traning and test changes

app/app2.py

@@ -0,0 +1,106 @@
+import datetime
+import joblib
+from math import radians
+
+import pandas as pd
+import numpy as np
+import plotly.express as px
+from matplotlib import pyplot
+import warnings
+
+import hopsworks
+
+import streamlit as st
+
+import folium
+from streamlit_folium import st_folium
+import json
+
+start_date = (datetime.datetime.now() - datetime.timedelta(hours=200)) 
+end_date = (datetime.datetime.now()) 
+
+synthetic_data.set_random_seed(12345)
+credit_cards = [cc["cc_num"] for cc in synthetic_data.generate_list_credit_card_numbers()]
+lat = 0
+long = 0
+
+warnings.filterwarnings("ignore")
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
+def retrieve_dataset(fv, start_date, end_date):
+    st.write(36 * "-")
+    print_fancy_header('\n💾 Dataset Retrieving...')
+    batch_data = fv.get_batch_data(start_time = start_date, end_time = end_date)
+    batch_data.drop(["tid", "cc_num", "datetime"], axis = 1, inplace=True)
+    return batch_data
+
+
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
+def get_feature_view():
+    fv = fs.get_feature_view("cc_trans_fraud", 1)
+    return fv
+
+
+@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+def get_model(project = project):
+    mr = project.get_model_registry()
+    model = mr.get_model("cc_fraud", version = 1)
+    model_dir = model.download()
+    return joblib.load(model_dir + "/cc_fraud_model.pkl")
+
+def explore_data(batch_data):
+    st.write(36 * "-")
+    print_fancy_header('\n👁 Data Exploration...')
+    labels = ["Suspected of Fraud", "Not Suspected of Fraud"]
+    unique, counts = np.unique(batch_data.fraud.values, return_counts=True)
+    values = counts.tolist()
+
+    def plot_pie(values, labels):
+        fig = px.pie(values=values, names=labels, title='Distribution of predicted fraud transactions')
+        return fig
+
+    fig1 = plot_pie(values, labels)
+    st.plotly_chart(fig1)
+
+
+def print_fancy_header(text, font_size=24):
+    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
+    st.markdown(res, unsafe_allow_html=True)
+
+def transform_preds(predictions):
+    return ['Fraud' if pred == 1 else 'Not Fraud' for pred in predictions]    
+
+progress_bar = st.sidebar.header('⚙️ Working Progress')
+progress_bar = st.sidebar.progress(0)
+st.title('🆘 Fraud transactions detection 🆘')
+
+st.write(36 * "-")
+print_fancy_header('\n📡 Connecting to Hopsworks Feature Store...')
+
+st.write(36 * "-")
+print_fancy_header('\n🤖 Connecting to Model Registry on Hopsworks...')
+model = get_model(project)
+st.write(model)
+st.write("✅ Connected!")
+
+progress_bar.progress(40)
+
+st.write(36 * "-")
+print_fancy_header('\n✨ Fetch batch data and predict')
+fv = get_feature_view()
+
+
+if st.button('📊 Make a prediction'):
+    batch_data = retrieve_dataset(fv, start_date, end_date)
+    st.write("✅ Retrieved!")
+    progress_bar.progress(55)
+    predictions = model.predict(batch_data)
+    predictions = transform_preds(predictions)
+    batch_data_to_explore = batch_data.copy()
+    batch_data_to_explore['fraud'] = predictions
+    explore_data(batch_data_to_explore)
+
+st.button("Re-run")

features/calendar.py

@@ -0,0 +1,34 @@
+from datetime import datetime, date
+import numpy as np
+import pandas as pd
+
+
+def get_calendar() -> pd.DataFrame:
+    """
+    Fetches calendar for Denmark.
+
+    Parameters:
+    - ....
+
+    Returns:
+    - pd.DataFrame: DataFrame with danish calendar.
+    """
+
+    df = pd.read_csv('https://raw.githubusercontent.com/Camillahannesbo/MLOPs-Assignment-/main/data/calendar_incl_holiday.csv', delimiter=';', usecols=['date', 'type'])
+
+    # Formatting the date column to 'YYYY-MM-DD' dateformat
+    df["date"] = df["date"].map(lambda x: datetime.strptime(x, '%d/%m/%Y').strftime("%Y-%m-%d"))
+
+    # Add features to the calender dataframe
+    df['date_'] = pd.to_datetime(df['date'])
+    df['dayofweek'] = df['date_'].dt.dayofweek
+    df['day'] = df['date_'].dt.day
+    df['month'] = df['date_'].dt.month
+    df['year'] = df['date_'].dt.year
+    df['holiday'] = np.where(df['type'] == 'Not a Workday', 1, 0)
+
+    # Drop the columns 'type' and 'date_' to finalize the calender dataframe
+    calendar = df.drop(['type','date_'], axis=1)
+
+    # Return the DataFrame with weather data
+    return calendar

features/electricity_prices.py

@@ -31,7 +31,10 @@ def electricity_prices(historical: bool = False, area: list = None, start: str =
 
