all_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08eff97b-3d1b-4187-8b8b-8b796ef9ad81",
   "metadata": {},
   "source": [
    "## Data Collection and Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb35b6e-7095-491c-aeda-add3b8ea48e6",
   "metadata": {},
   "source": [
    "Please note the datascraping code below is an example for only one source. See report for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4db53622-3f4c-4361-8ac3-9f8b58c52565",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from datetime import datetime, timedelta\n",
    "import csv\n",
    "\n",
    "# Function to scrape maximum daily temperatures for a given date\n",
    "def scrape_max_temperatures(date):\n",
    "    url = f\"https://open-meteo.com/history?latitude=0&longitude=0&date={date.strftime('%Y-%m-%d')}\"\n",
    "    response = requests.get(url)\n",
    "    soup = BeautifulSoup(response.content, 'html.parser')\n",
    "    # Find the maximum temperature element\n",
    "    max_temp_element = soup.find('div', class_='weather__day__temperature-max')\n",
    "    if max_temp_element:\n",
    "        max_temp = max_temp_element.text.strip()\n",
    "        return max_temp\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Start and end dates\n",
    "start_date = datetime(2020, 1, 1)\n",
    "end_date = datetime(2023, 12, 31)\n",
    "\n",
    "# Open CSV file for writing\n",
    "with open('open-meteo.csv', 'w', newline='') as csvfile:\n",
    "    fieldnames = ['Date', 'Max Temperature (°C)']\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "    writer.writeheader()\n",
    "\n",
    "    # Loop through each date and scrape the maximum temperature\n",
    "    current_date = start_date\n",
    "    while current_date <= end_date:\n",
    "        max_temp = scrape_max_temperatures(current_date)\n",
    "        if max_temp:\n",
    "            writer.writerow({'Date': current_date.strftime('%Y-%m-%d'), 'Max Temperature (°C)': max_temp})\n",
    "        else:\n",
    "            writer.writerow({'Date': current_date.strftime('%Y-%m-%d'), 'Max Temperature (°C)': 'N/A'})\n",
    "        current_date += timedelta(days=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "020b4b14-4937-4998-8ef4-0bd43f9d3e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load in data\n",
    "Miami_openmeteo = pd.read_csv('open metro/Miami_daily_daily_weather_data.csv')\n",
    "Austin_openmeteo = pd.read_csv('open metro/Austin_daily_daily_weather_data.csv')\n",
    "NYC_openmeteo = pd.read_csv('open metro/NYC_daily_daily_weather_data.csv')\n",
    "Chicago_openmeteo = pd.read_csv('open metro/Chicago_daily_daily_weather_data.csv')\n",
    "\n",
    "# Function to process dataframe\n",
    "def process(df):\n",
    "    # Convert 'Datetime' to datetime objects\n",
    "    df['Datetime'] = pd.to_datetime(df['Datetime'])\n",
    "    # Extract just the date part and format it as \"year-month-date\"\n",
    "    df['Date'] = df['Datetime'].dt.strftime('%Y-%d-%m')\n",
    "    # Optionally, remove the original 'Datetime' column\n",
    "    df.drop('Datetime', axis=1, inplace=True)\n",
    "    # Rename 'temperature_2m_max' to 'maxtemp_openmeteo'\n",
    "    df.rename(columns={'temperature_2m_max': 'maxtemp_openmeteo'}, inplace=True)\n",
    "    # Create a new DataFrame with selected columns\n",
    "    return df[['Date', 'maxtemp_openmeteo']]\n",
    "\n",
    "# Apply the function\n",
    "Miami_openmeteo = process(Miami_openmeteo)\n",
    "Austin_openmeteo = process(Austin_openmeteo)\n",
    "NYC_openmeteo = process(NYC_openmeteo)\n",
    "Chicago_openmeteo = process(Chicago_openmeteo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f7036c8-0b34-403f-8ba8-bad2cc2d538d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in data\n",
    "Miami_vc = pd.read_csv('VC/Miami 2022-01-01 to 2023-12-31.csv')\n",
    "Austin_vc = pd.read_csv('VC/Austin 2022-01-01 to 2023-12-31.csv')\n",
    "NYC_vc = pd.read_csv('VC/New york city 2022-01-01 to 2023-12-31.csv')\n",
    "Chicago_vc = pd.read_csv('VC/Chicago 2022-01-01 to 2023-12-31.csv')\n",
    "\n",
    "# Function to process dataframe\n",
    "def process(df):\n",
    "    # Rename columns\n",
    "    df = df.rename(columns={'datetime': 'Date', 'tempmax': 'maxtemp_vc'})\n",
    "    # Convert the 'Date' column to the desired format\n",
    "    df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%d-%m')\n",
    "    # Select only the required columns\n",
    "    new_df = df[['Date', 'maxtemp_vc']]\n",
    "    return new_df\n",
    "\n",
    "# Apply the function\n",
    "Miami_vc = process(Miami_vc)\n",
    "Austin_vc = process(Austin_vc)\n",
    "NYC_vc = process(NYC_vc)\n",
    "Chicago_vc = process(Chicago_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23001be7-18b8-4add-8418-260fa7be4ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in data\n",
    "Miami_meteostat = pd.read_csv('meteostat/Miami.csv')\n",
    "Austin_meteostat = pd.read_csv('meteostat/Austin.csv')\n",
    "Central_meteostat = pd.read_csv('meteostat/Newyork.csv')\n",
    "Chicago_meteostat = pd.read_csv('meteostat/Chicago.csv')\n",
    "\n",
    "# Function to process dataframe\n",
    "def process(df):\n",
    "    # Rename columns\n",
    "    df = df.rename(columns={'date': 'Date', 'tmax': 'maxtemp_mt'})\n",
    "    # Convert the 'Date' column to the desired format\n",
    "    df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%d-%m')\n",
    "    # Select only the required columns\n",
    "    new_df = df[['Date', 'maxtemp_mt']]\n",
    "    return new_df\n",
    "\n",
    "# Apply the function\n",
    "Miami_meteostat = process(Miami_meteostat)\n",
    "Austin_meteostat = process(Austin_meteostat)\n",
    "NYC_meteostat = process(NYC_meteostat)\n",
    "Chicago_meteostat = process(Chicago_meteostat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c74fe552-ea9f-40e7-8028-21cab7f0bf96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in data\n",
    "Miami_noaa = pd.read_csv('NOAA/Miami.csv')\n",
    "Austin_noaa = pd.read_csv('NOAA/Austin.csv')\n",
