Class 4 - Lab 20.03 - Data Analysis.ipynb

{"cells":[{"cell_type":"code","source":["#Import drive\n","from google.colab import drive\n","#Mount Google Drive\n","ROOT=\"/content/drive\"\n","drive.mount(ROOT, force_remount=True)"],"metadata":{"id":"6FPLd0El3JGg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710926601490,"user_tz":-60,"elapsed":21356,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"1fd566a0-db6e-4018-d13d-3bb4af438431"},"id":"6FPLd0El3JGg","execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["#added code"],"metadata":{"id":"Czbt7JpULASV"},"id":"Czbt7JpULASV","execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"5eb03263","metadata":{"id":"5eb03263"},"source":["# Week 2 - Class 2 - Data Analysis\n","\n","This notebook have good example exercises to get you going on data analysis"]},{"cell_type":"markdown","id":"1efb5121","metadata":{"id":"1efb5121"},"source":["# Coronavirus (COVID-19) Visualization & Prediction  \n","Coronavirus is a family of viruses that are named after their spiky crown. The novel coronavirus, also known as SARS-CoV-2, is a contagious respiratory virus that first reported in Wuhan, China. On 2/11/2020, the World Health Organization designated the name COVID-19 for the disease caused by the novel coronavirus. This notebook aims at exploring COVID-19 through data analysis and projections.\n","\n","   Coronavirus Case Data is provided by <a href='https://github.com/CSSEGISandData/COVID-19'>Johns Hopkins University</a>\n","   <br>Learn more from the <a href='https://www.who.int/emergencies/diseases/novel-coronavirus-2019'>World Health Organization</a>\n","   <br>Learn more from the <a href='https://www.cdc.gov/coronavirus/2019-ncov'>Centers for Disease Control and Prevention</a>\n","   <br>Check out map visualizations from  <a href='https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6'>JHU CCSE Dashboard</a>\n","   <br>Source code is also on <a href='https://github.com/therealcyberlord'>my Github</a>\n","   \n","   \n","   ```Last update: 1/16/2023 7:41 PM ET. New Updates: Daily report data update for 1/16/23. time series data update for 1/15/23. ```\n","\n","source: https://www.kaggle.com/code/therealcyberlord/coronavirus-covid-19-visualization-prediction/notebook"]},{"cell_type":"code","source":["import sklearn.model_selection as sk"],"metadata":{"id":"0ZMmd6GiUjYy","executionInfo":{"status":"ok","timestamp":1710929112167,"user_tz":-60,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"id":"0ZMmd6GiUjYy","execution_count":54,"outputs":[]},{"cell_type":"code","source":["sk.train_test_split()"],"metadata":{"id":"7SHRExNCUnKB"},"id":"7SHRExNCUnKB","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":2,"id":"df18517a","metadata":{"id":"df18517a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710926900241,"user_tz":-60,"elapsed":2119,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"d4b885f8-2f90-47c5-f4a4-261407a852f0"},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-2-aafedaf1d530>:18: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n","  plt.style.use('seaborn-poster')\n"]}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import matplotlib.colors as mcolors\n","import pandas as pd\n","import random\n","import math\n","import time\n","from sklearn.linear_model import LinearRegression, BayesianRidge\n","from sklearn.model_selection import RandomizedSearchCV, train_test_split\n","from sklearn.preprocessing import PolynomialFeatures\n","from sklearn.svm import SVR\n","from sklearn.metrics import mean_squared_error, mean_absolute_error\n","import datetime\n","import operator\n","%matplotlib inline\n","import warnings\n","\n","plt.style.use('seaborn-poster')\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"code","execution_count":3,"id":"e84c3248","metadata":{"id":"e84c3248","executionInfo":{"status":"ok","timestamp":1710926968401,"user_tz":-60,"elapsed":862,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n","deaths_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n","latest_data = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-15-2023.csv')"]},{"cell_type":"code","source":["deaths_df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162},"id":"sUncvE-sMnZA","executionInfo":{"status":"ok","timestamp":1710927060025,"user_tz":-60,"elapsed":8,"user":{"displayName":"Catarina 