4H/tDM7sArwbwKr76IAX9nZncBP8Er47p2jte62Tm33zlXwysjsQk4CkwBl5vZG4FCMN0QmZtG8BI6ZrYFqOJVCDTgfc65Hx7zmHcAg8CznHNlv+Jlao6XLM74uQrEnHMVM3sOXiGuNwMX4m1qIrJiNIKXUDGzQeBzwGecV4jph8B7/HLMmNkp/sYcvXi16stm9mLgRP8lJvC2TVzoPFmg1zn3feAivMJjIitKI3gJg7Q/BRMHKnjVD6fLLl+ON6Vym//h6BDweuBK4HtmtgNv2uU+AOfciJndaN4GzT8A/nOOc3YD3zGzFN67hA80vFciC1A1SRGRDqUpGhGRDqWAFxHpUAp4EZEOpYAXEelQCngRkQ6lgBcR6VAKeBGRDvX/AT30gJcD+JDgAAAAAElFTkSuQmCC","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# kernel density estimate (KDE) plot \n","sns.kdeplot(data=data['Deaths'], shade=True)"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/plain":["<seaborn.axisgrid.JointGrid at 0x168c66e1910>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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size 432x432 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 2D KDE plot\n","sns.jointplot(x=data['Deaths'], y=data['Confirmed'], kind=\"kde\")"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[1;32m<ipython-input-4-9f33636b51d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in 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\u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 175\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert_yunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m   1509\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1510\u001b[0m         \u001b[1;31m# If x is natively supported by Matplotlib, doesn't need converting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1511\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mmunits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_natively_supported\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1512\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1513\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\matplotlib\\units.py\u001b[0m in \u001b[0;36m_is_natively_supported\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     62\u001b[0m     \"\"\"\n\u001b[0;32m     63\u001b[0m     \u001b[1;31m# Matplotlib natively supports all number types except Decimal.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m         \u001b[1;31m# Assume lists are homogeneous as other functions in unit system.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mthisx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;31mKeyboardInterrupt\u001b[0m: "]}],"source":["sns.pairplot(data)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["grid_layout = sns.FacetGrid(covid_data_country_sub\n","                            ,col = 'country'\n","                            ,col_wrap = 3\n","                            ,col_order= covid_top9_countries.values\n","                            ,aspect = 1.2\n","                            )\n","grid_layout.map(sns.lineplot, 'date', 'new_cases',color ='#FF2700')\n","grid_layout.set_titles('{col_name}')\n","for ax in grid_layout.axes:\n","    ax.set_xlabel(\"\")\n","    ax.set_ylabel(\"\")\n","for ax in grid_layout.axes:\n","    for label in ax.get_xticklabels():\n","        label.set_rotation(90)\n","grid_layout.fig.text(0.5, -.1,'Date', fontsize=20) #add text\n","grid_layout.fig.text(-0.12, .5,'New Cases', fontsize=20) #add text\n","grid_layout.fig.suptitle('Growth of Daily New Cases for COVID-19\\nas of April 9, 2020'\n","                         ,y = 1.12\n","                         ,fontsize = 24\n","                         )"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"ename":"KeyError","evalue":"'2021-01-01'","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[1;32m<ipython-input-18-503c2986c580>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtotal_cases\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mtotal_cases\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Confirmed\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 882\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m    987\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    988\u001b[0m         \u001b[1;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 989\u001b[1;33m         \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    990\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    991\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\range.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 358\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    359\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;31mKeyError\u001b[0m: '2021-01-01'"]}],"source":["total_cases = []\n","for i in data['date_parsed']:\n","    total_cases.append(data[\"Confirmed\"][i].sum())\n","\n","plt.figure(figsize = (10,6))\n","sns.lineplot(x=data[\"date_parsed\"],y=total_cases,color='r',marker='*')\n","plt.title('World Total confirmed cases- {:.2f} million'.format(total_cases[-1]/10**6),fontweight='bold',size=20)\n","plt.xlabel('No of days since 22 Jan 2020',size=15)\n","plt.ylabel('World Covid Count',size=15)\n","plt.show()"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"ename":"KeyError","evalue":"'date_parsed'","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n","\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n","\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n","\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n","\u001b[1;31mKeyError\u001b[0m: 'date_parsed'","\nThe above exception was the direct cause of the following exception:\n","\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[1;32m<ipython-input-22-ecca6edc462e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtotal_cases_Nevada\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'date_parsed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mtotal_cases_Nevada\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Province_State'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'Nevada'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;31mKeyError\u001b[0m: 'date_parsed'"]}],"source":["total_cases_Nevada = []\n","for i in data['date_parsed']:\n","    total_cases_Nevada.append(data[data['Province_State']=='Nevada'][i].sum())"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Alabama', 'Alaska', 'American Samoa', 'Arizona', 'Arkansas',\n","       'California', 'Colorado', 'Connecticut', 'Delaware',\n","       'Diamond Princess', 'District of Columbia', 'Florida', 'Georgia',\n","       'Grand Princess', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana',\n","       'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland',\n","       'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi',\n","       'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire',\n","       'New Jersey', 'New Mexico', 'New York', 'North Carolina',\n","       'North Dakota', 'Northern Mariana Islands', 'Ohio', 'Oklahoma',\n","       'Oregon', 'Pennsylvania', 'Puerto Rico', 'Recovered',\n","       'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee',\n","       'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia',\n","       'Washington', 'West Virginia', 'Wisconsin', 'Wyoming'],\n","      dtype=object)"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["states = data['Province_State'].unique()\n","\n","# sort them alphabetically and then take a closer look\n","states.sort()\n","states"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"data":{"text/plain":["Index(['Country_Region', 'Last_Update', 'Lat', 'Long_', 'Confirmed', 'Deaths',\n","       'Recovered', 'Active', 'FIPS', 'Incident_Rate', 'Total_Test_Results',\n","       'People_Hospitalized', 'Case_Fatality_Ratio', 'UID', 'ISO3',\n","       'Testing_Rate', 'Hospitalization_Rate', 'Date', 'People_Tested',\n","       'Mortality_Rate', 'Province_State_Alabama', 'Province_State_Alaska',\n","       'Province_State_American Samoa', 'Province_State_Arizona',\n","       'Province_State_Arkansas', 'Province_State_California',\n","       'Province_State_Colorado', 'Province_State_Connecticut',\n","       'Province_State_Delaware', 'Province_State_Diamond Princess',\n","       'Province_State_District of Columbia', 'Province_State_Florida',\n","       'Province_State_Georgia', 'Province_State_Grand Princess',\n","       'Province_State_Guam', 'Province_State_Hawaii', 'Province_State_Idaho',\n","       'Province_State_Illinois', 'Province_State_Indiana',\n","       'Province_State_Iowa', 'Province_State_Kansas',\n","       'Province_State_Kentucky', 'Province_State_Louisiana',\n","       'Province_State_Maine', 'Province_State_Maryland',\n","       'Province_State_Massachusetts', 'Province_State_Michigan',\n","       'Province_State_Minnesota', 'Province_State_Mississippi',\n","       'Province_State_Missouri', 'Province_State_Montana',\n","       'Province_State_Nebraska', 'Province_State_Nevada',\n","       'Province_State_New Hampshire', 'Province_State_New Jersey',\n","       'Province_State_New Mexico', 'Province_State_New York',\n","       'Province_State_North Carolina', 'Province_State_North Dakota',\n","       'Province_State_Northern Mariana Islands', 'Province_State_Ohio',\n","       'Province_State_Oklahoma', 'Province_State_Oregon',\n","       'Province_State_Pennsylvania', 'Province_State_Puerto Rico',\n","       'Province_State_Recovered', 'Province_State_Rhode Island',\n","       'Province_State_South Carolina', 'Province_State_South Dakota',\n","       'Province_State_Tennessee', 'Province_State_Texas',\n","       'Province_State_Utah', 'Province_State_Vermont',\n","       'Province_State_Virgin Islands', 'Province_State_Virginia',\n","       'Province_State_Washington', 'Province_State_West Virginia',\n","       'Province_State_Wisconsin', 'Province_State_Wyoming'],\n","      dtype='object')"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["data.columns"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"ename":"ValueError","evalue":"Input X contains NaN.