    "NYC_noaa = pd.read_csv('NOAA/NYC.csv')\n",
    "Chicago_noaa = pd.read_csv('NOAA/Chicago.csv')\n",
    "\n",
    "# Function to process dataframe\n",
    "def process(df):\n",
    "    # Rename TMAX column\n",
    "    df = df.rename(columns={'TMAX (Degrees Fahrenheit)': 'maxtemp_noaa'})\n",
    "    # Convert the 'Date' column to datetime\n",
    "    df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y').dt.strftime('%Y-%d-%m')\n",
    "    # Select only the required columns\n",
    "    new_df = df[['Date', 'maxtemp_noaa']]\n",
    "    return new_df\n",
    "\n",
    "# Apply the function\n",
    "Miami_noaa = process_noaa_dataframe(Miami_noaa)\n",
    "Austin_noaa = process_noaa_dataframe(Austin_noaa)\n",
    "NYC_noaa = process_noaa_dataframe(NYC_noaa)\n",
    "Chicago_noaa = process_noaa_dataframe(Chicago_noaa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd5618cd-8a76-404a-a96d-7f362ba4460c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in data\n",
    "Miami_sc = pd.read_excel('data/SC/Miami_SC.xlsx')\n",
    "Austin_sc = pd.read_excel('data/SC/Austin_SC.xlsx')\n",
    "NYC_sc = pd.read_excel('data/SC/newyork_SC.xlsx')\n",
    "Chicago_sc = pd.read_excel('data/SC/Chicago_SC.xlsx')\n",
    "\n",
    "# Function to process dataframe\n",
    "def process(df):\n",
    "    # Rename columns\n",
    "    df = df.rename(columns={'GHCN-Daily Results': 'maxtemp_sc'})\n",
    "    # Convert the 'Date' column to the desired format\n",
    "    df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%Y-%d-%m')\n",
    "    # Select only the required columns\n",
    "    new_df = df[['Date', 'maxtemp_sc']]\n",
    "    return new_df\n",
    "\n",
    "# Apply the function\n",
    "Miami_daily_dataframe_sc_new = process(Miami_sc)\n",
    "Austin_daily_dataframe_sc_new = process(Austin_sc)\n",
    "NYC_daily_dataframe_sc_new = process(NYC_sc)\n",
    "Chicago_daily_dataframe_sc_new = process(Chicago_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "660b0883-02d5-4be3-ad15-f6b8ed4e6b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Merge the dataframes\n",
    "Miami_merged = pd.merge(Miami_sc, Miami_noaa, on='Date', how='outer', suffixes=('_sc', '_noaa'))\n",
    "Miami_merged = pd.merge(Miami_merged, Miami_vc, on='Date', how='outer', suffixes=('', '_vc'))\n",
    "Miami_merged = pd.merge(Miami_merged, Miami_meteostat, on='Date', how='outer', suffixes=('', '_mt'))\n",
    "Miami_merged = pd.merge(Miami_merged, Miami_openmeteo, on='Date', how='outer', suffixes=('', '_om'))\n",
    "\n",
    "# Calculate the average max temp\n",
    "temperature_columns = ['maxtemp_sc', 'maxtemp_noaa', 'maxtemp_vc', 'maxtemp_mt', 'maxtemp_om']\n",
    "Miami_merged['max_temp'] = Miami_merged[temperature_columns].mean(axis=1).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29bc903b-0969-4806-9f81-f45b91e7a556",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the dataframes\n",
    "Austin_merged = pd.merge(Austin_sc, Austin_noaa, on='Date', how='outer', suffixes=('_sc', '_noaa'))\n",
    "Austin_merged = pd.merge(Austin_merged, Austin_vc, on='Date', how='outer')\n",
    "Austin_merged = pd.merge(Austin_merged, Austin_meteostat, on='Date', how='outer')\n",
    "Austin_merged = pd.merge(Austin_merged, Austin_openmeteo, on='Date', how='outer')\n",
    "\n",
    "# Calculate the average max temp\n",
    "temperature_columns_austin = ['maxtemp_sc', 'maxtemp_noaa', 'maxtemp_vc', 'maxtemp_mt', 'maxtemp_om']\n",
    "Austin_merged['max_temp'] = Austin_merged[temperature_columns_austin].mean(axis=1).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5085a0ca-6c4d-49e4-88bf-09464e4a57c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the dataframes\n",
    "NYC_merged = pd.merge(NYC_sc, NYC_noaa, on='Date', how='outer', suffixes=('_sc', '_noaa'))\n",
    "NYC_merged = pd.merge(NYC_merged, NYC_vc, on='Date', how='outer')\n",
    "NYC_merged = pd.merge(NYC_merged, NYC_meteostat, on='Date', how='outer')\n",
    "NYC_merged = pd.merge(NYC_merged, NYC_openmeteo, on='Date', how='outer')\n",
    "\n",
    "# Calculate the average max temp\n",
    "temperature_columns_cp = ['maxtemp_sc', 'maxtemp_noaa', 'maxtemp_vc', 'maxtemp_mt', 'maxtemp_om']\n",
    "NYC_merged['max_temp'] = NYC_merged[temperature_columns_cp].mean(axis=1).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bc2ec27-4fad-49d0-b56e-674106f594d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the dataframes\n",
    "Chicago_merged = pd.merge(Chicago_sc, Chicago_noaa, on='Date', how='outer', suffixes=('_sc', '_noaa'))\n",
    "Chicago_merged = pd.merge(Chicago_merged, Chicago_vc, on='Date', how='outer')\n",
    "Chicago_merged = pd.merge(Chicago_merged, Chicago_meteostat, on='Date', how='outer')\n",
    "Chicago_merged = pd.merge(Chicago_merged, Chicago_openmeteo, on='Date', how='outer')\n",
    "\n",
    "# Calculate the average max temp\n",
    "temperature_columns_chicago = ['maxtemp_sc', 'maxtemp_noaa', 'maxtemp_vc', 'maxtemp_mt', 'maxtemp_om']\n",
    "Chicago_merged['max_temp'] = Chicago_merged[temperature_columns_chicago].mean(axis=1).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "788d62a3-febb-4d61-a287-0926eb604480",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add city as a column to use as feature later\n",
    "Miami_merged['City'] = 'Miami'\n",
    "Austin_merged['City'] = 'Austin'\n",
    "NYC_merged['City'] = 'NYC'\n",
    "Chicago_merged['City'] = 'Chicago'\n",
    "\n",
    "# Concatenate into one master dataframe\n",
    "merged = pd.concat([Miami_merged, Austin_merged, NYC_merged, Chicago_merged], ignore_index=True)\n",
    "\n",
    "# Reorder dataframe to include the 'City' column\n",
    "columns_order = ['Date', 'maxtemp_sc', 'maxtemp_noaa', 'maxtemp_vc', 'maxtemp_mt', 'maxtemp_om', 'max_temp', 'City']\n",
    "merged = all_cities_merged[columns_order]\n",
    "\n",
    "# Save to CSV\n",
    "file_path = 'max_temps.csv' \n",
    "merged.to_csv(file_path, index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523a5b01-40c3-4b82-bc58-845c97571809",
   "metadata": {},
   "source": [