     # Format date and time
     df["date"] = df["HourDK"].map(lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S').strftime("%Y-%m-%d"))
-    df['time'] = pd.to_datetime(df['HourDK'])
+    df['datetime'] = pd.to_datetime(df['HourDK'])
+    # df['time'] = pd.to_datetime(df['datetime']).dt.time
+    df['hour'] = pd.to_datetime(df['datetime']).dt.hour
+
 
     # Dicide the price to KWH
     df['SpotPriceDKK_KWH'] = df['SpotPriceDKK'] / 1000
@@ -53,16 +56,16 @@ def electricity_prices(historical: bool = False, area: list = None, start: str =
         filtered_df = filtered_df[filtered_df.date == today]
 
     # Convert datetime to timestamp in milliseconds and add it as a new column
-    filtered_df["timestamp"] = filtered_df["time"].apply(lambda x: int(x.timestamp() * 1000))
+    filtered_df["timestamp"] = filtered_df["datetime"].apply(lambda x: int(x.timestamp() * 1000))
 
     # Reset the index to avoid duplicate entries
     filtered_df.reset_index(drop=True, inplace=True)
 
     # Select relevant columns for weather data and reorder them
-    reordered_df = filtered_df[['timestamp', 'date', 'time', 'PriceArea', 'SpotPriceDKK_KWH']]
+    reordered_df = filtered_df[['timestamp', 'datetime', 'date', 'hour', 'PriceArea', 'SpotPriceDKK_KWH']]
 
     # Unpivot DataFrame
-    reordered_df = reordered_df.melt(id_vars=["timestamp", "time", "date", "PriceArea"], var_name="attribute", value_name="value")
+    reordered_df = reordered_df.melt(id_vars=['timestamp', 'datetime', 'date', 'hour', "PriceArea"], var_name="attribute", value_name="value")
 
     # Combine columns into a single "heading" column
     reordered_df["heading"] = reordered_df["PriceArea"] + "_" + reordered_df["attribute"]
@@ -72,7 +75,7 @@ def electricity_prices(historical: bool = False, area: list = None, start: str =
     reordered_df.drop(columns=["attribute"], inplace=True)
 
     # Pivot DataFrame
-    electricity_prices = reordered_df.pivot_table(index=["timestamp", "time", "date"], columns="heading", values="value").reset_index()
+    electricity_prices = reordered_df.pivot_table(index=['timestamp', 'datetime', 'date', 'hour'], columns="heading", values="value").reset_index()
 
     # Converting column names to lowercase for consistency
     electricity_prices.columns = list(map(str.lower, electricity_prices.columns))
@@ -109,7 +112,9 @@ def forecast_renewable_energy(historical: bool = False, area: str = None, start:
 
     # Format date and time
     df["date"] = df["HourDK"].map(lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S').strftime("%Y-%m-%d"))
-    df['time'] = pd.to_datetime(df['HourDK'])
+    df['datetime'] = pd.to_datetime(df['HourDK'])
+    # df['time'] = pd.to_datetime(df['datetime']).dt.time
+    df['hour'] = pd.to_datetime(df['datetime']).dt.hour
 
     # Drop unnecessary columns
     df.drop('Forecast5Hour', axis=1, inplace=True)
@@ -135,7 +140,7 @@ def forecast_renewable_energy(historical: bool = False, area: str = None, start:
         filtered_df = filtered_df[df.date == today]
 
     # Convert datetime to timestamp in milliseconds and add it as a new column
-    filtered_df["timestamp"] = filtered_df["time"].apply(lambda x: int(x.timestamp() * 1000))
+    filtered_df["timestamp"] = filtered_df["datetime"].apply(lambda x: int(x.timestamp() * 1000))
 
     # Divide specified columns by 1000
     filtered_df["ForecastIntraday_KWH"] = filtered_df["ForecastIntraday"] / 1000
@@ -147,10 +152,10 @@ def forecast_renewable_energy(historical: bool = False, area: str = None, start:
     filtered_df.reset_index(drop=True, inplace=True)
 
     # Select relevant columns for weather data and reorder them
-    reordered_df = filtered_df[['timestamp', 'date', 'time', 'PriceArea', 'ForecastType', 'ForecastIntraday_KWH']]
+    reordered_df = filtered_df[['timestamp', 'datetime', 'date', 'hour', 'PriceArea', 'ForecastType', 'ForecastIntraday_KWH']]
 