\nSelectKBest does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[1;32mIn [24], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[39m# Apply Univariate Feature Selection\u001b[39;00m\n\u001b[0;32m     13\u001b[0m selector \u001b[39m=\u001b[39m SelectKBest(score_func\u001b[39m=\u001b[39mchi2, k\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n\u001b[1;32m---> 14\u001b[0m fit \u001b[39m=\u001b[39m selector\u001b[39m.\u001b[39;49mfit(X, Y)\n\u001b[0;32m     16\u001b[0m \u001b[39m# Summarize scores\u001b[39;00m\n\u001b[0;32m     17\u001b[0m np\u001b[39m.\u001b[39mset_printoptions(precision\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m)\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:461\u001b[0m, in \u001b[0;36m_BaseFilter.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    444\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y):\n\u001b[0;32m    445\u001b[0m     \u001b[39m\"\"\"Run score function on (X, y) and get the appropriate features.\u001b[39;00m\n\u001b[0;32m    446\u001b[0m \n\u001b[0;32m    447\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[39m        Returns the instance itself.\u001b[39;00m\n\u001b[0;32m    460\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 461\u001b[0m     X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m    462\u001b[0m         X, y, accept_sparse\u001b[39m=\u001b[39;49m[\u001b[39m\"\u001b[39;49m\u001b[39mcsr\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mcsc\u001b[39;49m\u001b[39m\"\u001b[39;49m], multi_output\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[0;32m    463\u001b[0m     )\n\u001b[0;32m    465\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m callable(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_func):\n\u001b[0;32m    466\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[0;32m    467\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mThe score function should be a callable, \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m (\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m) was passed.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    468\u001b[0m             \u001b[39m%\u001b[39m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_func, \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_func))\n\u001b[0;32m    469\u001b[0m         )\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:596\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    594\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[0;32m    595\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 596\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n\u001b[0;32m    597\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[0;32m    599\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1074\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1069\u001b[0m         estimator_name \u001b[39m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1070\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m   1071\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m requires y to be passed, but the target y is None\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1072\u001b[0m     )\n\u001b[1;32m-> 1074\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[0;32m   1075\u001b[0m     X,\n\u001b[0;32m   1076\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[0;32m   1077\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[0;32m   1078\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[0;32m   1079\u001b[0m     order\u001b[39m=\u001b[39;49morder,\n\u001b[0;32m   1080\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[0;32m   1081\u001b[0m     force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[0;32m   1082\u001b[0m     ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[0;32m   1083\u001b[0m     allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[0;32m   1084\u001b[0m     ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[0;32m   1085\u001b[0m     ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[0;32m   1086\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[0;32m   1087\u001b[0m     input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m   1088\u001b[0m )\n\u001b[0;32m   1090\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[0;32m   1092\u001b[0m check_consistent_length(X, y)\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:899\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    893\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    894\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mFound array with dim \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m expected <= 2.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    895\u001b[0m             \u001b[39m%\u001b[39m (array\u001b[39m.\u001b[39mndim, estimator_name)\n\u001b[0;32m    896\u001b[0m         )\n\u001b[0;32m    898\u001b[0m     \u001b[39mif\u001b[39;00m force_all_finite:\n\u001b[1;32m--> 899\u001b[0m         _assert_all_finite(\n\u001b[0;32m    900\u001b[0m             array,\n\u001b[0;32m    901\u001b[0m             input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[0;32m    902\u001b[0m             estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[0;32m    903\u001b[0m             allow_nan\u001b[39m=\u001b[39;49mforce_all_finite \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    904\u001b[0m         )\n\u001b[0;32m    906\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_samples \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m    907\u001b[0m     n_samples \u001b[39m=\u001b[39m _num_samples(array)\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:146\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m    124\u001b[0m         \u001b[39mif\u001b[39;00m (\n\u001b[0;32m    125\u001b[0m             \u001b[39mnot\u001b[39;00m allow_nan\n\u001b[0;32m    126\u001b[0m             \u001b[39mand\u001b[39;00m estimator_name\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    130\u001b[0m             \u001b[39m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[0;32m    131\u001b[0m             \u001b[39m# scikit-learn.\u001b[39;00m\n\u001b[0;32m    132\u001b[0m             msg_err \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[0;32m    133\u001b[0m                 \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m does not accept missing values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    134\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    144\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39m#estimators-that-handle-nan-values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    145\u001b[0m             )\n\u001b[1;32m--> 146\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg_err)\n\u001b[0;32m    148\u001b[0m \u001b[39m# for object dtype data, we only check for NaNs (GH-13254)\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[39melif\u001b[39;00m X\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mdtype(\u001b[39m\"\u001b[39m\u001b[39mobject\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m allow_nan:\n","\u001b[1;31mValueError\u001b[0m: Input X contains NaN.\nSelectKBest does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values"]}],"source":["from sklearn.feature_selection import SelectKBest\n","from sklearn.feature_selection import chi2\n","\n","# Select the columns to use as predictors\n","# data = pd.read_csv(\"combined_csv.csv\")\n","# data = pd.get_dummies(data, columns=['Date'])\n","\n","X = data[[ 'Lat', 'Long_', 'Confirmed','FIPS', 'Incident_Rate', 'Total_Test_Results', 'Testing_Rate', 'Case_Fatality_Ratio','Hospitalization_Rate', 'People_Tested']]\n","\n","# Select the target column\n","Y = data['Deaths']\n","# Apply Univariate Feature Selection\n","selector = SelectKBest(score_func=chi2, k=10)\n","fit = selector.fit(X, Y)\n","\n","# Summarize scores\n","np.set_printoptions(precision=3)\n","print(fit.scores_)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["The error message is indicating that the input data, \"X\", contains missing values encoded as NaN. The Univariate Feature Selection method from the scikit-learn library does not accept missing values. You have a few options to resolve this issue:\n","\n","Use an imputer transformer to preprocess the data and replace the missing values with some numerical value (such as the mean or median).