    "## Data Processing and Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "66acb2f9-b21b-4640-a616-55cec1eb3d03",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>maxtemp_sc</th>\n",
       "      <th>maxtemp_noaa</th>\n",
       "      <th>maxtemp_vc</th>\n",
       "      <th>maxtemp_mt</th>\n",
       "      <th>maxtemp_om</th>\n",
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       "      <th>City_Austin</th>\n",
       "      <th>City_Chicago</th>\n",
       "      <th>City_Miami</th>\n",
       "      <th>City_NYC</th>\n",
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       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
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       "      <td>77.0</td>\n",
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       "      <td>73.133938</td>\n",
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       "      <th>5839</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>43.400000</td>\n",
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       "      <td>2023-12-30</td>\n",
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       "      <td>2023-12-31</td>\n",
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       "      <td>33.643400</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5844 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  maxtemp_sc  maxtemp_noaa  maxtemp_vc  maxtemp_mt  maxtemp_om  \\\n",
       "0    2020-01-01        82.0     79.000000   73.133938        82.0   76.811905   \n",
       "1    2020-01-02        81.0     77.000000   73.133938        77.0   79.511900   \n",
       "2    2020-01-03        84.0     78.000000   73.133938        75.9   81.491900   \n",
       "3    2020-01-04        87.0     70.921331   73.133938        88.0   84.731895   \n",
       "4    2020-01-05        72.0     83.000000   73.133938        84.9   67.091900   \n",
       "...         ...         ...           ...         ...         ...         ...   \n",
       "5839 2023-12-27        44.0     44.000000   43.400000        44.1   41.383400   \n",
       "5840 2023-12-28        44.0     44.000000   42.800000        44.1   39.673400   \n",
       "5841 2023-12-29        44.0     44.000000   42.800000        44.1   40.123398   \n",
       "5842 2023-12-30        39.0     39.000000   37.500000        39.0   42.553400   \n",
       "5843 2023-12-31        38.0     38.000000   37.300000        37.9   33.643400   \n",
       "\n",
       "      max_temp  City_Austin  City_Chicago  City_Miami  City_NYC  \n",
       "0         80.0        False         False        True     False  \n",
       "1         79.0        False         False        True     False  \n",
       "2         80.0        False         False        True     False  \n",
       "3         87.0        False         False        True     False  \n",
       "4         77.0        False         False        True     False  \n",
       "...        ...          ...           ...         ...       ...  \n",
       "5839      43.0        False          True       False     False  \n",
       "5840      43.0        False          True       False     False  \n",
       "5841      43.0        False          True       False     False  \n",
       "5842      39.0        False          True       False     False  \n",
       "5843      37.0        False          True       False     False  \n",
       "\n",
       "[5844 rows x 11 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer\n",
    "df = pd.read_excel('max_temps.xlsx')\n",
    "\n",
    "# Convert 'Date' column to datetime format again\n",
    "df['Date'] = pd.to_datetime(df['Date'], format='%Y-%d-%m')\n",
    "\n",
    "# Separate non-numerical columns\n",
    "non_numerical_columns = ['Date', 'City']\n",
    "\n",
    "# Separate numerical columns\n",
    "numerical_columns = df.select_dtypes(include=['number']).columns\n",
    "\n",
    "# Instantiate SimpleImputer with strategy='mean' to replace NaN values with the mean of each numerical column\n",
    "imputer_numerical = SimpleImputer(strategy='mean')\n",
    "\n",
    "# Fit the imputer on numerical columns\n",
    "imputer_numerical.fit(df[numerical_columns])\n",
    "\n",
    "# Transform numerical columns by replacing NaN values with the mean\n",
    "df_imputed_numerical = pd.DataFrame(imputer_numerical.transform(df[numerical_columns]), columns=numerical_columns)\n",
    "\n",
    "# Combine the non-numerical column, imputed numerical columns\n",
    "df_imputed = pd.concat([df[non_numerical_columns], df_imputed_numerical], axis=1)\n",
    "\n",
    "# One-hot encode the 'City' column\n",
    "df = pd.get_dummies(df_imputed, columns=['City'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "6dc38640-d465-47a4-9955-e62bb23c3ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data['Date'] = pd.to_datetime(data['Date'], format = '%Y-%d-%m')\n",
    "df['Year'] = df['Date'].dt.year\n",
    "df['Month'] = df['Date'].dt.month\n",
    "df['Day'] = df['Date'].dt.day\n",
    "# data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "fe261486-40b4-4c60-aed8-5df81b664ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>maxtemp_sc</th>\n",
       "      <th>maxtemp_noaa</th>\n",
       "      <th>maxtemp_vc</th>\n",
       "      <th>maxtemp_mt</th>\n",
       "      <th>maxtemp_om</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>City_Austin</th>\n",
       "      <th>City_Chicago</th>\n",
       "      <th>City_Miami</th>\n",
       "      <th>City_NYC</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>82.0</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>73.133938</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.811905</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>81.0</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>73.133938</td>\n",
       "      <td>77.0</td>\n",
       "      <td>79.511900</td>\n",
       "      <td>79.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>84.0</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.133938</td>\n",
       "      <td>75.9</td>\n",