     # Unpivot DataFrame
-    reordered_df = reordered_df.melt(id_vars=["timestamp", "time", "date", "PriceArea", "ForecastType"], var_name="attribute", value_name="value")
+    reordered_df = reordered_df.melt(id_vars=["timestamp", 'datetime', "date", "hour", "PriceArea", "ForecastType"], var_name="attribute", value_name="value")
 
     # Combine columns into a single "heading" column
     reordered_df["heading"] = reordered_df["PriceArea"] + "_" + reordered_df["ForecastType"] + "_" + reordered_df["attribute"]
@@ -161,7 +166,7 @@ def forecast_renewable_energy(historical: bool = False, area: str = None, start:
     reordered_df.drop(columns=["attribute"], inplace=True)
 
     # Pivot DataFrame
-    forecast_renewable_energy = reordered_df.pivot_table(index=["timestamp", "time", "date"], columns="heading", values="value").reset_index()
+    forecast_renewable_energy = reordered_df.pivot_table(index=["timestamp", "datetime", "date", "hour"], columns="heading", values="value").reset_index()
 
     # Converting column names to lowercase for consistency
     forecast_renewable_energy.columns = list(map(str.lower, forecast_renewable_energy.columns))

features/weather_measures.py

@@ -32,8 +32,9 @@ def historical_weather_measures(historical: bool = False, lat: float = 57.048, l
 
     # Extract date from the 'time' column and convert it to datetime format
     df["date"] = df['time'].str[:10]
-    df['time'] = pd.to_datetime(df['time'])
-
+    df['datetime'] = pd.to_datetime(df['time'])
+    # df['time'] = pd.to_datetime(df['datetime']).dt.time
+    df['hour'] = pd.to_datetime(df['datetime']).dt.hour
 
     # Filter the DataFrame based on whether historical data is requested or not
     today = (date.today()).strftime("%Y-%m-%d")
@@ -43,10 +44,10 @@ def historical_weather_measures(historical: bool = False, lat: float = 57.048, l
         df = df[df.date == today]
 
     # Convert datetime to timestamp in milliseconds and add it as a new column
-    df["timestamp"] = df["time"].apply(lambda x: int(x.timestamp() * 1000))
+    df["timestamp"] = df["datetime"].apply(lambda x: int(x.timestamp() * 1000))
 
     # Select relevant columns for weather data and reorder them
-    weather = df[['timestamp', 'date', 'time', 'temperature_2m', 'relative_humidity_2m', 'precipitation', 'rain', 'snowfall', 'weather_code', 'cloud_cover', 'wind_speed_10m', 'wind_gusts_10m']]
+    weather = df[['timestamp', 'datetime', 'date', 'hour', 'temperature_2m', 'relative_humidity_2m', 'precipitation', 'rain', 'snowfall', 'weather_code', 'cloud_cover', 'wind_speed_10m', 'wind_gusts_10m']]
 
     # Deleting rows with missing values
     weather = weather.dropna()
@@ -79,13 +80,20 @@ def forecast_weather_measures(lat: float = 57.048, lon: float = 9.9187, forecast
 
     # Extract date from the 'time' column and convert it to datetime format
     df["date"] = df['time'].str[:10]
-    df['time'] = pd.to_datetime(df['time'])
+    df['datetime'] = pd.to_datetime(df['time'])
+    # df['time'] = pd.to_datetime(df['datetime']).dt.time
+    df['hour'] = pd.to_datetime(df['datetime']).dt.hour
 
     # Convert datetime to timestamp in milliseconds and add it as a new column
-    df["timestamp"] = df["time"].apply(lambda x: int(x.timestamp() * 1000))
+    df["timestamp"] = df["datetime"].apply(lambda x: int(x.timestamp() * 1000))
 
     # Select relevant columns for forecast weather data and reorder them
-    forecast_weather = df[['timestamp', 'date', 'time', 'temperature_2m', 'relative_humidity_2m', 'precipitation', 'rain', 'snowfall', 'weather_code', 'cloud_cover', 'wind_speed_10m', 'wind_gusts_10m']]
+    forecast_weather = df[['timestamp', 'datetime', 'date', 'hour', 'temperature_2m', 'relative_humidity_2m', 'precipitation', 'rain', 'snowfall', 'weather_code', 'cloud_cover', 'wind_speed_10m', 'wind_gusts_10m']]
+
+    # Convert columns to float
+    forecast_weather['relative_humidity_2m'] = forecast_weather['relative_humidity_2m'].astype(float)
+    forecast_weather['weather_code'] = forecast_weather['weather_code'].astype(float)
+    forecast_weather['cloud_cover'] = forecast_weather['cloud_cover'].astype(float)
 