\n","\n","Drop the samples that contain missing values.\n","\n","Use a different machine learning algorithm, such as HistGradientBoostingClassifier or Regressor, that can handle missing values encoded as NaNs.\n","\n","For more information and examples, you can refer to the scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# Compute the correlation matrix\n","corr = data.corr()\n","\n","# Generate a mask for the upper triangle\n","mask = np.triu(np.ones_like(corr, dtype=bool))\n","\n","# Plot the heatmap\n","fig, ax = plt.subplots(figsize=(10,10))\n","sns.heatmap(corr, mask=mask, cmap='coolwarm', annot=True, ax=ax)\n","\n","plt.show()"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"text/plain":["Index(['Country_Region', 'Last_Update', 'Lat', 'Long_', 'Confirmed', 'Deaths',\n","       'Recovered', 'Active', 'FIPS', 'Incident_Rate', 'Total_Test_Results',\n","       'People_Hospitalized', 'Case_Fatality_Ratio', 'UID', 'ISO3',\n","       'Testing_Rate', 'Hospitalization_Rate', 'Date', 'People_Tested',\n","       'Mortality_Rate', 'Province_State_Alabama', 'Province_State_Alaska',\n","       'Province_State_American Samoa', 'Province_State_Arizona',\n","       'Province_State_Arkansas', 'Province_State_California',\n","       'Province_State_Colorado', 'Province_State_Connecticut',\n","       'Province_State_Delaware', 'Province_State_Diamond Princess',\n","       'Province_State_District of Columbia', 'Province_State_Florida',\n","       'Province_State_Georgia', 'Province_State_Grand Princess',\n","       'Province_State_Guam', 'Province_State_Hawaii', 'Province_State_Idaho',\n","       'Province_State_Illinois', 'Province_State_Indiana',\n","       'Province_State_Iowa', 'Province_State_Kansas',\n","       'Province_State_Kentucky', 'Province_State_Louisiana',\n","       'Province_State_Maine', 'Province_State_Maryland',\n","       'Province_State_Massachusetts', 'Province_State_Michigan',\n","       'Province_State_Minnesota', 'Province_State_Mississippi',\n","       'Province_State_Missouri', 'Province_State_Montana',\n","       'Province_State_Nebraska', 'Province_State_Nevada',\n","       'Province_State_New Hampshire', 'Province_State_New Jersey',\n","       'Province_State_New Mexico', 'Province_State_New York',\n","       'Province_State_North Carolina', 'Province_State_North Dakota',\n","       'Province_State_Northern Mariana Islands', 'Province_State_Ohio',\n","       'Province_State_Oklahoma', 'Province_State_Oregon',\n","       'Province_State_Pennsylvania', 'Province_State_Puerto Rico',\n","       'Province_State_Recovered', 'Province_State_Rhode Island',\n","       'Province_State_South Carolina', 'Province_State_South Dakota',\n","       'Province_State_Tennessee', 'Province_State_Texas',\n","       'Province_State_Utah', 'Province_State_Vermont',\n","       'Province_State_Virgin Islands', 'Province_State_Virginia',\n","       'Province_State_Washington', 'Province_State_West Virginia',\n","       'Province_State_Wisconsin', 'Province_State_Wyoming'],\n","      dtype='object')"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["data.columns"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"ename":"ValueError","evalue":"could not convert string to float: '2021-01-01'","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[1;32mIn [18], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[39m# Apply Univariate Feature Selection\u001b[39;00m\n\u001b[0;32m     13\u001b[0m selector \u001b[39m=\u001b[39m SelectKBest(score_func\u001b[39m=\u001b[39mchi2, k\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n\u001b[1;32m---> 14\u001b[0m fit \u001b[39m=\u001b[39m selector\u001b[39m.\u001b[39;49mfit(X, Y)\n\u001b[0;32m     16\u001b[0m \u001b[39m# Summarize scores\u001b[39;00m\n\u001b[0;32m     17\u001b[0m np\u001b[39m.\u001b[39mset_printoptions(precision\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m)\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:461\u001b[0m, in \u001b[0;36m_BaseFilter.fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    444\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y):\n\u001b[0;32m    445\u001b[0m     \u001b[39m\"\"\"Run score function on (X, y) and get the appropriate features.\u001b[39;00m\n\u001b[0;32m    446\u001b[0m \n\u001b[0;32m    447\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    459\u001b[0m \u001b[39m        Returns the instance itself.\u001b[39;00m\n\u001b[0;32m    460\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 461\u001b[0m     X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m    462\u001b[0m         X, y, accept_sparse\u001b[39m=\u001b[39;49m[\u001b[39m\"\u001b[39;49m\u001b[39mcsr\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mcsc\u001b[39;49m\u001b[39m\"\u001b[39;49m], multi_output\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[0;32m    463\u001b[0m     )\n\u001b[0;32m    465\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m callable(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_func):\n\u001b[0;32m    466\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[0;32m    467\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mThe score function should be a callable, \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m (\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m) was passed.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    468\u001b[0m             \u001b[39m%\u001b[39m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_func, \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_func))\n\u001b[0;32m    469\u001b[0m         )\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:596\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    594\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[0;32m    595\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 596\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n\u001b[0;32m    597\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[0;32m    599\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:1074\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1069\u001b[0m         estimator_name \u001b[39m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1070\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m   1071\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m requires y to be passed, but the target y is None\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1072\u001b[0m     )\n\u001b[1;32m-> 1074\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[0;32m   1075\u001b[0m     X,\n\u001b[0;32m   1076\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[0;32m   1077\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[0;32m   1078\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[0;32m   1079\u001b[0m     order\u001b[39m=\u001b[39;49morder,\n\u001b[0;32m   1080\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[0;32m   1081\u001b[0m     force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[0;32m   1082\u001b[0m     ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[0;32m   1083\u001b[0m     allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[0;32m   1084\u001b[0m     ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[0;32m   1085\u001b[0m     ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[0;32m   1086\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[0;32m   1087\u001b[0m     input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m   1088\u001b[0m )\n\u001b[0;32m   1090\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[0;32m   1092\u001b[0m check_consistent_length(X, y)\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:856\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    854\u001b[0m         array \u001b[39m=\u001b[39m array\u001b[39m.