       "      <td>81.491900</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>87.0</td>\n",
       "      <td>70.921331</td>\n",
       "      <td>73.133938</td>\n",
       "      <td>88.0</td>\n",
       "      <td>84.731895</td>\n",
       "      <td>87.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>72.0</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>73.133938</td>\n",
       "      <td>84.9</td>\n",
       "      <td>67.091900</td>\n",
       "      <td>77.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5839</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>43.400000</td>\n",
       "      <td>44.1</td>\n",
       "      <td>41.383400</td>\n",
       "      <td>43.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5840</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>44.1</td>\n",
       "      <td>39.673400</td>\n",
       "      <td>43.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>44.1</td>\n",
       "      <td>40.123398</td>\n",
       "      <td>43.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5842</th>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.553400</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5843</th>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>37.300000</td>\n",
       "      <td>37.9</td>\n",
       "      <td>33.643400</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5844 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  maxtemp_sc  maxtemp_noaa  maxtemp_vc  maxtemp_mt  maxtemp_om  \\\n",
       "0    2020-01-01        82.0     79.000000   73.133938        82.0   76.811905   \n",
       "1    2020-01-02        81.0     77.000000   73.133938        77.0   79.511900   \n",
       "2    2020-01-03        84.0     78.000000   73.133938        75.9   81.491900   \n",
       "3    2020-01-04        87.0     70.921331   73.133938        88.0   84.731895   \n",
       "4    2020-01-05        72.0     83.000000   73.133938        84.9   67.091900   \n",
       "...         ...         ...           ...         ...         ...         ...   \n",
       "5839 2023-12-27        44.0     44.000000   43.400000        44.1   41.383400   \n",
       "5840 2023-12-28        44.0     44.000000   42.800000        44.1   39.673400   \n",
       "5841 2023-12-29        44.0     44.000000   42.800000        44.1   40.123398   \n",
       "5842 2023-12-30        39.0     39.000000   37.500000        39.0   42.553400   \n",
       "5843 2023-12-31        38.0     38.000000   37.300000        37.9   33.643400   \n",
       "\n",
       "      max_temp  City_Austin  City_Chicago  City_Miami  City_NYC  Year  Month  \\\n",
       "0         80.0        False         False        True     False  2020      1   \n",
       "1         79.0        False         False        True     False  2020      1   \n",
       "2         80.0        False         False        True     False  2020      1   \n",
       "3         87.0        False         False        True     False  2020      1   \n",
       "4         77.0        False         False        True     False  2020      1   \n",
       "...        ...          ...           ...         ...       ...   ...    ...   \n",
       "5839      43.0        False          True       False     False  2023     12   \n",
       "5840      43.0        False          True       False     False  2023     12   \n",
       "5841      43.0        False          True       False     False  2023     12   \n",
       "5842      39.0        False          True       False     False  2023     12   \n",
       "5843      37.0        False          True       False     False  2023     12   \n",
       "\n",
       "      Day  \n",
       "0       1  \n",
       "1       2  \n",
       "2       3  \n",
       "3       4  \n",
       "4       5  \n",
       "...   ...  \n",
       "5839   27  \n",
       "5840   28  \n",
       "5841   29  \n",
       "5842   30  \n",
       "5843   31  \n",
       "\n",
       "[5844 rows x 14 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = pd.get_dummies(data_imputed, columns=['City'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c7b41b49-a3c3-4801-bb32-4cc6ab697693",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no NaN values in the DataFrame.\n"
     ]
    }
   ],
   "source": [
    "# Ensure no NaN values remnaining\n",
    "if df.isna().any().any():\n",
    "    print(\"There are still NaN values in the DataFrame.\")\n",
    "else:\n",
    "    print(\"There are no NaN values in the DataFrame.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f431caa5-52e7-474d-98f5-652d0c35ace1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: []\n"
     ]
    }
   ],
   "source": [
    "# Check for NaN values in each column\n",
    "nan_columns = df.columns[df.isna().any()].tolist()\n",
    "\n",
    "# Print columns with NaN values\n",
    "print(\"Columns with NaN values:\", nan_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "35ceaf52-6eab-43ec-ba65-4c1c9932f868",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get features and target prediction \n",
    "features = df[['Year','Month','Day'] + [col for col in df.columns if 'City_'in col]]\n",
    "target = df['max_temp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7b7a4701-24fa-421f-bd33-366ef510ebce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no NaN values present in features or target.\n"
     ]
    }
   ],
   "source": [
    "# check for NaN values in features or target - can you tell I'm having issues ;-;\n",
    "if features.isna().any().any() or target.isna().any():\n",
    "    print(\"There are still NaN values present in features or target.\")\n",
    "else:\n",
    "    print(\"There are no NaN values present in features or target.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "b80f2176-c29d-4e25-a5f9-de9ca3c7f4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "# Separate train and test sets and train Gradient Boosting Regressor model\n",