     # Deleting rows with missing values
     forecast_weather = forecast_weather.dropna()

notebooks/1_feature_backfill.ipynb

@@ -56,7 +56,7 @@
     "\n",
     "# Now we import the functions from the features folder\n",
     "# This is the functions we have created to generate features for electricity prices and weather measures\n",
-    "from features import electricity_prices, weather_measures \n",
+    "from features import electricity_prices, weather_measures, calendar\n",
     "\n",
     "# We go back into the notebooks folder\n",
     "%cd notebooks"
@@ -70,6 +70,7 @@
    "source": [
     "# Importing the packages for the needed libraries for the Jupyter notebook\n",
     "import pandas as pd\n",
+    "import numpy as np\n",
     "import requests\n",
     "from datetime import datetime, timedelta\n",
     "\n",
@@ -146,8 +147,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
-       "      <th>time</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
+       "      <th>hour</th>\n",
        "      <th>dk1_spotpricedkk_kwh</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -157,6 +159,7 @@
        "      <td>1640995200000</td>\n",
        "      <td>2022-01-01 00:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>0</td>\n",
        "      <td>0.37220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -164,6 +167,7 @@
        "      <td>1640998800000</td>\n",
        "      <td>2022-01-01 01:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.30735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -171,6 +175,7 @@
        "      <td>1641002400000</td>\n",
        "      <td>2022-01-01 02:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>2</td>\n",
        "      <td>0.32141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -178,6 +183,7 @@
        "      <td>1641006000000</td>\n",
        "      <td>2022-01-01 03:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>3</td>\n",
        "      <td>0.33806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -185,6 +191,7 @@
        "      <td>1641009600000</td>\n",
        "      <td>2022-01-01 04:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>4</td>\n",
        "      <td>0.28013</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -192,12 +199,12 @@
        "</div>"
       ],
       "text/plain": [
-       "       timestamp                time        date  dk1_spotpricedkk_kwh\n",
-       "0  1640995200000 2022-01-01 00:00:00  2022-01-01               0.37220\n",
-       "1  1640998800000 2022-01-01 01:00:00  2022-01-01               0.30735\n",
-       "2  1641002400000 2022-01-01 02:00:00  2022-01-01               0.32141\n",
-       "3  1641006000000 2022-01-01 03:00:00  2022-01-01               0.33806\n",
-       "4  1641009600000 2022-01-01 04:00:00  2022-01-01               0.28013"
+       "       timestamp            datetime        date  hour  dk1_spotpricedkk_kwh\n",
+       "0  1640995200000 2022-01-01 00:00:00  2022-01-01     0               0.37220\n",
+       "1  1640998800000 2022-01-01 01:00:00  2022-01-01     1               0.30735\n",
+       "2  1641002400000 2022-01-01 02:00:00  2022-01-01     2               0.32141\n",
+       "3  1641006000000 2022-01-01 03:00:00  2022-01-01     3               0.33806\n",
+       "4  1641009600000 2022-01-01 04:00:00  2022-01-01     4               0.28013"
       ]
      },
      "execution_count": 5,
@@ -237,58 +244,71 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
-       "      <th>time</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
+       "      <th>hour</th>\n",
        "      <th>dk1_spotpricedkk_kwh</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>20416</th>\n",
-       "      <td>1714503600000</td>\n",
-       "      <td>2024-04-30 19:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.48640</td>\n",
+       "      <th>20440</th>\n",
+       "      <td>1714590000000</td>\n",
+       "      <td>2024-05-01 19:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.37590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20417</th>\n",
-       "      <td>1714507200000</td>\n",
-       "      <td>2024-04-30 20:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.48275</td>\n",
+       "      <th>20441</th>\n",
+       "      <td>1714593600000</td>\n",
+       "      <td>2024-05-01 20:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.37292</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20418</th>\n",
-       "      <td>1714510800000</td>\n",
-       "      <td>2024-04-30 21:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.49259</td>\n",
+       "      <th>20442</th>\n",
+       "      <td>1714597200000</td>\n",
+       "      <td>2024-05-01 21:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.25366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20419</th>\n",
-       "      <td>1714514400000</td>\n",
-       "      <td>2024-04-30 22:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.40340</td>\n",
+       "      <th>20443</th>\n",