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Confirmed  Deaths  Recovered  Active Combined_Key  \\\n","0  67.709953     207900    7854        NaN     NaN  Afghanistan   \n","1  20.168300     334037    3596        NaN     NaN      Albania   \n","\n","   Incident_Rate  Case_Fatality_Ratio  \n","0     534.058207             3.777778  \n","1   11607.373688             1.076527  "],"text/html":["\n","  <div id=\"df-d2e16336-aea1-4a81-8a56-be50a8599963\" 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>FIPS</th>\n","      <th>Admin2</th>\n","      <th>Province_State</th>\n","      <th>Country_Region</th>\n","      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0\n","    for element in lst:\n","        if any('a' <= char <= 'z' for char in element):\n","            count += 1\n","    return count"],"metadata":{"id":"ZMkTxzSFnOEg","executionInfo":{"status":"ok","timestamp":1710927680730,"user_tz":-60,"elapsed":395,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"id":"ZMkTxzSFnOEg","execution_count":17,"outputs":[]},{"cell_type":"code","source":["def count_non_date_elements(lst):\n","  from datetime import datetime\n","\n","  count = 0\n","  for element in lst:\n","      try:\n","          datetime.strptime(element, '%m/%d/%y')\n","      except ValueError:\n","          count += 1\n","  return count\n"],"metadata":{"id":"fin_0em9nTIc","executionInfo":{"status":"ok","timestamp":1710927832293,"user_tz":-60,"elapsed":324,"user":{"displayName":"Catarina 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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","\n","\n","<div id=\"df-62ce315d-38b5-4ee4-9ed4-d62af379f58a\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62ce315d-38b5-4ee4-9ed4-d62af379f58a')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-62ce315d-38b5-4ee4-9ed4-d62af379f58a button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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deaths_cols[4]:]"]},{"cell_type":"code","source":["count_elements_with_characters(confirmed_cols)\n"],"metadata":{"id":"q0nfXwG3nVs5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710927687608,"user_tz":-60,"elapsed":408,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"60b102f5-913f-4b10-ce10-50d9ec9d7ec4"},"id":"q0nfXwG3nVs5","execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["4"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["count_non_date_elements(confirmed)"],"metadata":{"id":"TuoI9MR4nfKu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710927837838,"user_tz":-60,"elapsed":254,"user":{"displayName":"Catarina 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3/2/23  3/3/23  3/4/23  3/5/23  \\\n","0        0        0  ...   209322  209340  209358  209362  209369  209390   \n","1        0        0  ...   334391  334408  334408  334427  334427  334427   \n","\n","   3/6/23  3/7/23  3/8/23  3/9/23  \n","0  209406  209436  209451  209451  \n","1  334427  334427  334443  334457  \n","\n","[2 rows x 1143 columns]"],"text/html":["\n","  <div id=\"df-63f2c9fb-133b-442f-99b8-08f39f28d90c\" 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>1/22/20</th>\n","      <th>1/23/20</th>\n","      <th>1/24/20</th>\n","      <th>1/25/20</th>\n","      <th>1/26/20</th>\n","      <th>1/27/20</th>\n","      <th>1/28/20</th>\n","      <th>1/29/20</th>\n","      <th>1/30/20</th>\n","      <th>1/31/20</th>\n","      <th>...</th>\n","      <th>2/28/23</th>\n","      <th>3/1/23</th>\n","      <th>3/2/23</th>\n","      <th>3/3/23</th>\n","      <th>3/4/23</th>\n","      <th>3/5/23</th>\n","      <th>3/6/23</th>\n","      <th>3/7/23</th>\n","      <th>3/8/23</th>\n","      <th>3/9/23</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>209322</td>\n","      <td>209340</td>\n","      <td>209358</td>\n","      <td>209362</td>\n","      <td>209369</td>\n","      <td>209390</td>\n","      <td>209406</td>\n","      <td>209436</td>\n","      <td>209451</td>\n","      <td>209451</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>334391</td>\n","      <td>334408</td>\n","      <td>334408</td>\n","      <td>334427</td>\n","      <td>334427</td>\n","      <td>334427</td>\n","      <td>334427</td>\n","      <td>334427</td>\n","      <td>334443</td>\n","      <td>334457</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 1143 