    "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "model = GradientBoostingRegressor()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "acdf6ef4-cc82-4b04-86c6-c4848c7b448c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE: 46.85289177383847, Training RMSE: 6.844917221839755\n",
      "Testing MSE: 53.912886564779136, Testing RMSE: 7.342539517413518\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "\n",
    "# Caclulate training and testing MSEs and RMSEs\n",
    "y_train_pred = model.predict(x_train)\n",
    "\n",
    "mse_train = mean_squared_error(y_train, y_train_pred)\n",
    "rmse_train = sqrt(mse_train)\n",
    "\n",
    "y_test_pred = model.predict(x_test)\n",
    "mse_test = mean_squared_error(y_test, y_test_pred)\n",
    "rmse_test = sqrt(mse_test)\n",
    "\n",
    "print(f\"Training MSE: {mse_train}, Training RMSE: {rmse_train}\")\n",
    "print(f\"Testing MSE: {mse_test}, Testing RMSE: {rmse_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "259a566e-ad02-4beb-ace7-3430882acc9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming y_train_pred, y_test_pred, y_train, y_test are available\n",
    "\n",
    "# Concatenate train and test sets\n",
    "y_pred = np.concatenate([y_train_pred, y_test_pred])\n",
    "y_true = np.concatenate([y_train, y_test])\n",
    "\n",
    "# Assuming you have a DataFrame df with a date/time column 'Date'\n",
    "# Extract the date/time column as a pandas Series\n",
    "dates = df['Date']\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(dates, y_true, label='True Values', color='cornflowerblue')\n",
    "plt.plot(dates, y_pred, label='Predicted Values', color='plum')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Max Temperature')\n",
    "plt.title('Predicted vs. True Values over Time')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "59bc44d0-e193-4bd6-8cff-b875595fdf69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming y_train_pred, y_test_pred, y_train, y_test are available\n",
    "\n",
    "# Concatenate train and test sets\n",
    "y_pred = np.concatenate([y_train_pred, y_test_pred])\n",
    "y_true = np.concatenate([y_train, y_test])\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(y_true)), y_true, label='True Values', color='cornflowerblue')\n",
    "plt.plot(range(len(y_pred)), y_pred, label='Predicted Values', color='plum')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Max Temperature')\n",
    "plt.title('Predicted vs. True Values')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ea9f40f2-cc5a-41e6-9226-63e54bd9a06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# true_temperatures = pd.DataFrame({'Date': x_test['Year'].astype(str) + '-' + x_test['Month'].astype(str) + '-' + x_test['Day'].astype(str)})\n",
    "# true_temperatures['Date'] = pd.to_datetime(true_temperatures['Date'], format='%Y-%m-%d')\n",
    "# true_temperatures['True_Max_Temp'] = y_test.values\n",
    "\n",
    "\n",
    "# # Predict max temperatures for each city\n",
    "# predicted_temperatures_df = pd.DataFrame({'Date': true_temperatures['Date']})\n",
    "# for city in cities:\n",
    "#     predicted_temp_city = []\n",
    "#     for _, row in true_temperatures.iterrows():\n",
    "#         example_features_city = example_features.copy()\n",
    "#         example_features_city.loc[:, cities] = 0  # Reset all city columns to 0\n",
    "#         example_features_city.loc[:, city] = 1   # Set the current city to 1\n",
    "#         predicted_temp = model.predict(example_features_city.iloc[[0]]).round()  # Predict temperature for the current row\n",
    "#         predicted_temp_city.append(predicted_temp[0])  # Append the predicted temperature to the list\n",
    "#     predicted_temperatures_df[city] = predicted_temp_city\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "75bf57ab-5079-4c23-bebb-3967556aa210",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "# Define Gradient Boosting Regressor\n",
    "gb_regressor = GradientBoostingRegressor()\n",
    "\n",
    "# Define hyperparameters to tune\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'learning_rate': [0.05, 0.1, 0.2],\n",
    "    'max_depth': [3, 4, 5],\n",
    "    'min_samples_split': [2, 3, 4],\n",
    "    'min_samples_leaf': [1, 2, 3]\n",
    "}\n",
    "\n",
    "# Grid search to find the best hyperparameters\n",
    "grid_search = GridSearchCV(gb_regressor, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "grid_search.fit(x_train, y_train)\n",
    "\n",
    "# Get the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "365716ea-927c-41ed-b4cc-c9418fb643dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE: 27.65141208144527\n",
      "Testing MSE: 43.401885396681074\n",
      "Training RMSE: 5.258460999327205\n",
      "Testing RMSE: 6.58801073137264\n"
     ]
    }
   ],
   "source": [
    "# Train the best model\n",
    "best_model.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_train_pred_best = best_model.predict(x_train)\n",
    "y_test_pred_best = best_model.predict(x_test)\n",
    "\n",
    "# Evaluate the model\n",
    "train_mse = mean_squared_error(y_train, y_train_pred_best)\n",
    "test_mse = mean_squared_error(y_test, y_test_pred_best)\n",
    "train_rmse = np.sqrt(train_mse)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "\n",
    "print(\"Training MSE:\", train_mse)\n",
    "print(\"Testing MSE:\", test_mse)\n",
    "print(\"Training RMSE:\", train_rmse)\n",
    "print(\"Testing RMSE:\", test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "3ee5429d-52e1-41d2-9ae4-d2f61dd90700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'learning_rate': 0.1, 'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "Best Model: GradientBoostingRegressor(max_depth=5, min_samples_split=3, n_estimators=200)\n"
     ]
    }
   ],
   "source": [
    "# Get the best model parameters\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Parameters:\", best_params)\n",
    "\n",
    "# Get the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "print(\"Best Model:\", best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "d44b7ddf-24f6-4675-8669-e1342bdede8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming y_train_pred, y_test_pred, y_train, y_test are available\n",