+       "      <td>1714600800000</td>\n",
+       "      <td>2024-05-01 22:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.22315</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20420</th>\n",
-       "      <td>1714518000000</td>\n",
-       "      <td>2024-04-30 23:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.38438</td>\n",
+       "      <th>20444</th>\n",
+       "      <td>1714604400000</td>\n",
+       "      <td>2024-05-01 23:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.16408</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           timestamp                time        date  dk1_spotpricedkk_kwh\n",
-       "20416  1714503600000 2024-04-30 19:00:00  2024-04-30               0.48640\n",
-       "20417  1714507200000 2024-04-30 20:00:00  2024-04-30               0.48275\n",
-       "20418  1714510800000 2024-04-30 21:00:00  2024-04-30               0.49259\n",
-       "20419  1714514400000 2024-04-30 22:00:00  2024-04-30               0.40340\n",
-       "20420  1714518000000 2024-04-30 23:00:00  2024-04-30               0.38438"
+       "           timestamp            datetime        date  hour  \\\n",
+       "20440  1714590000000 2024-05-01 19:00:00  2024-05-01    19   \n",
+       "20441  1714593600000 2024-05-01 20:00:00  2024-05-01    20   \n",
+       "20442  1714597200000 2024-05-01 21:00:00  2024-05-01    21   \n",
+       "20443  1714600800000 2024-05-01 22:00:00  2024-05-01    22   \n",
+       "20444  1714604400000 2024-05-01 23:00:00  2024-05-01    23   \n",
+       "\n",
+       "       dk1_spotpricedkk_kwh  \n",
+       "20440               0.37590  \n",
+       "20441               0.37292  \n",
+       "20442               0.25366  \n",
+       "20443               0.22315  \n",
+       "20444               0.16408  "
       ]
      },
      "execution_count": 6,
@@ -311,16 +331,17 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 20421 entries, 0 to 20420\n",
-      "Data columns (total 4 columns):\n",
+      "RangeIndex: 20445 entries, 0 to 20444\n",
+      "Data columns (total 5 columns):\n",
       " #   Column                Non-Null Count  Dtype         \n",
       "---  ------                --------------  -----         \n",
-      " 0   timestamp             20421 non-null  int64         \n",
-      " 1   time                  20421 non-null  datetime64[ns]\n",
-      " 2   date                  20421 non-null  object        \n",
-      " 3   dk1_spotpricedkk_kwh  20421 non-null  float64       \n",
-      "dtypes: datetime64[ns](1), float64(1), int64(1), object(1)\n",
-      "memory usage: 638.3+ KB\n"
+      " 0   timestamp             20445 non-null  int64         \n",
+      " 1   datetime              20445 non-null  datetime64[ns]\n",
+      " 2   date                  20445 non-null  object        \n",
+      " 3   hour                  20445 non-null  int64         \n",
+      " 4   dk1_spotpricedkk_kwh  20445 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
+      "memory usage: 798.8+ KB\n"
      ]
     }
    ],
@@ -381,8 +402,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
-       "      <th>time</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
+       "      <th>hour</th>\n",
        "      <th>dk1_offshore_wind_forecastintraday_kwh</th>\n",
        "      <th>dk1_onshore_wind_forecastintraday_kwh</th>\n",
        "      <th>dk1_solar_forecastintraday_kwh</th>\n",
@@ -394,6 +416,7 @@
        "      <td>1641024000000</td>\n",
        "      <td>2022-01-01 08:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>8</td>\n",
        "      <td>0.611708</td>\n",
        "      <td>0.236792</td>\n",
        "      <td>0.000050</td>\n",
@@ -403,6 +426,7 @@
        "      <td>1641027600000</td>\n",
        "      <td>2022-01-01 09:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>9</td>\n",
        "      <td>0.459708</td>\n",
        "      <td>0.196667</td>\n",
        "      <td>0.004841</td>\n",
@@ -412,6 +436,7 @@
        "      <td>1641031200000</td>\n",
        "      <td>2022-01-01 10:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>10</td>\n",
        "      <td>0.310375</td>\n",
        "      <td>0.178500</td>\n",
        "      <td>0.020353</td>\n",
@@ -421,6 +446,7 @@
        "      <td>1641034800000</td>\n",
        "      <td>2022-01-01 11:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>11</td>\n",
        "      <td>0.320750</td>\n",
        "      <td>0.201125</td>\n",
        "      <td>0.035719</td>\n",
@@ -430,6 +456,7 @@
        "      <td>1641038400000</td>\n",
        "      <td>2022-01-01 12:00:00</td>\n",
        "      <td>2022-01-01</td>\n",
+       "      <td>12</td>\n",
        "      <td>0.355667</td>\n",
        "      <td>0.277667</td>\n",
        "      <td>0.038027</td>\n",
@@ -439,12 +466,12 @@
        "</div>"
       ],
       "text/plain": [
-       "       timestamp                time        date  \\\n",
-       "0  1641024000000 2022-01-01 08:00:00  2022-01-01   \n",
-       "1  1641027600000 2022-01-01 09:00:00  2022-01-01   \n",