\u001b[39mastype(dtype, casting\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39munsafe\u001b[39m\u001b[39m\"\u001b[39m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m    855\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 856\u001b[0m         array \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49masarray(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype)\n\u001b[0;32m    857\u001b[0m \u001b[39mexcept\u001b[39;00m ComplexWarning \u001b[39mas\u001b[39;00m complex_warning:\n\u001b[0;32m    858\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    859\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mComplex data not supported\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[0;32m    860\u001b[0m     ) \u001b[39mfrom\u001b[39;00m \u001b[39mcomplex_warning\u001b[39;00m\n","File \u001b[1;32mc:\\Users\\ailaty\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:2069\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2068\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array__\u001b[39m(\u001b[39mself\u001b[39m, dtype: npt\u001b[39m.\u001b[39mDTypeLike \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m np\u001b[39m.\u001b[39mndarray:\n\u001b[1;32m-> 2069\u001b[0m     \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49masarray(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_values, dtype\u001b[39m=\u001b[39;49mdtype)\n","\u001b[1;31mValueError\u001b[0m: could not convert string to float: '2021-01-01'"]}],"source":[]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Using linear regression for feature selection:"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[{"ename":"ValueError","evalue":"Input X contains NaN.\nLinearRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[1;32m<ipython-input-30-ae625654175b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# Create and fit a linear regression model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mlr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# Use SelectFromModel to select features with coefficients greater than a threshold\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    646\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"coo\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 648\u001b[1;33m         X, y = self._validate_data(\n\u001b[0m\u001b[0;32m    649\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    650\u001b[0m         )\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    563\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 565\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    566\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1104\u001b[0m         )\n\u001b[0;32m   1105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1106\u001b[1;33m     X = check_array(\n\u001b[0m\u001b[0;32m   1107\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 921\u001b[1;33m             _assert_all_finite(\n\u001b[0m\u001b[0;32m    922\u001b[0m                 \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    923\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;32mc:\\Users\\Arshia\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[0;32m    159\u001b[0m                 \u001b[1;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    160\u001b[0m             )\n\u001b[1;32m--> 161\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    162\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;31mValueError\u001b[0m: Input X contains NaN.\nLinearRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values"]}],"source":["from sklearn.linear_model import LinearRegression\n","from sklearn.feature_selection import SelectFromModel\n","\n","X = data[['Lat', 'Long_', 'Confirmed', 'FIPS', 'Incident_Rate', 'Total_Test_Results', \n","          'Testing_Rate', 'Case_Fatality_Ratio','Hospitalization_Rate', 'People_Tested']]\n","Y = data['Deaths']\n","\n","# Create and fit a linear regression model\n","lr = LinearRegression().fit(X, Y)\n","\n","# Use SelectFromModel to select features with coefficients greater than a threshold\n","sfm = SelectFromModel(lr, threshold=0.01)\n","X_new = sfm.transform(X)\n","\n","# Print the number of features selected\n","print(\"Number of features selected:\", X_new.shape[1])\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["The problem is that you are using LogisticRegression as your model, but it seems that you are trying to do regression and not classification, as the target variable \"Deaths\" is a continuous variable. To resolve this issue, you can use a regression model such as Linear Regression or Random Forest Regressor instead."]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"__init__() takes 2 positional arguments but 3 were given","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[1;32m<ipython-input-29-b770233a8547>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# Apply Recursive Feature Elimination\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mrfe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRFE\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mfit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrfe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n","\u001b[1;31mTypeError\u001b[0m: __init__() takes 2 positional arguments but 3 were given"]}],"source":["from sklearn.feature_selection import RFE\n","from sklearn.linear_model import LogisticRegression\n","\n","\n","X = data[[ 'Lat', 'Long_', 'Confirmed','FIPS', 'Incident_Rate', 'Total_Test_Results', 'Testing_Rate', 'Case_Fatality_Ratio','Hospitalization_Rate', 'People_Tested']]\n","\n","# Select the target column\n","Y = data['Deaths']\n","# Apply Recursive Feature Elimination\n","model = LogisticRegression()\n","rfe = RFE(model, 5)\n","fit = rfe.fit(X, Y)\n","\n","# Summarize selected features\n","print(\"Num Features: %d\" % fit.n_features_)\n","print(\"Selected Features: %s\" % fit.support_)\n","print(\"Feature Ranking: %s\" % fit.ranking_)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Using random forest for feature selection:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sklearn.ensemble import RandomForestRegressor\n","from sklearn.feature_selection import SelectFromModel\n","\n","X = data[['Lat', 'Long_', 'Confirmed', 'FIPS', 'Incident_Rate', 'Total_Test_Results', \n","          'Testing_Rate', 'Case_Fatality_Ratio','Hospitalization_Rate', 'People_Tested']]\n","Y = data['Deaths']\n","\n","# Create and fit a random forest regressor\n","rf = RandomForestRegressor().fit(X, Y)\n","\n","# Use SelectFromModel to select features with importance greater than a threshold\n","sfm = SelectFromModel(rf, threshold=0.01)\n","X_new = sfm.transform(X)\n","\n","# Print the number of features selected\n","print(\"Number of features selected:\", X_new.shape[1])\n"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":133938,"status":"ok","timestamp":1675026956633,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"zqtoVHLNYJSH","outputId":"9e9dfec1-c8a0-48a5-fe6b-4e3b25228434"},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f42baa546d0>"]},"execution_count":10,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.lineplot(data=data, x='Confirmed', y='Deaths')"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":311},"executionInfo":{"elapsed":281,"status":"error","timestamp":1675027135310,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"cRYsBxk9YoRD","outputId":"bc10ea81-2ca5-4523-e8e1-975dd7d5cef0"},"outputs":[{"ename":"ValueError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-12-8262e5ea0b15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Confirmed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Deaths'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m 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x_bins, y_bins, units, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m     \u001b[0mvariables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ScatterPlotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 808\u001b[0;31m     p = _ScatterPlotter(\n\u001b[0m\u001b[1;32m    809\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m         \u001b[0mx_bins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_bins\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    603\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;34m\"long\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m             plot_data, variables = self._assign_variables_longform(\n\u001b[0m\u001b[1;32m    669\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m             )\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m_assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m                 \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret value `{val}` for parameter `{key}`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Could not interpret value `time` for parameter `hue`"]}],"source":["sns.scatterplot(data=data, x='Confirmed', y='Deaths', hue='time')"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"elapsed":910,"status":"ok","timestamp":1675026731841,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"fZwoYbZkXzeu","outputId":"4356064a-7f86-49eb-ecf0-5b1d7b6bc17c"},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f42baf45ac0>"]},"execution_count":9,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["data['Confirmed'].plot.line()"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"elapsed":1334,"status":"ok","timestamp":1675026624867,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"ibQHoBHcXF3q","outputId":"350f77d9-acae-4242-eaa7-bd792ebdea9e"},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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legend_name='Confirmed Cases'\n",").add_to(map)\n","\n","# Display the map\n","map.save(\"index.html\")\n","map\n"]},{"cell_type":"markdown","metadata":{"id":"cpWcw0XnIe2m"},"source":["If there are duplicate rows, you can drop them using the drop_duplicates() function.