    "\n",
    "# Concatenate train and test sets\n",
    "y_pred_best = np.concatenate([y_train_pred_best, y_test_pred_best])\n",
    "y_true = np.concatenate([y_train, y_test])\n",
    "\n",
    "# Assuming you have a DataFrame df with a date/time column 'Date'\n",
    "# Extract the date/time column as a pandas Series\n",
    "dates = df['Date']\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(dates[:len(y_true)], y_true, label='True Values', color='cornflowerblue')\n",
    "plt.plot(dates[:len(y_pred)], y_pred_best, label='Predicted Values', color='plum')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Max Temperature')\n",
    "plt.title('Predicted vs. True Values over Time')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "966b9a44-a4cc-4236-8918-ca8949ad67ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming y_train_pred, y_test_pred, y_train, y_test are available\n",
    "\n",
    "# Concatenate train and test sets\n",
    "y_pred_best = np.concatenate([y_train_pred_best, y_test_pred_best])\n",
    "y_true = np.concatenate([y_train, y_test])\n",
    "\n",
    "# Plotting as a line graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_true, label='True Values', color='cornflowerblue')\n",
    "plt.plot(y_pred_best, label='Predicted Values', color='plum')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Max Temperature')\n",
    "plt.title('Predicted vs. True Values')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "9675d7dc-f190-4582-89e5-d32d141be787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 13.94479171566264\n",
      "Test RMSE: 14.00059093450391\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Define a pipeline with feature scaling and Ridge Regression\n",
    "model = make_pipeline(StandardScaler(), RidgeCV(alphas=[0.1, 1.0, 10.0]))\n",
    "\n",
    "# Fit the model\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# Predict and evaluate\n",
    "y_train_pred = model.predict(x_train)\n",
    "mse_train = mean_squared_error(y_train, y_train_pred)\n",
    "rmse_train = sqrt(mse_train)\n",
    "\n",
    "y_test_pred = model.predict(x_test)\n",
    "mse_test = mean_squared_error(y_test, y_test_pred)\n",
    "rmse_test = sqrt(mse_test)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print(\"Train RMSE:\", rmse_train)\n",
    "print(\"Test RMSE:\", rmse_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "aa0d730e-3892-4fdb-98f2-d2628d575cec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE scores for each fold: [14.00112997 14.06096464 14.53590623 13.47814297 13.83116787]\n",
      "Mean RMSE: 13.981462336306222\n",
      "Standard deviation of RMSE: 0.34352126037908604\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Define the Ridge Regression model with standardization\n",
    "model = make_pipeline(StandardScaler(), Ridge(alpha=1.0))  # You can adjust the alpha value as needed\n",
    "\n",
    "# Define the number of folds for k-fold cross-validation\n",
    "num_folds = 5  # You can adjust the number of folds as needed\n",
    "\n",
    "# Perform k-fold cross-validation and compute RMSE for each fold\n",
    "kfold = KFold(n_splits=num_folds, shuffle=True, random_state=42)\n",
    "rmse_scores = cross_val_score(model, features, target, cv=kfold, scoring='neg_root_mean_squared_error')\n",
    "\n",
    "# Convert negative RMSE scores to positive\n",
    "rmse_scores = -rmse_scores\n",
    "\n",
    "# Print RMSE scores for each fold\n",
    "print(\"RMSE scores for each fold:\", rmse_scores)\n",
    "\n",
    "# Calculate and print the mean and standard deviation of RMSE scores\n",
    "mean_rmse = rmse_scores.mean()\n",
    "std_rmse = rmse_scores.std()\n",
    "print(\"Mean RMSE:\", mean_rmse)\n",
    "print(\"Standard deviation of RMSE:\", std_rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "c8ad8e2c-0d75-4631-bafa-54fcee6355e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Month', 'Day', 'City_Austin', 'City_Chicago', 'City_Miami',\n",
       "       'City_NYC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get city/feature names as list\n",
    "feature_names = features.columns\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "94ef2746-8e3e-4ff7-818d-5ba6979b995e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no NaN values present in example_features.\n",
      "Predicted Maximum Temperature: 83.8171758724604\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Create example feature data frame and index by column names\n",
    "example_features = pd.DataFrame({\n",
    "    'Year': [2024],\n",
    "    'Month': [4],\n",
    "    'Day': [3],\n",
    "    'City_Austin': [0],\n",
    "    'City_NYC': [0],\n",
    "    'City_Chicago': [0],\n",
    "    'City_Miami': [1]\n",
    "})\n",
    "example_features = example_features.reindex(columns=feature_names)\n",
    "\n",
    "if example_features.isna().any().any():\n",
    "    print(\"There are NaN values present in example_features.\")\n",
    "else:\n",
    "    print(\"There are no NaN values present in example_features.\")\n",
    "\n",
    "# Predict max temp and print output\n",
    "predicted_temp = model.predict(example_features)\n",
    "print(f\"Predicted Maximum Temperature: {predicted_temp[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "fd1c3ff4-3344-40fa-9955-95eb4be0ebc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no NaN values present in example_features.\n",
      "Predicted Maximum Temperature: 78.86675767708385\n"
     ]
    }
   ],
   "source": [
    "# Create example feature data frame and index by column names\n",
    "example_features = pd.DataFrame({\n",
    "    'Year': [2024],\n",
    "    'Month': [4],\n",
    "    'Day': [3],\n",
    "    'City_Austin': [1],\n",
    "    'City_NYC': [0],\n",
    "    'City_Chicago': [0],\n",
    "    'City_Miami': [0]\n",
    "})\n",
    "example_features = example_features.reindex(columns=feature_names)\n",
    "\n",
    "if example_features.isna().any().any():\n",
    "    print(\"There are NaN values present in example_features.\")\n",