-       "2  1641031200000 2022-01-01 10:00:00  2022-01-01   \n",
-       "3  1641034800000 2022-01-01 11:00:00  2022-01-01   \n",
-       "4  1641038400000 2022-01-01 12:00:00  2022-01-01   \n",
+       "       timestamp            datetime        date  hour  \\\n",
+       "0  1641024000000 2022-01-01 08:00:00  2022-01-01     8   \n",
+       "1  1641027600000 2022-01-01 09:00:00  2022-01-01     9   \n",
+       "2  1641031200000 2022-01-01 10:00:00  2022-01-01    10   \n",
+       "3  1641034800000 2022-01-01 11:00:00  2022-01-01    11   \n",
+       "4  1641038400000 2022-01-01 12:00:00  2022-01-01    12   \n",
        "\n",
        "   dk1_offshore_wind_forecastintraday_kwh  \\\n",
        "0                                0.611708   \n",
@@ -498,8 +525,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
-       "      <th>time</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
+       "      <th>hour</th>\n",
        "      <th>dk1_offshore_wind_forecastintraday_kwh</th>\n",
        "      <th>dk1_onshore_wind_forecastintraday_kwh</th>\n",
        "      <th>dk1_solar_forecastintraday_kwh</th>\n",
@@ -507,48 +535,53 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>14402</th>\n",
-       "      <td>1714503600000</td>\n",
-       "      <td>2024-04-30 19:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.655292</td>\n",
-       "      <td>0.998375</td>\n",
-       "      <td>0.163898</td>\n",
+       "      <th>14426</th>\n",
+       "      <td>1714590000000</td>\n",
+       "      <td>2024-05-01 19:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.816250</td>\n",
+       "      <td>1.382208</td>\n",
+       "      <td>0.272911</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14403</th>\n",
-       "      <td>1714507200000</td>\n",
-       "      <td>2024-04-30 20:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.674583</td>\n",
-       "      <td>1.042500</td>\n",
-       "      <td>0.028304</td>\n",
+       "      <th>14427</th>\n",
+       "      <td>1714593600000</td>\n",
+       "      <td>2024-05-01 20:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.848500</td>\n",
+       "      <td>1.388583</td>\n",
+       "      <td>0.046087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14404</th>\n",
-       "      <td>1714510800000</td>\n",
-       "      <td>2024-04-30 21:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.707333</td>\n",
-       "      <td>1.188708</td>\n",
-       "      <td>0.001001</td>\n",
+       "      <th>14428</th>\n",
+       "      <td>1714597200000</td>\n",
+       "      <td>2024-05-01 21:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.886042</td>\n",
+       "      <td>1.554792</td>\n",
+       "      <td>0.001339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14405</th>\n",
-       "      <td>1714514400000</td>\n",
-       "      <td>2024-04-30 22:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.728000</td>\n",
-       "      <td>1.326250</td>\n",
+       "      <th>14429</th>\n",
+       "      <td>1714600800000</td>\n",
+       "      <td>2024-05-01 22:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.919417</td>\n",
+       "      <td>1.698875</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14406</th>\n",
-       "      <td>1714518000000</td>\n",
-       "      <td>2024-04-30 23:00:00</td>\n",
-       "      <td>2024-04-30</td>\n",
-       "      <td>0.727333</td>\n",
-       "      <td>1.366417</td>\n",
+       "      <th>14430</th>\n",
+       "      <td>1714604400000</td>\n",
+       "      <td>2024-05-01 23:00:00</td>\n",
+       "      <td>2024-05-01</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.934708</td>\n",
+       "      <td>1.739375</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -556,26 +589,26 @@
        "</div>"
       ],
       "text/plain": [
-       "           timestamp                time        date  \\\n",
-       "14402  1714503600000 2024-04-30 19:00:00  2024-04-30   \n",
-       "14403  1714507200000 2024-04-30 20:00:00  2024-04-30   \n",
-       "14404  1714510800000 2024-04-30 21:00:00  2024-04-30   \n",
-       "14405  1714514400000 2024-04-30 22:00:00  2024-04-30   \n",
-       "14406  1714518000000 2024-04-30 23:00:00  2024-04-30   \n",
+       "           timestamp            datetime        date  hour  \\\n",
+       "14426  1714590000000 2024-05-01 19:00:00  2024-05-01    19   \n",
+       "14427  1714593600000 2024-05-01 20:00:00  2024-05-01    20   \n",
+       "14428  1714597200000 2024-05-01 21:00:00  2024-05-01    21   \n",
+       "14429  1714600800000 2024-05-01 22:00:00  2024-05-01    22   \n",
+       "14430  1714604400000 2024-05-01 23:00:00  2024-05-01    23   \n",
        "\n",
        "       dk1_offshore_wind_forecastintraday_kwh  \\\n",
-       "14402                                0.655292   \n",
-       "14403                                0.674583   \n",
-       "14404                                0.707333   \n",
-       "14405                                0.728000   \n",