\n","\n","df.drop_duplicates(inplace=True)"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":123,"status":"ok","timestamp":1674961567582,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"KeTEk5fDIe2n","outputId":"dbc5c637-ffa5-465b-e16e-449ae7c1cc71"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-5ef1fca2-7ece-4c8f-b13a-d32c7a119716\">\n","    <div class=\"colab-df-container\">\n","      <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>Province_State</th>\n","      <th>Confirmed</th>\n","      <th>Deaths</th>\n","      <th>Recovered</th>\n","      <th>Active</th>\n","      <th>FIPS</th>\n","      <th>Incident_Rate</th>\n","      <th>Total_Test_Results</th>\n","      <th>Case_Fatality_Ratio</th>\n","      <th>UID</th>\n","      <th>ISO3</th>\n","      <th>Testing_Rate</th>\n","      <th>Date</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alabama</td>\n","      <td>365747</td>\n","      <td>4872</td>\n","      <td>202137.0</td>\n","      <td>158738.0</td>\n","      <td>1.0</td>\n","      <td>7459.375895</td>\n","      <td>NaN</td>\n","      <td>1.332068</td>\n","      <td>84000001.0</td>\n","      <td>USA</td>\n","      <td>NaN</td>\n","      <td>2021-01-01</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Alaska</td>\n","      <td>47019</td>\n","      <td>206</td>\n","      <td>7165.0</td>\n","      <td>39648.0</td>\n","      <td>2.0</td>\n","      <td>6427.355802</td>\n","      <td>1275750.0</td>\n","      <td>0.438121</td>\n","      <td>84000002.0</td>\n","      <td>USA</td>\n","      <td>174391.185778</td>\n","      <td>2021-01-01</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>American Samoa</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>60.0</td>\n","      <td>0.000000</td>\n","      <td>2140.0</td>\n","      <td>NaN</td>\n","      <td>16.0</td>\n","      <td>ASM</td>\n","      <td>3846.084722</td>\n","      <td>2021-01-01</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Arizona</td>\n","      <td>530267</td>\n","      <td>9015</td>\n","      <td>76934.0</td>\n","      <td>444318.0</td>\n","      <td>4.0</td>\n","      <td>7285.171274</td>\n","      <td>5155330.0</td>\n","      <td>1.700087</td>\n","      <td>84000004.0</td>\n","      <td>USA</td>\n","      <td>39551.860582</td>\n","      <td>2021-01-01</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Arkansas</td>\n","      <td>229442</td>\n","      <td>3711</td>\n","      <td>199247.0</td>\n","      <td>26484.0</td>\n","      <td>5.0</td>\n","      <td>7602.945718</td>\n","      <td>2051488.0</td>\n","      <td>1.617402</td>\n","      <td>84000005.0</td>\n","      <td>USA</td>\n","      <td>67979.497674</td>\n","      <td>2021-01-01</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ef1fca2-7ece-4c8f-b13a-d32c7a119716')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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3711   199247.0   26484.0   5.0   \n","\n","   Incident_Rate  Total_Test_Results  Case_Fatality_Ratio         UID ISO3  \\\n","0    7459.375895                 NaN             1.332068  84000001.0  USA   \n","1    6427.355802           1275750.0             0.438121  84000002.0  USA   \n","2       0.000000              2140.0                  NaN        16.0  ASM   \n","3    7285.171274           5155330.0             1.700087  84000004.0  USA   \n","4    7602.945718           2051488.0             1.617402  84000005.0  USA   \n","\n","    Testing_Rate        Date  \n","0            NaN  2021-01-01  \n","1  174391.185778  2021-01-01  \n","2    3846.084722  2021-01-01  \n","3   39551.860582  2021-01-01  \n","4   67979.497674  2021-01-01  "]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["data.drop('Country_Region', axis=1, inplace=True)\n","data.drop('Last_Update',axis=1, inplace=True)\n","data.drop('Lat', axis=1, inplace=True)\n","data.drop('Long_', axis=1, inplace=True)\n","data.drop('People_Hospitalized', axis=1, inplace=True)\n","data.drop('Hospitalization_Rate', axis=1, inplace=True)\n","data.drop('People_Tested', axis=1, inplace=True)\n","data.drop('Mortality_Rate', axis=1, inplace=True)\n","data.head()"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":118,"status":"ok","timestamp":1674961567583,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"MpwKVDK2Ie2o","outputId":"dcbdf521-dfc9-4346-bae6-58567427268e"},"outputs":[{"name":"stdout","output_type":"stream","text":["['Alabama' 'Alaska' 'American Samoa' 'Arizona' 'Arkansas' 'California'\n"," 'Colorado' 'Connecticut' 'Delaware' 'Diamond Princess'\n"," 'District of Columbia' 'Florida' 'Georgia' 'Grand Princess' 'Guam'\n"," 'Hawaii' 'Idaho' 'Illinois' 'Indiana' 'Iowa' 'Kansas' 'Kentucky'\n"," 'Louisiana' 'Maine' 'Maryland' 'Massachusetts' 'Michigan' 'Minnesota'\n"," 'Mississippi' 'Missouri' 'Montana' 'Nebraska' 'Nevada' 'New Hampshire'\n"," 'New Jersey' 'New Mexico' 'New York' 'North Carolina' 'North Dakota'\n"," 'Northern Mariana Islands' 'Ohio' 'Oklahoma' 'Oregon' 'Pennsylvania'\n"," 'Puerto Rico' 'Rhode Island' 'South Carolina' 'South Dakota' 'Tennessee'\n"," 'Texas' 'Utah' 'Vermont' 'Virgin Islands' 'Virginia' 'Washington'\n"," 'West Virginia' 'Wisconsin' 'Wyoming' 'Recovered']\n"]}],"source":["print(data['Province_State'].unique())"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":290},"executionInfo":{"elapsed":114,"status":"ok","timestamp":1674961567584,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"kW9pODpIIe2p","outputId":"16f68dd8-d497-4e70-f5ab-102a06989f11"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["data[\"Date\"] = pd.to_datetime(data[\"Date\"])\n","data1 = data.groupby(\"Date\").sum()\n","\n","# Create the line plot\n","plt.plot(data1[\"Confirmed\"], label=\"Confirmed Cases\")\n","plt.plot(data1[\"Deaths\"], label=\"Deaths\")\n","plt.plot(data1[\"Recovered\"], label=\"Recoveries\")\n","plt.xlabel(\"Date\")\n","plt.ylabel(\"Count\")\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":738},"executionInfo":{"elapsed":114,"status":"ok","timestamp":1674961567585,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"enhRw4zHIe2p","outputId":"f5d82552-ccc9-4aea-ccb1-a8317aa45024"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1440x720 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["data = data.groupby('Province_State').Confirmed.sum()\n","\n","# Create a figure with a width of 20 and a height of 10\n","plt.figure(figsize=(20, 10))\n","# Create a bar plot\n","plt.bar(data.index, data.values)\n","plt.xlabel('States')\n","plt.ylabel('Total Confirmed Cases')\n","plt.title('Total Confirmed Cases by States')\n","plt.xticks(rotation=90)\n","plt.show()"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":113,"status":"ok","timestamp":1674961567586,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"vsy45MlgUYEm","outputId":"d0a75b6c-061e-4341-ee6d-7076cef6b5e6"},"outputs":[{"data":{"text/plain":["Index(['Confirmed'], dtype='object')"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["data = data.to_frame()\n","data.columns"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"executionInfo":{"elapsed":6560,"status":"ok","timestamp":1674961574038,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"zmmgOljpIe2q","outputId":"8ef2b94a-507a-471a-900d-2dbf74c81424"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["data = pd.read_csv(\"/content/drive/My Drive/CS 631 Introduction to Big Data/New folder/combined_csv.csv\")\n","\n","# Convert 'Date' column to datetime\n","data[\"Date\"] = pd.to_datetime(data[\"Date\"])\n","\n","# Create pivot table with rows as Province_State, columns as 'Date' and values as 'Confirmed'\n","data_pivot = data.pivot_table(values='Confirmed', index='Province_State', columns='Date')\n","\n","# Create heatmap\n","sns.heatmap(data_pivot, cmap='YlGnBu')\n","\n","# Show the plot\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["train_data=data.iloc[:int(len(data)*0.8)] \n","test_data=data.iloc[int(len(data)*0.8):]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def train_test_split(data):\n","    size=int(len(data)*0.8)\n","    # for train data will be collected from each country's data which index is from 0-size (80%)\n","    x_train =data.drop(columns=['TargetValue']).iloc[0:size] \n","    # for test data will be collected from each country's  data which index is from size to the end (20%)\n","    x_test = data.drop(columns=['TargetValue']).iloc[size:]\n","    y_train=data['TargetValue'].iloc[0:size] \n","    y_test=data['TargetValue'].iloc[size:] \n","    return x_train, x_test,y_train,y_test\n","# unique countries\n","country=list(set(data.country))\n","# loop each country_Region and split the data into train and test data\n","X_train=[]\n","X_test=[]\n","Y_train=[]\n","Y_test=[]\n","for i in range(0,len(country)):\n","    df=data[['country']==country[i]]\n","    # applied the function I created above\n","    x_train, x_test,y_train,y_test=train_test_split(df)\n","    X_train.append(x_train)\n","    X_test.append(x_test)\n","    Y_train.append(y_train)\n","    Y_test.append(y_test)\n","# concatenate each train data n X_train list and Y_train list respectively\n","X_train=pd.concat(X_train)\n","Y_train=pd.DataFrame(pd.concat(Y_train))\n","# concatenate each test dataset in X_test list and Y_test list respectively\n","X_test=pd.concat(X_test)\n","Y_test=pd.DataFrame(pd.concat(Y_test))"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["The function below is to convert series to supervised learning."