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63f2c9fb-133b-442f-99b8-08f39f28d90c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg 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date"]},{"cell_type":"code","source":["ck[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":56},"id":"7b1RF4WxQ11w","executionInfo":{"status":"ok","timestamp":1710928132053,"user_tz":-60,"elapsed":6,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"105128dd-f8c7-4744-f4a7-124628491416"},"id":"7b1RF4WxQ11w","execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'1/22/20'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["confirmed['1/22/20']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mSfHpSvQQ4-D","executionInfo":{"status":"ok","timestamp":1710928152755,"user_tz":-60,"elapsed":315,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"2da297f0-fb1c-41c4-df53-18ce702da1d5"},"id":"mSfHpSvQQ4-D","execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0      0\n","1      0\n","2      0\n","3      0\n","4      0\n","      ..\n","284    0\n","285    0\n","286    0\n","287    0\n","288    0\n","Name: 1/22/20, Length: 289, dtype: int64"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","execution_count":33,"id":"cce6540e","metadata":{"id":"cce6540e","executionInfo":{"status":"ok","timestamp":1710928245341,"user_tz":-60,"elapsed":308,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["num_dates = len(confirmed.columns)\n","ck = confirmed.columns\n","dk = deaths.columns\n","\n","world_cases = []\n","total_deaths = []\n","mortality_rate = []\n","\n","# list comprehension of this?\n","for i in range(num_dates):\n","    confirmed_sum = confirmed[ck[i]].sum()\n","    death_sum = deaths[dk[i]].sum()\n","\n","    world_cases.append(confirmed_sum)\n","    total_deaths.append(death_sum)\n","\n","    # calculate rates\n","    mortality_rate.append(death_sum/confirmed_sum)"]},{"cell_type":"code","source":["world_cases,total_deaths"],"metadata":{"id":"1duwab1pRwvT"},"id":"1duwab1pRwvT","execution_count":null,"outputs":[]},{"cell_type":"code","source":["def daily_increase(data):\n","    d = []\n","    for i in range(len(data)):\n","        if i == 0:\n","            d.append(data[0])\n","        else:\n","            d.append(data[i]-data[i-1])\n","    return d\n","\n","def moving_average(data, window_size):\n","    moving_average = []\n","    for i in range(len(data)):\n","        if i + window_size < len(data):\n","            moving_average.append(np.mean(data[i:i+window_size]))\n","        else:\n","            moving_average.append(np.mean(data[i:len(data)]))\n","    return moving_average"],"metadata":{"id":"3ry_IhzrR34d","executionInfo":{"status":"ok","timestamp":1710928399148,"user_tz":-60,"elapsed":386,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"id":"3ry_IhzrR34d","execution_count":34,"outputs":[]},{"cell_type":"code","source":["world_cases"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F13vJZ6fSbsB","executionInfo":{"status":"ok","timestamp":1710928545795,"user_tz":-60,"elapsed":3,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"c317ebc2-09dc-463c-8f4b-f04c774aadce"},"id":"F13vJZ6fSbsB","execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[557,\n"," 657,\n"," 944,\n"," 1437,\n"," 2120,\n"," 2929,\n"," 5580,\n"," 6169,\n"," 8237,\n"," 9927,\n"," 12038,\n"," 16787,\n"," 19887,\n"," 23899,\n"," 27644,\n"," 30806,\n"," 34400,\n"," 37131,\n"," 40162,\n"," 42771,\n"," 44814,\n"," 45232,\n"," 60384,\n"," 66912,\n"," 69055,\n"," 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621221027,\n"," 621440044,\n"," 621658996,\n"," 622112629,\n"," 622802836,\n"," 623353219,\n"," 624016113,\n"," 624494008,\n"," 624712097,\n"," 624904267,\n"," 625380762,\n"," ...]"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","execution_count":35,"id":"c9523de5","metadata":{"id":"c9523de5","executionInfo":{"status":"ok","timestamp":1710928404533,"user_tz":-60,"elapsed":319,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["\n","\n","# window size\n","window = 7\n","\n","# confirmed cases\n","world_daily_increase = daily_increase(world_cases)\n","world_confirmed_avg= moving_average(world_cases, window)\n","world_daily_increase_avg = moving_average(world_daily_increase, window)\n","\n","# deaths\n","world_daily_death = daily_increase(total_deaths)\n","world_death_avg = moving_average(total_deaths, window)\n","world_daily_death_avg = moving_average(world_daily_death, window)"]},{"cell_type":"code","source":["ck[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":56},"id":"iO1JXK_RTOee","executionInfo":{"status":"ok","timestamp":1710928755358,"user_tz":-60,"elapsed":7,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"2264df93-6eb6-4368-85a2-cbbf42961dbe"},"id":"iO1JXK_RTOee","execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'1/22/20'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["np.array([i for i in range(len(ck))]).reshape(-1, 1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ycg8z-FpSxkv","executionInfo":{"status":"ok","timestamp":1710928679812,"user_tz":-60,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"33cba736-6e6d-4007-c0f8-292b317cb527"},"id":"ycg8z-FpSxkv","execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[   0],\n","       [   1],\n","       [   2],\n","       ...,\n","       [1140],\n","       [1141],\n","       [1142]])"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","execution_count":90,"id":"54e831bb","metadata":{"id":"54e831bb","executionInfo":{"status":"ok","timestamp":1710931666131,"user_tz":-60,"elapsed":221,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["days_since_1_22 = np.array([i for i in range(len(ck))]).reshape(-1, 1)\n","world_cases = np.array(world_daily_increase).reshape(-1, 1)\n","total_deaths = np.array(world_daily_increase).reshape(-1, 1)"]},{"cell_type":"code","execution_count":84,"id":"9b39ada7","metadata":{"id":"9b39ada7","executionInfo":{"status":"ok","timestamp":1710931572645,"user_tz":-60,"elapsed":281,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["days_in_future = 10\n","future_forcast = np.array([i for i in range(len(ck)+days_in_future)]).reshape(-1, 1)\n","adjusted_dates = future_forcast[:-10]"]},{"cell_type":"code","source":[],"metadata":{"id":"Arta5dOwUFqR"},"id":"Arta5dOwUFqR","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":85,"id":"b3a47ba7","metadata":{"id":"b3a47ba7","executionInfo":{"status":"ok","timestamp":1710931574459,"user_tz":-60,"elapsed":241,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["start = '1/22/2020'\n","start_date = datetime.datetime.strptime(start, '%m/%d/%Y')\n","future_forcast_dates = []\n","for i in range(len(future_forcast)):\n","    future_forcast_dates.append((start_date + datetime.timedelta(days=i)).strftime('%m/%d/%Y'))"]},{"cell_type":"code","source":["len(world_cases)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wzWOnEe7T62o","executionInfo":{"status":"ok","timestamp":1710929228943,"user_tz":-60,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"d2a35d38-380a-476a-fdac-fa9a95693241"},"id":"wzWOnEe7T62o","execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1143"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","execution_count":91,"id":"43bab22a","metadata":{"id":"43bab22a","executionInfo":{"status":"ok","timestamp":1710931670672,"user_tz":-60,"elapsed":3,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["# slightly modify the data to fit the model better (regression models cannot pick the pattern), we are using data from 8/1/22 and onwards for the prediction modeling\n","days_to_skip = 922\n","X_train_confirmed, \\\n","X_test_confirmed, \\\n","y_train_confirmed, \\\n","y_test_confirmed = \\\n","train_test_split(days_since_1_22[days_to_skip:], world_cases[days_to_skip:], test_size=0.07, shuffle=False)"]},{"cell_type":"code","source":[],"metadata":{"id":"bm8xQ38aaovE"},"id":"bm8xQ38aaovE","execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(X_test_confirmed)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cnLlejsMaknQ","executionInfo":{"status":"ok","timestamp":1710930684789,"user_tz":-60,"elapsed":212,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"44e19116-9057-4d63-943b-ee3fc5b79a5e"},"id":"cnLlejsMaknQ","execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["16"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","source":["X_test_confirmed"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MsDzNS6Jaxb2","executionInfo":{"status":"ok","timestamp":1710930730224,"user_tz":-60,"elapsed":362,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"2d568d1a-d0cf-4555-fe2b-75fb463afda4"},"id":"MsDzNS6Jaxb2","execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1127],\n","       [1128],\n","       [1129],\n","       [1130],\n","       [1131],\n","       [1132],\n","       [1133],\n","       [1134],\n","       [1135],\n","       [1136],\n","       [1137],\n","       [1138],\n","       [1139],\n","       [1140],\n","       [1141],\n","       [1142]])"]},"metadata":{},"execution_count":77}]},{"cell_type":"code","source":["poly_X_test_confirmed"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PMrY6rBkayyc","executionInfo":{"status":"ok","timestamp":1710930735427,"user_tz":-60,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"3af2e82e-486e-4871-cc70-a86f167e1691"},"id":"PMrY6rBkayyc","execution_count":78,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1.00000000e+00, 1.12700000e+03, 1.27012900e+06, 1.43143538e+09],\n","       [1.00000000e+00, 1.12800000e+03, 1.27238400e+06, 1.43524915e+09],\n","       [1.00000000e+00, 1.12900000e+03, 1.27464100e+06, 1.43906969e+09],\n","       [1.00000000e+00, 1.13000000e+03, 1.27690000e+06, 1.44289700e+09],\n","       [1.00000000e+00, 1.13100000e+03, 1.27916100e+06, 1.44673109e+09],\n","       [1.00000000e+00, 1.13200000e+03, 1.28142400e+06, 1.45057197e+09],\n","       [1.00000000e+00, 1.13300000e+03, 1.28368900e+06, 1.45441964e+09],\n","       [1.00000000e+00, 1.13400000e+03, 1.28595600e+06, 1.45827410e+09],\n","       [1.00000000e+00, 1.13500000e+03, 1.28822500e+06, 1.46213538e+09],\n","       [1.00000000e+00, 1.13600000e+03, 1.29049600e+06, 1.46600346e+09],\n","       [1.00000000e+00, 1.13700000e+03, 1.29276900e+06, 1.46987835e+09],\n","       [1.00000000e+00, 1.13800000e+03, 1.29504400e+06, 1.47376007e+09],\n","       [1.00000000e+00, 1.13900000e+03, 1.29732100e+06, 1.47764862e+09],\n","       [1.00000000e+00, 1.14000000e+03, 1.29960000e+06, 1.48154400e+09],\n","       [1.00000000e+00, 1.14100000e+03, 1.30188100e+06, 1.48544622e+09],\n","       [1.00000000e+00, 1.14200000e+03, 1.30416400e+06, 1.48935529e+09]])"]},"metadata":{},"execution_count":78}]},{"cell_type":"code","execution_count":92,"id":"94fa87ac","metadata":{"id":"94fa87ac","executionInfo":{"status":"ok","timestamp":1710931676914,"user_tz":-60,"elapsed":321,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"outputs":[],"source":["\n","poly = PolynomialFeatures(degree=3)\n","poly_X_train_confirmed = poly.fit_transform(X_train_confirmed)\n","poly_X_test_confirmed = poly.fit_transform(X_test_confirmed)\n","poly_future_forcast = poly.fit_transform(future_forcast)"]},{"cell_type":"code","source":["from sklearn.preprocessing import MinMaxScaler\n","# Create MinMaxScaler object\n","scaler = MinMaxScaler()\n","\n","# Fit and transform the data\n","poly_X_train_confirmedN = scaler.fit_transform(poly_X_train_confirmed)\n","# Fit and transform the data\n","poly_X_test_confirmedN = scaler.fit_transform(poly_X_test_confirmed)"],"metadata":{"id":"ph32irmTXEXP","executionInfo":{"status":"ok","timestamp":1710929829849,"user_tz":-60,"elapsed":241,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}}},"id":"ph32irmTXEXP","execution_count":61,"outputs":[]},{"cell_type":"code","execution_count":93,"id":"2e887336","metadata":{"id":"2e887336","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1710931679048,"user_tz":-60,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"249bd00f-1cc8-4055-9618-d389a17483a1"},"outputs":[{"output_type":"stream","name":"stdout","text":["MAE: 330485.76022306085\n","MSE: 121083474142.82959\n"]}],"source":["# polynomial regression\n","linear_model = LinearRegression( fit_intercept=True)\n","linear_model.fit(poly_X_train_confirmed, y_train_confirmed)\n","test_linear_pred = linear_model.predict(poly_X_test_confirmed)\n","linear_pred = linear_model.predict(poly_future_forcast)\n","print('MAE:', mean_absolute_error(test_linear_pred, y_test_confirmed))\n","print('MSE:',mean_squared_error(test_linear_pred, y_test_confirmed))\n"]},{"cell_type":"code","source":["len(test_linear_pred)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KzLFSWJKXj9Q","executionInfo":{"status":"ok","timestamp":1710931131196,"user_tz":-60,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"f7bfdeaf-fa1f-4885-b242-0d04c9b15a2c"},"id":"KzLFSWJKXj9Q","execution_count":81,"outputs":[{"output_type":"execute_result","data":{"text/plain":["16"]},"metadata":{},"execution_count":81}]},{"cell_type":"code","source":["len(world_cases)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_HUKKTkDXhmN","executionInfo":{"status":"ok","timestamp":1710929893146,"user_tz":-60,"elapsed":4,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"300a7849-841c-4bfb-a8f8-507b096867c4"},"id":"_HUKKTkDXhmN","execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1143"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["world_cases[1143-1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WoSqMBbucnpH","executionInfo":{"status":"ok","timestamp":1710931684792,"user_tz":-60,"elapsed":224,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"d2e623d5-5c02-4feb-95dc-57758f05ccf5"},"id":"WoSqMBbucnpH","execution_count":94,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([177325])"]},"metadata":{},"execution_count":94}]},{"cell_type":"code","source":["mean_absolute_error(test_linear_pred, y_test_confirmed)/world_cases[1143-1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oprTwTx5Xl7D","executionInfo":{"status":"ok","timestamp":1710929930860,"user_tz":-60,"elapsed":5,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"6a40c9fb-1d51-4a48-f8ce-aadf8fb8bb03"},"id":"oprTwTx5Xl7D","execution_count":66,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.06615795])"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","execution_count":null,"id":"f028ca4b","metadata":{"id":"f028ca4b"},"outputs":[],"source":["print(linear_model.coef_)"]},{"cell_type":"code","execution_count":95,"id":"dba470c1","metadata":{"id":"dba470c1","colab":{"base_uri":"https://localhost:8080/","height":663},"executionInfo":{"status":"ok","timestamp":1710931987979,"user_tz":-60,"elapsed":595,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"a80dfaaa-099f-4faa-e904-b9bc9ed8ed3c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7abdbe1aec50>"]},"metadata":{},"execution_count":95},{"output_type":"display_data","data":{"text/plain":["<Figure size 1280x880 with 1 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\n"},"metadata":{}}],"source":["plt.plot(y_test_confirmed)\n","plt.plot(test_linear_pred)\n","plt.legend(['Test Data', 'Polynomial Regression Predictions'])"]},{"cell_type":"markdown","id":"468573c9","metadata":{"id":"468573c9"},"source":["## Q16"]},{"cell_type":"code","execution_count":null,"id":"a98b6983","metadata":{"id":"a98b6983"},"outputs":[],"source":["# https://scikit-learn.org/stable/getting_started.html#fitting-and-predicting-estimator-basics\n","# Apply a Random Forest Classifier (??) and do the same plot"]},{"cell_type":"code","source":[],"metadata":{"id":"hX40gZKkYDPU"},"id":"hX40gZKkYDPU","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"OATPSWH5YDMf"},"id":"OATPSWH5YDMf","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"yWJYeMF6YDKJ"},"id":"yWJYeMF6YDKJ","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"eOOZBxA-YDHl"},"id":"eOOZBxA-YDHl","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"TfndRPHlYDFC"},"id":"TfndRPHlYDFC","execution_count":null,"outputs":[]},{"cell_type":"code","source":[" - Print, head, see it\n"," - Cleaning (confirm your data)\n"," - Model struture\n"," - Metric\n"," - Interpret\n"," - Conclusions"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":127},"id":"jltBlTLKYDCB","executionInfo":{"status":"error","timestamp":1710932360631,"user_tz":-60,"elapsed":248,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"454642bf-e3cf-4697-d6c0-ee5daa96dbef"},"id":"jltBlTLKYDCB","execution_count":100,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"invalid syntax (<ipython-input-100-1e96410eb49f>, line 1)","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-100-1e96410eb49f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    - Print, head, see it\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","source":["from sklearn.datasets import fetch_california_housing\n","from sklearn.ensemble import RandomForestRegressor\n","from sklearn.model_selection import RandomizedSearchCV\n","from sklearn.model_selection import train_test_split\n","from scipy.stats import randint\n","#X, y = fetch_california_housing(return_X_y=True)\n","#X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n","# define the parameter space that will be searched over\n","param_distributions = {'n_estimators': randint(1, 5),\n","                       'max_depth': randint(5, 10)}\n","# now create a searchCV object and fit it to the data\n","search = RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0),\n","                            