-       "14406                                0.727333   \n",
+       "14426                                0.816250   \n",
+       "14427                                0.848500   \n",
+       "14428                                0.886042   \n",
+       "14429                                0.919417   \n",
+       "14430                                0.934708   \n",
        "\n",
        "       dk1_onshore_wind_forecastintraday_kwh  dk1_solar_forecastintraday_kwh  \n",
-       "14402                               0.998375                        0.163898  \n",
-       "14403                               1.042500                        0.028304  \n",
-       "14404                               1.188708                        0.001001  \n",
-       "14405                               1.326250                        0.000000  \n",
-       "14406                               1.366417                        0.000000  "
+       "14426                               1.382208                        0.272911  \n",
+       "14427                               1.388583                        0.046087  \n",
+       "14428                               1.554792                        0.001339  \n",
+       "14429                               1.698875                        0.000000  \n",
+       "14430                               1.739375                        0.000000  "
       ]
      },
      "execution_count": 10,
@@ -598,18 +631,19 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 14407 entries, 0 to 14406\n",
-      "Data columns (total 6 columns):\n",
+      "RangeIndex: 14431 entries, 0 to 14430\n",
+      "Data columns (total 7 columns):\n",
       " #   Column                                  Non-Null Count  Dtype         \n",
       "---  ------                                  --------------  -----         \n",
-      " 0   timestamp                               14407 non-null  int64         \n",
-      " 1   time                                    14407 non-null  datetime64[ns]\n",
-      " 2   date                                    14407 non-null  object        \n",
-      " 3   dk1_offshore_wind_forecastintraday_kwh  14391 non-null  float64       \n",
-      " 4   dk1_onshore_wind_forecastintraday_kwh   14391 non-null  float64       \n",
-      " 5   dk1_solar_forecastintraday_kwh          14391 non-null  float64       \n",
-      "dtypes: datetime64[ns](1), float64(3), int64(1), object(1)\n",
-      "memory usage: 675.5+ KB\n"
+      " 0   timestamp                               14431 non-null  int64         \n",
+      " 1   datetime                                14431 non-null  datetime64[ns]\n",
+      " 2   date                                    14431 non-null  object        \n",
+      " 3   hour                                    14431 non-null  int64         \n",
+      " 4   dk1_offshore_wind_forecastintraday_kwh  14415 non-null  float64       \n",
+      " 5   dk1_onshore_wind_forecastintraday_kwh   14415 non-null  float64       \n",
+      " 6   dk1_solar_forecastintraday_kwh          14415 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(3), int64(2), object(1)\n",
+      "memory usage: 789.3+ KB\n"
      ]
     }
    ],
@@ -676,8 +710,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
-       "      <th>time</th>\n",
+       "      <th>hour</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>relative_humidity_2m</th>\n",
        "      <th>precipitation</th>\n",
@@ -693,8 +728,9 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1640995200000</td>\n",
-       "      <td>2022-01-01</td>\n",
        "      <td>2022-01-01 00:00:00</td>\n",
+       "      <td>2022-01-01</td>\n",
+       "      <td>0</td>\n",
        "      <td>6.7</td>\n",
        "      <td>100.0</td>\n",
        "      <td>0.0</td>\n",
@@ -708,8 +744,9 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1640998800000</td>\n",
-       "      <td>2022-01-01</td>\n",
        "      <td>2022-01-01 01:00:00</td>\n",
+       "      <td>2022-01-01</td>\n",
+       "      <td>1</td>\n",
        "      <td>6.6</td>\n",
        "      <td>100.0</td>\n",
        "      <td>0.0</td>\n",
@@ -723,8 +760,9 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1641002400000</td>\n",
-       "      <td>2022-01-01</td>\n",
        "      <td>2022-01-01 02:00:00</td>\n",
+       "      <td>2022-01-01</td>\n",
+       "      <td>2</td>\n",
        "      <td>6.7</td>\n",
        "      <td>99.0</td>\n",
        "      <td>0.0</td>\n",
@@ -738,8 +776,9 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1641006000000</td>\n",
-       "      <td>2022-01-01</td>\n",
        "      <td>2022-01-01 03:00:00</td>\n",
+       "      <td>2022-01-01</td>\n",
+       "      <td>3</td>\n",
        "      <td>6.7</td>\n",
        "      <td>100.0</td>\n",
        "      <td>0.0</td>\n",
@@ -753,8 +792,9 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1641009600000</td>\n",
-       "      <td>2022-01-01</td>\n",
        "      <td>2022-01-01 04:00:00</td>\n",