    "else:\n",
    "    print(\"There are no NaN values present in example_features.\")\n",
    "\n",
    "# Predict max temp and print output\n",
    "predicted_temp = model.predict(example_features)\n",
    "print(f\"Predicted Maximum Temperature: {predicted_temp[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "f12a9152-2ac7-4cdd-ab93-f10a7094319a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no NaN values present in example_features.\n",
      "Predicted Maximum Temperature: 58.02843252870829\n"
     ]
    }
   ],
   "source": [
    "# Create example feature data frame and index by column names\n",
    "example_features = pd.DataFrame({\n",
    "    'Year': [2024],\n",
    "    'Month': [4],\n",
    "    'Day': [3],\n",
    "    'City_Austin': [0],\n",
    "    'City_NYC': [1],\n",
    "    'City_Chicago': [0],\n",
    "    'City_Miami': [0]\n",
    "})\n",
    "example_features = example_features.reindex(columns=feature_names)\n",
    "\n",
    "if example_features.isna().any().any():\n",
    "    print(\"There are NaN values present in example_features.\")\n",
    "else:\n",
    "    print(\"There are no NaN values present in example_features.\")\n",
    "\n",
    "# Predict max temp and print output\n",
    "predicted_temp = model.predict(example_features)\n",
    "print(f\"Predicted Maximum Temperature: {predicted_temp[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "8b8d8545-e8c2-46c9-b7d1-f545b17ea401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no NaN values present in example_features.\n",
      "Predicted Maximum Temperature: 55.60417259148401\n"
     ]
    }
   ],
   "source": [
    "# Create example feature data frame and index by column names\n",
    "example_features = pd.DataFrame({\n",
    "    'Year': [2024],\n",
    "    'Month': [4],\n",
    "    'Day': [3],\n",
    "    'City_Austin': [0],\n",
    "    'City_NYC': [0],\n",
    "    'City_Chicago': [1],\n",
    "    'City_Miami': [0]\n",
    "})\n",
    "example_features = example_features.reindex(columns=feature_names)\n",
    "\n",
    "if example_features.isna().any().any():\n",
    "    print(\"There are NaN values present in example_features.\")\n",
    "else:\n",
    "    print(\"There are no NaN values present in example_features.\")\n",
    "\n",
    "# Predict max temp and print output\n",
    "predicted_temp = model.predict(example_features)\n",
    "print(f\"Predicted Maximum Temperature: {predicted_temp[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "8f63cc90-9d96-4918-9d21-f0b7046414f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted max temps: {'City_Austin': 67.0, 'City_NYC': 56.0, 'City_Chicago': 56.0, 'City_Miami': 72.0}\n"
     ]
    }
   ],
   "source": [
    "cities = ['City_Austin', 'City_NYC', 'City_Chicago', 'City_Miami']\n",
    "predicted_temperatures = {}\n",
    "\n",
    "# Get feature names used during training\n",
    "feature_names = model.feature_names_in_\n",
    "\n",
    "for city in cities:\n",
    "    example_features = pd.DataFrame({\n",
    "        'Year': [2024],\n",
    "        'Month': [4],\n",
    "        'Day': [3],\n",
    "        'City_Austin': [0],\n",
    "        'City_NYC': [0],\n",
    "        'City_Chicago': [1],\n",
    "        'City_Miami': [0]\n",
    "    })\n",
    "\n",
    "    # Reorder columns to match feature names used during training\n",
    "    example_features = example_features.reindex(columns=feature_names, fill_value=0)\n",
    "\n",
    "    example_features[city] = 1\n",
    "    predicted_temp = model.predict(example_features).round()\n",
    "    predicted_temperatures[city] = predicted_temp[0]\n",
    "\n",
    "print(f\"Predicted max temps: {predicted_temperatures}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54ddc60-5163-4a0d-80e2-c1560c699874",
   "metadata": {},
   "source": [
    "## Kalshi API and Automatic Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "272bd612-44ab-443e-a62b-50db95b6def6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exchange status response: \n",
      "{'exchange_active': True, 'trading_active': True}\n"
     ]
    }
   ],
   "source": [
    "import kalshi_python\n",
    "import time\n",
    "import json\n",
    "import uuid\n",
    "import uuid\n",
    "from kalshi_python.models import *\n",
    "from pprint import pprint\n",
    "\n",
    "config = kalshi_python.Configuration()\n",
    "# Comment the line below to use production\n",
    "config.host = 'https://demo-api.kalshi.co/trade-api/v2'\n",
    "kalshi_api = kalshi_python.ApiInstance(\n",
    "    email=\"astoicad@bu.edu\",\n",
    "    password=\"*****\", #Password has been removed for privacy reasons\n",
    "    configuration=config,\n",
    ")\n",
    "exchangeStatus = kalshi_api.get_exchange_status()\n",
    "print('Exchange status response: ')\n",
    "pprint(exchangeStatus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "10316c1d-936e-4139-8d41-9b7361dab3e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'24APR03'"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_tickers = {\n",
    "    'City_Austin' : 'HIGHAUS',\n",
    "    'City_NYC' : 'HIGHNY',\n",
    "    'City_Chicago' : 'HIGHCHI',\n",
    "    'City_Miami' : 'HIGHMIA'}\n",
    "import datetime \n",
    "current_date=datetime.datetime.now().strftime('%y%b%d').upper()\n",
    "current_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "04d6cecf-18a7-4fb8-98a9-fc7784f18ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'City_Austin': 'HIGHAUS-24APR03',\n",
       " 'City_NYC': 'HIGHNY-24APR03',\n",
       " 'City_Chicago': 'HIGHCHI-24APR03',\n",
       " 'City_Miami': 'HIGHMIA-24APR03'}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_tickers = {}\n",
    "for city, predicted_temp in predicted_temperatures.items():\n",
    "    event_tickers[city] = f\"{city_tickers[city]}-{current_date}\"\n",