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n","    n_vars = 1 if type(data) is list else data.shape[1]\n","    df = DataFrame(data)\n","    cols, names = list(), list()\n","    # input sequence (t-n, ... t-1)\n","    for i in range(n_in, 0, -1):\n","        cols.append(df.shift(i))\n","        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n","    # forecast sequence (t, t+1, ... t+n)\n","    for i in range(0, n_out):\n","        cols.append(df.shift(-i))\n","        if i == 0:\n","            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n","        else:\n","            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n","    # put it all together\n","    agg = concat(cols, axis=1)\n","    agg.columns = names\n","    # drop rows with NaN values\n","    if dropnan:\n","        agg.dropna(inplace=True)\n","    return agg"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def reshape_data(train,test):\n","    #Frame as supervised learning and drop all time t columns except\n","    reframed_train = series_to_supervised(train, 1, 1)\n","    reframed_test = series_to_supervised(test, 1, 1)\n","    # split into train and test sets\n","    train= reframed_train.values\n","    test=reframed_test.values\n","    # split into input and outputs\n","    train_X, y_train = train[:, :-1], train[:, -1]\n","    test_X, y_test = test[:, :-1], test[:, -1]\n","    # reshape input to be 3D [samples, timesteps, features]\n","    x_train = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n","    x_test = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))\n","    return x_train,x_test,y_train,y_test"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["encoder = LabelEncoder()\n","#combine X train and Y train as train data \n","train_data=pd.DataFrame()\n","train_data[X_train.columns]=X_train\n","train_data[Y_train.columns]=Y_train\n","train_data['country']= encoder.fit_transform(train_data['country'])\n","#combine X test and Y test as test data \n","test_data=pd.DataFrame()\n","test_data[X_test.columns]=X_test\n","test_data[Y_test.columns]=Y_test\n","test_data['country']= encoder.fit_transform(test_data['country'])\n","# using the function to obtian reshaped data \n","x_train,x_test,y_train,y_test=reshape_data(train_data,test_data)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# design network for confirmed cases data \n","model = Sequential()\n","model.add(LSTM(60, activation='relu',input_shape=(x_train.shape[1], x_train.shape[2])))\n","\n","model.add(Dense(1))\n","model.compile(loss='mae', optimizer='adam')\n","# fit network\n","history = model.fit(x_train, y_train, epochs=30, batch_size=50,  verbose=1, shuffle=False)"]},{"cell_type":"code","execution_count":32,"metadata":{"executionInfo":{"elapsed":67,"status":"ok","timestamp":1674961574039,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"wxxBPlD7XmhR"},"outputs":[],"source":["from sklearn.preprocessing import MinMaxScaler\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM\n","\n","# Load the data and keep only the 'Confirmed' column\n","data = pd.read_csv(\"/content/drive/My Drive/CS 631 Introduction to Big Data/New folder/combined_csv.csv\")\n","data = data[['Confirmed']]\n","\n","# Scale the data between 0 and 1\n","scaler = MinMaxScaler(feature_range=(0, 1))\n","data = scaler.fit_transform(data)"]},{"cell_type":"code","execution_count":33,"metadata":{"executionInfo":{"elapsed":67,"status":"ok","timestamp":1674961574040,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"hoi5u8z_bFul"},"outputs":[],"source":["# Split the data into training and testing sets\n","train_size = int(len(data) * 0.67)\n","test_size = len(data) - train_size\n","train, test = data[0:train_size,:], data[train_size:len(data),:]"]},{"cell_type":"code","execution_count":34,"metadata":{"executionInfo":{"elapsed":69,"status":"ok","timestamp":1674961574043,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"xyia-Bu1bGb3"},"outputs":[],"source":["# Function to create a time-series dataset\n","def create_dataset(data, look_back=1):\n","    X, y = [], []\n","    for i in range(len(data)-look_back-1):\n","        a = data[i:(i+look_back), 0]\n","        X.append(a)\n","        y.append(data[i + look_back, 0])\n","    return np.array(X), np.array(y)"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":71,"status":"ok","timestamp":1674961574045,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"kDB7HTekbG7i"},"outputs":[],"source":["# Use the function to create time-series dataset\n","look_back = 1\n","trainX, trainY = create_dataset(train, look_back)\n","testX, testY = create_dataset(test, look_back)"]},{"cell_type":"code","execution_count":36,"metadata":{"executionInfo":{"elapsed":73,"status":"ok","timestamp":1674961574047,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"gTvl3R_ebPpG"},"outputs":[],"source":["# Reshape the input data for LSTM\n","trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n","testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"]},{"cell_type":"code","execution_count":37,"metadata":{"executionInfo":{"elapsed":73,"status":"ok","timestamp":1674961574048,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"yrH7hn_ZbT_r"},"outputs":[],"source":["# Create an LSTM model\n","model = Sequential()\n","model.add(LSTM(4, input_shape=(1, look_back)))\n","model.add(Dense(1))\n","model.compile(loss='mean_squared_error', optimizer='adam')"]},{"cell_type":"markdown","metadata":{"id":"g4HdV6PKg3To"},"source":["You can track the train loss and validation loss using the callback functions in Keras. The EarlyStopping callback is a commonly used method to monitor the validation loss and stop the training process if the validation loss has not improved after a specified number of epochs."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cyYMoT0Gg5NO"},"outputs":[],"source":["# from keras.callbacks import EarlyStopping\n","\n","# early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n","\n","# history = model.fit(X_train, y_train, validation_data=(X_val, y_val), \n","#           epochs=100, batch_size=32, callbacks=[early_stopping])"]},{"cell_type":"markdown","metadata":{"id":"zGfbpP0yhBNg"},"source":["The monitor argument is set to 'val_loss' to monitor the validation loss, and patience argument is set to 10, meaning that training will stop after 10 epochs if the validation loss does not improve. The history object returned by the fit function can then be used to plot the train and validation loss over the epochs \n","to see the trend.