n_iter=5,\n","                            param_distributions=param_distributions,\n","                            random_state=0)\n","search.fit(poly_X_train_confirmed, y_train_confirmed)\n","search.best_params_\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LNTwood6YC36","executionInfo":{"status":"ok","timestamp":1710932220424,"user_tz":-60,"elapsed":338,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"b3826123-d25e-41de-f636-6642864a6a91"},"id":"LNTwood6YC36","execution_count":97,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'max_depth': 7, 'n_estimators': 1}"]},"metadata":{},"execution_count":97}]},{"cell_type":"code","source":["\n","# the search object now acts like a normal random forest estimator\n","# with max_depth=9 and n_estimators=4\n","search.score(poly_X_test_confirmed, y_test_confirmed)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RCTcd1CiY6um","executionInfo":{"status":"ok","timestamp":1710932222485,"user_tz":-60,"elapsed":305,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"23773dd5-da25-4c75-8232-24795462c752"},"id":"RCTcd1CiY6um","execution_count":98,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.667131495339206"]},"metadata":{},"execution_count":98}]},{"cell_type":"code","source":["plt.plot(poly_X_test_confirmed)\n","predictions = search.predict(poly_X_test_confirmed)\n","\n","plt.plot(predictions)\n","plt.legend(['Test Data', 'Random Regression Predictions'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":720},"id":"lG_ZGVFNZpt4","executionInfo":{"status":"ok","timestamp":1710932247521,"user_tz":-60,"elapsed":915,"user":{"displayName":"Catarina Pereira","userId":"01889833079344592364"}},"outputId":"1a459309-7558-4567-c419-11667f863961"},"id":"lG_ZGVFNZpt4","execution_count":99,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7abdbe1879d0>"]},"metadata":{},"execution_count":99},{"output_type":"display_data","data":{"text/plain":["<Figure size 1280x880 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","id":"27bf5bc1","metadata":{"id":"27bf5bc1"},"source":["## Q17"]},{"cell_type":"code","execution_count":null,"id":"4ea74ee8","metadata":{"id":"4ea74ee8"},"outputs":[],"source":["import pandas as pd\n","from sklearn.datasets import load_iris\n","\n","# Load the iris dataset\n","iris_data = load_iris()\n","\n","# Create a DataFrame\n","<your-answer>\n","\n","# Add the target (species) column to the DataFrame\n","<your-answer>\n","\n","# Map target numerical values to species names\n","<your-answer>\n","\n","\n"]},{"cell_type":"code","execution_count":null,"id":"19e9ac8f","metadata":{"id":"19e9ac8f"},"outputs":[],"source":["# Display basic information\n","print(\"Basic Information:\")\n","\n","print(\"\\nData Types:\")\n"]},{"cell_type":"code","execution_count":null,"id":"3db72033","metadata":{"id":"3db72033"},"outputs":[],"source":["\n","print(\"\\nSummary Statistics:\")\n"]},{"cell_type":"code","execution_count":null,"id":"dbcf5da0","metadata":{"id":"dbcf5da0"},"outputs":[],"source":["\n","# Data Exploration\n","print(\"\\nMean Values by Species:\")\n","\n"]},{"cell_type":"code","execution_count":null,"id":"05f5ab53","metadata":{"id":"05f5ab53"},"outputs":[],"source":["\n","print(\"\\nMedian Values by Species:\")\n"]},{"cell_type":"code","execution_count":null,"id":"40faaeaa","metadata":{"id":"40faaeaa"},"outputs":[],"source":["\n","print(\"\\nMinimum Values by Species:\")\n"]},{"cell_type":"code","execution_count":null,"id":"ae8c216f","metadata":{"id":"ae8c216f"},"outputs":[],"source":["\n","print(\"\\nMaximum Values by Species:\")\n"]},{"cell_type":"code","execution_count":null,"id":"c3921fee","metadata":{"id":"c3921fee"},"outputs":[],"source":["\n","print(\"\\nCount of Species:\")\n"]},{"cell_type":"code","source":[],"metadata":{"id":"HjKAIEH7pV5n"},"id":"HjKAIEH7pV5n","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"kuNeLhzTpV21"},"id":"kuNeLhzTpV21","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"BPyCt3wupVzt"},"id":"BPyCt3wupVzt","execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"bLUdNEtcp5W_"},"id":"bLUdNEtcp5W_","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3.9.13","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"},"vscode":{"interpreter":{"hash":"d33a1388bb95f6f47390937b44de6829ea073fadb3ed2a95296fdc5e4fdfba80"}},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}