    "event_tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "b20c6436-81f9-4a30-b93d-89e3de2094a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City_Austin: [{'subtitle': '71° or below', 'ticker': 'HIGHAUS-24APR03-T72'}, {'subtitle': '72° to 73°', 'ticker': 'HIGHAUS-24APR03-B72.5'}, {'subtitle': '74° to 75°', 'ticker': 'HIGHAUS-24APR03-B74.5'}, {'subtitle': '76° to 77°', 'ticker': 'HIGHAUS-24APR03-B76.5'}, {'subtitle': '78° to 79°', 'ticker': 'HIGHAUS-24APR03-B78.5'}, {'subtitle': '80° or above', 'ticker': 'HIGHAUS-24APR03-T79'}]\n",
      "City_NYC: [{'subtitle': '43° or below', 'ticker': 'HIGHNY-24APR03-T44'}, {'subtitle': '44° to 45°', 'ticker': 'HIGHNY-24APR03-B44.5'}, {'subtitle': '46° to 47°', 'ticker': 'HIGHNY-24APR03-B46.5'}, {'subtitle': '48° to 49°', 'ticker': 'HIGHNY-24APR03-B48.5'}, {'subtitle': '50° to 51°', 'ticker': 'HIGHNY-24APR03-B50.5'}, {'subtitle': '52° or above', 'ticker': 'HIGHNY-24APR03-T51'}]\n",
      "City_Chicago: [{'subtitle': '34° or below', 'ticker': 'HIGHCHI-24APR03-T35'}, {'subtitle': '35° to 36°', 'ticker': 'HIGHCHI-24APR03-B35.5'}, {'subtitle': '37° to 38°', 'ticker': 'HIGHCHI-24APR03-B37.5'}, {'subtitle': '39° to 40°', 'ticker': 'HIGHCHI-24APR03-B39.5'}, {'subtitle': '41° to 42°', 'ticker': 'HIGHCHI-24APR03-B41.5'}, {'subtitle': '43° or above', 'ticker': 'HIGHCHI-24APR03-T42'}]\n",
      "City_Miami: [{'subtitle': '83° or below', 'ticker': 'HIGHMIA-24APR03-T84'}, {'subtitle': '84° to 85°', 'ticker': 'HIGHMIA-24APR03-B84.5'}, {'subtitle': '86° to 87°', 'ticker': 'HIGHMIA-24APR03-B86.5'}, {'subtitle': '88° to 89°', 'ticker': 'HIGHMIA-24APR03-B88.5'}, {'subtitle': '90° to 91°', 'ticker': 'HIGHMIA-24APR03-B90.5'}, {'subtitle': '92° or above', 'ticker': 'HIGHMIA-24APR03-T91'}]\n"
     ]
    }
   ],
   "source": [
    "city_options = {}\n",
    "for city, event_ticker in event_tickers.items():\n",
    "    try:\n",
    "        eventResponse = kalshi_api.get_event(event_ticker)\n",
    "        market_options = [{'subtitle':market.subtitle, 'ticker':market.ticker} for market in eventResponse.markets]\n",
    "        city_options[city] = market_options\n",
    "    except Exception as e:\n",
    "        print(f\"Error fetching market options for {city} ({event_ticker}):{e}\")\n",
    "\n",
    "for city, options in city_options.items():\n",
    "    print(f\"{city}: {options}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bceda36-9d47-4c0d-889e-bbc085703e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "81e4d793-eb5f-4f5c-ba38-95940f489309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'City_Austin': 67.0,\n",
       " 'City_NYC': 56.0,\n",
       " 'City_Chicago': 56.0,\n",
       " 'City_Miami': 72.0}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_temperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "b1d8a6e2-6d1d-47f0-9ef3-626c390e5b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def vote(predicted_temp, market_options):\n",
    "    closest_option = None\n",
    "    closest_temp_diff = float('inf')\n",
    "\n",
    "    for option in market_options:\n",
    "        subtitle = option['subtitle']\n",
    "        temps = re.findall(r'\\d+', subtitle)\n",
    "        \n",
    "        if len(temps) == 1:\n",
    "            temp = int(temps[0])\n",
    "            temp_diff = abs(predicted_temp - temp)\n",
    "            if temp_diff < closest_temp_diff:\n",
    "                closest_temp_diff = temp_diff\n",
    "                closest_option = option\n",
    "        \n",
    "        elif len(temps) == 2:\n",
    "            range_min, range_max = map(int, temps)\n",
    "            if range_min <= predicted_temp <= range_max:\n",
    "                return option\n",
    "            else:\n",
    "                # Calculate the difference between predicted_temp and the range boundary closest to it\n",
    "                if abs(predicted_temp - range_min) < abs(predicted_temp - range_max):\n",
    "                    closest_temp = range_min\n",
    "                else:\n",
    "                    closest_temp = range_max\n",
    "                \n",
    "                temp_diff = abs(predicted_temp - closest_temp)\n",
    "                if temp_diff < closest_temp_diff:\n",
    "                    closest_temp_diff = temp_diff\n",
    "                    closest_option = option\n",
    "    \n",
    "    return closest_option\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "0f574b4b-f24e-4037-b2f8-2cbbd37b1c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subtitle': '52° or above', 'ticker': 'HIGHNY-24APR03-T51'}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_options = city_options.get('City_NYC',[])\n",
    "closest_option = vote(predicted_temp, market_options)\n",
    "closest_option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce057aba-62ee-49db-bc95-0b43cca1b10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for city, predicted_temp in predicted_temperatures.items():\n",
    "    market_options = city_options.get(city, [])\n",
    "    closest_option = vote(predicted_temp, market_options)\n",
    "\n",
    "    if closest_option:\n",
    "        ticker = closest_option['ticker']\n",
    "        print(f\"Attempting to place order for {city} with ticker {ticker} based on predicted temperature of {predicted_temp}\")\n",
    "\n",
    "        orderUuid = str(uuid.uuid4())\n",
    "        \n",
    "        try:    \n",
    "            orderResponse = kalshi_api.create_order(CreateOrderRequest(\n",
    "                ticker=ticker,\n",
    "                action='buy',\n",
    "                type='limit',\n",
    "                yes_price=99,\n",
    "                count=10,\n",
    "                client_order_id=orderUuid,\n",
    "                side='yes',\n",
    "            ))\n",
    "            print(f\"\\nOrder submitted for {city} with ticker {ticker}: {orderResponse}\")\n",
    "            pprint(orderResponse)\n",
    "        except Exception as e:\n",
    "            print(f\"\\nOrder not submitted for {city} with ticker {ticker}: {str(e)}\")\n",
    "    else:\n",
    "        print(f\"No suitable market option found for {city} with predicted temperature of {predicted_temp}\")"
   ]
  }
 ],
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