\n","\n","\n","---\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2168314,"status":"ok","timestamp":1674966995987,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"O_xmWHpxbUhN","outputId":"91a552d2-f989-4b7f-b7b2-03548fa74bd1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","39608/39608 - 56s - loss: 0.0125 - 56s/epoch - 1ms/step\n","Epoch 2/100\n","39608/39608 - 58s - loss: 0.0124 - 58s/epoch - 1ms/step\n","Epoch 3/100\n","39608/39608 - 57s - loss: 0.0123 - 57s/epoch - 1ms/step\n","Epoch 4/100\n","39608/39608 - 56s - loss: 0.0123 - 56s/epoch - 1ms/step\n","Epoch 5/100\n","39608/39608 - 54s - loss: 0.0123 - 54s/epoch - 1ms/step\n","Epoch 6/100\n","39608/39608 - 54s - loss: 0.0123 - 54s/epoch - 1ms/step\n","Epoch 7/100\n","39608/39608 - 54s - loss: 0.0122 - 54s/epoch - 1ms/step\n","Epoch 8/100\n","39608/39608 - 54s - loss: 0.0122 - 54s/epoch - 1ms/step\n","Epoch 9/100\n","39608/39608 - 54s - loss: 0.0122 - 54s/epoch - 1ms/step\n","Epoch 10/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 11/100\n","39608/39608 - 55s - loss: 0.0121 - 55s/epoch - 1ms/step\n","Epoch 12/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 13/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 14/100\n","39608/39608 - 60s - loss: 0.0121 - 60s/epoch - 2ms/step\n","Epoch 15/100\n","39608/39608 - 61s - loss: 0.0121 - 61s/epoch - 2ms/step\n","Epoch 16/100\n","39608/39608 - 61s - loss: 0.0121 - 61s/epoch - 2ms/step\n","Epoch 17/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 18/100\n","39608/39608 - 58s - loss: 0.0121 - 58s/epoch - 1ms/step\n","Epoch 19/100\n","39608/39608 - 55s - loss: 0.0121 - 55s/epoch - 1ms/step\n","Epoch 20/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 21/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 22/100\n","39608/39608 - 56s - loss: 0.0121 - 56s/epoch - 1ms/step\n","Epoch 23/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 24/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 25/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 26/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 27/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 28/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 29/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 30/100\n","39608/39608 - 57s - loss: 0.0121 - 57s/epoch - 1ms/step\n","Epoch 31/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 32/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 33/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 34/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 35/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 36/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 37/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 38/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 39/100\n","39608/39608 - 55s - loss: 0.0121 - 55s/epoch - 1ms/step\n","Epoch 40/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 41/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 42/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 43/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 44/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 45/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 46/100\n","39608/39608 - 54s - loss: 0.0121 - 54s/epoch - 1ms/step\n","Epoch 47/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 48/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 49/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 50/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 51/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 52/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 53/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 54/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 55/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 56/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 57/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 58/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 59/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 60/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 61/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 62/100\n","39608/39608 - 55s - loss: 0.0120 - 55s/epoch - 1ms/step\n","Epoch 63/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 64/100\n","39608/39608 - 53s - loss: 0.0120 - 53s/epoch - 1ms/step\n","Epoch 65/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 66/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 67/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 68/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 69/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 70/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 71/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 72/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 73/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 74/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 75/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 76/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 77/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 78/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 79/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 80/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 81/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 82/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 83/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 84/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 85/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 86/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 87/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 88/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 89/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 90/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 91/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 92/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 93/100\n","39608/39608 - 53s - loss: 0.0120 - 53s/epoch - 1ms/step\n","Epoch 94/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 95/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 96/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 97/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 98/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 99/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n","Epoch 100/100\n","39608/39608 - 54s - loss: 0.0120 - 54s/epoch - 1ms/step\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f200c48d400>"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["# Fit the model to the training data\n","model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4657,"status":"ok","timestamp":1674967000643,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"TLLnbY6vbU_4","outputId":"2e00ffc9-5031-4334-da02-44b9a0e81c0a"},"outputs":[{"name":"stdout","output_type":"stream","text":["1238/1238 [==============================] - 3s 1ms/step\n","610/610 [==============================] - 1s 1ms/step\n"]}],"source":["# Predict on the test data\n","trainPredict = model.predict(trainX)\n","testPredict = model.predict(testX)"]},{"cell_type":"code","execution_count":40,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1674967000646,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"1THzQ2EnbgO7"},"outputs":[],"source":["# Inverse transform the predictions to original scale\n","trainPredict = scaler.inverse_transform(trainPredict)\n","trainY = scaler.inverse_transform([trainY])\n","testPredict = scaler.inverse_transform(testPredict)\n","testY = scaler.inverse_transform([testY])"]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":380},"executionInfo":{"elapsed":246,"status":"error","timestamp":1674971158382,"user":{"displayName":"Arshia Ilaty","userId":"18300153136049904114"},"user_tz":480},"id":"_rgEZkcdbgnh","outputId":"01d8c50d-588e-4d3b-bf3d-ec42b2424cfb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Train Score: 1311944.64 RMSE\n"]},{"ename":"ValueError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-43-d896c8a3c415>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtrainScore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainPredict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Train Score: %.2f RMSE'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrainScore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtestScore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestPredict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test Score: %.2f RMSE'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtestScore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[0;36m0.825\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m     \"\"\"\n\u001b[0;32m--> 438\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    439\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m     )\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0mthe\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0margument\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \"\"\"\n\u001b[0;32m---> 94\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    330\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    333\u001b[0m             \u001b[0;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;34m%\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [1, 19508]"]}],"source":["from sklearn.metrics import mean_squared_error\n","# Evaluate the model using mean squared error\n","trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n","print('Train Score: %.2f RMSE' % (trainScore))\n","testScore = np.sqrt(mean_squared_error(testY, testPredict[:,0]))\n","print('Test Score: %.2f RMSE' % (testScore))"]},{"cell_type":"markdown","metadata":{"id":"JZ1EF4NUc1CB"},"source":["LSTMs are commonly used for time series forecasting, especially for datasets with long-term dependencies. However, there are other machine learning models that may perform better for a given problem, such as ARIMA, SARIMA, Prophet, and Random Forest. The choice of the model depends on the nature of the data and the problem being solved. It is important to try multiple models, tune their hyperparameters, and compare their performance to find the best model for a given problem."]},{"cell_type":"markdown","metadata":{"id":"ng5D7J3hf0Zb"},"source":["ARIMA (AutoRegressive Integrated Moving Average) is a popular time series forecasting model used for univariate time series data, i.e., where the data has only one variable. ARIMA models the relationship between the current observation and a linear combination of previous observations and residual errors, and it models the relationships between observations and their differences (integrated) over time.\n","\n","SARIMA (Seasonal AutoRegressive Integrated Moving Average) is an extension of ARIMA that considers seasonality in time series data. It takes into account the repeating patterns in the data, such as daily, weekly, or yearly patterns. The seasonal component of the model is defined by the seasonal order, which consists of the seasonal autoregression (SAR), seasonal differences (I), and seasonal moving average (SMA) parameters.\n","\n","Prophet is a time series forecasting model developed by Facebook. It is designed to be flexible and easy to use, and it is suitable for time series with linear or non-linear growth patterns, as well as for time series with multiple seasonality. Prophet is a decomposable time series model, which means that it separates the time series into its trend, seasonality, and holiday components. The model can also incorporate additional information such as holidays and events, which can be used to improve the forecast accuracy.\n","\n","In terms of comparison, ARIMA and SARIMA are classical time series models that are good for modeling univariate time series data. They are appropriate for data with linear or non-linear trends and with or without seasonality. Prophet is a newer model that is specifically designed to be flexible and easy to use, and it is suitable for time series with multiple seasonality. The choice of the best model depends on the nature of the time series data and the problem requirements. It's recommended to experiment with different models and select the one that provides the best performance for your particular time series data."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"us4macE8iK0z"},"outputs":[],"source":["from statsmodels.tsa.statespace.sarimax import SARIMAX\n","\n","# Load the dataset\n","data = pd.read_csv(\"/content/drive/My Drive/CS 631 Introduction to Big Data/New folder/combined_csv.csv\")\n","\n","# Sort data by date\n","data.sort_values(by='Date', inplace=True)\n","\n","# Convert 'Date' column to datetime\n","data['Date'] = pd.to_datetime(data['Date'])\n","\n","# Set 'Date' as the index of the dataframe\n","data.set_index('Date', inplace=True)\n","\n","# Extract the target variable 'Confirmed'\n","y = data['Confirmed']\n","\n","# Split the data into training and testing sets\n","train = y[:int(0.8*(len(y)))]\n","test = y[int(0.8*(len(y))):]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YFdyBSOlickJ"},"outputs":[],"source":["# Train the SARIMA model\n","model = SARIMAX(train, order=(1,1,1), seasonal_order=(1,1,1,12))\n","results = model.fit()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bMwE19_0ieYM"},"outputs":[],"source":["# Generate predictions for the test set\n","predictions = results.predict(start=test.index[0], end=test.index[-1], typ='levels')\n"]},{"cell_type":"markdown","metadata":{"id":"zQ95UH2XDWxY"},"source":["sklearn >= 0.22.0\n","\n","sklearn.metrics has a mean_squared_error function with a squared kwarg (defaults to True). \n","Setting squared to False will return the RMSE.\n","\n","from sklearn.metrics import mean_squared_error\n","\n","rms = mean_squared_error(y_actual, y_predicted, squared=False)\n","\n","sklearn < 0.22.0\n","\n","sklearn.metrics has a mean_squared_error function. The RMSE is just the square root of whatever it returns.\n","\n","from sklearn.metrics import mean_squared_error\n","\n","from math import sqrt\n","\n","rms = sqrt(mean_squared_error(y_actual, y_predicted))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GqvpkoV-lZeC"},"outputs":[],"source":["from sklearn.metrics import mean_squared_error \n","from math import sqrt\n","# Calculate RMSE for SARIMA\n","rmse = sqrt(mean_squared_error(test_data, predictions))\n","print(\"SARIMA RMSE: \", rmse)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZiH0-xQQii5g"},"outputs":[],"source":["# Plot the actual vs predicted values\n","plt.plot(test.values, label='actual')\n","plt.plot(predictions, label='predicted')\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DqgCxcczoRuk"},"outputs":[],"source":["from fbprophet import Prophet\n","\n","# Load data\n","data = pd.read_csv(\"/content/drive/My Drive/CS 631 Introduction to Big Data/New folder/combined_csv.csv\")\n","\n","# Format data\n","data = data.rename(columns={'ds': 'Date', 'y': 'Confirmed'})\n","data['Date'] = pd.to_datetime(data['Date'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9FmWs0r4odnW"},"outputs":[],"source":["# Fit model\n","model = Prophet()\n","model.fit(data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AgSbIvkHohFb"},"outputs":[],"source":["# Create future dataframe\n","future = model.make_future_dataframe(periods=30)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nqRHBuClolJq"},"outputs":[],"source":["# Forecast future data\n","forecast = model.predict(future)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ayfqG116ooZ9"},"outputs":[],"source":["# Plot results\n","model.plot(forecast)\n","model.plot_components(forecast)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7NR1jUfSs_9m"},"outputs":[],"source":["from sklearn.ensemble import RandomForestRegressor\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import mean_squared_error\n","\n","# Load the dataset into a pandas DataFrame\n","df = pd.read_csv(\"<your_dataset>.csv\")\n","\n","# Divide the dataset into input features (X) and target variable (y)\n","X = df.drop(\"target_variable\", axis=1)\n","y = df[\"target_variable\"]\n","\n","# Split the data into training and testing sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Train a random forest model on the training data\n","model = RandomForestRegressor(n_estimators=100, random_state=42)\n","model.fit(X_train, y_train)\n","\n","# Predict the target variable for the test data\n","y_pred = model.predict(X_test)\n","\n","# Calculate the mean squared error between the true target values and the predictions\n","mse = mean_squared_error(y_test, y_pred)\n","\n","# Print the mean squared error\n","print(\"Mean Squared Error:\", mse)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6ebaxls0Fr1M"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6a5SU7HTFscz"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hgY2WCRgFs56"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d-8HAIf9Ftbt"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"dNYZLi3wFp_J"},"source":["Cross-validation is a widely used technique for evaluating the performance of machine learning models. It helps in reducing the risk of overfitting by dividing the data into training and validation sets, and testing the model's performance on the validation set. In cross-validation, we divide the data into \"k\" equal parts, train the model on k-1 parts and test it on the remaining part. This process is repeated \"k\" times, with each part being used as the validation set once. The average performance of the model on all k iterations is used to get an overall performance score of the model."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KnUOm6L2Fwwq"},"outputs":[],"source":["from sklearn.ensemble import RandomForestClassifier\n","from sklearn.model_selection import cross_val_score\n","\n","# Load the dataset\n","X, y = load_data(\"path/to/dataset.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6gYynrR9F2c9"},"outputs":[],"source":["# Create a random forest classifier\n","model = RandomForestClassifier()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cmD1kbrqF4LN"},"outputs":[],"source":["# Evaluate the model using cross-validation\n","scores = cross_val_score(model, X, y, cv=10)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_A_9db7fF7bm"},"outputs":[],"source":["# Print the average accuracy\n","print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"base","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":"3.8.5"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"65aae9546fc100ad295d8d8d84ce722fa6b6b236997e248ddedeb84fda20c8cb"}}},"nbformat":4,"nbformat_minor":0}