+       "      <td>2022-01-01</td>\n",
+       "      <td>4</td>\n",
        "      <td>6.7</td>\n",
        "      <td>99.0</td>\n",
        "      <td>0.0</td>\n",
@@ -770,12 +810,12 @@
        "</div>"
       ],
       "text/plain": [
-       "       timestamp        date                time  temperature_2m  \\\n",
-       "0  1640995200000  2022-01-01 2022-01-01 00:00:00             6.7   \n",
-       "1  1640998800000  2022-01-01 2022-01-01 01:00:00             6.6   \n",
-       "2  1641002400000  2022-01-01 2022-01-01 02:00:00             6.7   \n",
-       "3  1641006000000  2022-01-01 2022-01-01 03:00:00             6.7   \n",
-       "4  1641009600000  2022-01-01 2022-01-01 04:00:00             6.7   \n",
+       "       timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "0  1640995200000 2022-01-01 00:00:00  2022-01-01     0             6.7   \n",
+       "1  1640998800000 2022-01-01 01:00:00  2022-01-01     1             6.6   \n",
+       "2  1641002400000 2022-01-01 02:00:00  2022-01-01     2             6.7   \n",
+       "3  1641006000000 2022-01-01 03:00:00  2022-01-01     3             6.7   \n",
+       "4  1641009600000 2022-01-01 04:00:00  2022-01-01     4             6.7   \n",
        "\n",
        "   relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
        "0                 100.0            0.0   0.0       0.0           3.0   \n",
@@ -829,8 +869,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
-       "      <th>time</th>\n",
+       "      <th>hour</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>relative_humidity_2m</th>\n",
        "      <th>precipitation</th>\n",
@@ -844,105 +885,110 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>20395</th>\n",
-       "      <td>1714417200000</td>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>2024-04-29 19:00:00</td>\n",
-       "      <td>10.6</td>\n",
-       "      <td>76.0</td>\n",
+       "      <th>20419</th>\n",
+       "      <td>1714503600000</td>\n",
+       "      <td>2024-04-30 19:00:00</td>\n",
+       "      <td>2024-04-30</td>\n",
+       "      <td>19</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>64.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>12.9</td>\n",
-       "      <td>21.2</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>26.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20396</th>\n",
-       "      <td>1714420800000</td>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>2024-04-29 20:00:00</td>\n",
-       "      <td>9.7</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>20420</th>\n",
+       "      <td>1714507200000</td>\n",
+       "      <td>2024-04-30 20:00:00</td>\n",
+       "      <td>2024-04-30</td>\n",
+       "      <td>20</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>66.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>23.8</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>32.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20397</th>\n",
-       "      <td>1714424400000</td>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>2024-04-29 21:00:00</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>20421</th>\n",
+       "      <td>1714510800000</td>\n",
+       "      <td>2024-04-30 21:00:00</td>\n",
+       "      <td>2024-04-30</td>\n",
+       "      <td>21</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>67.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11.3</td>\n",
-       "      <td>22.7</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.1</td>\n",
+       "      <td>38.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20398</th>\n",
-       "      <td>1714428000000</td>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>2024-04-29 22:00:00</td>\n",
-       "      <td>8.6</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>20422</th>\n",
+       "      <td>1714514400000</td>\n",
+       "      <td>2024-04-30 22:00:00</td>\n",
+       "      <td>2024-04-30</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>67.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>19.1</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>38.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20399</th>\n",
-       "      <td>1714431600000</td>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>2024-04-29 23:00:00</td>\n",
-       "      <td>8.1</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>20423</th>\n",
+       "      <td>1714518000000</td>\n",
+       "      <td>2024-04-30 23:00:00</td>\n",
+       "      <td>2024-04-30</td>\n",
+       "      <td>23</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>20.2</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>38.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           timestamp        date                time  temperature_2m  \\\n",
-       "20395  1714417200000  2024-04-29 2024-04-29 19:00:00            10.6   \n",
-       "20396  1714420800000  2024-04-29 2024-04-29 20:00:00             9.7   \n",
-       "20397  1714424400000  2024-04-29 2024-04-29 21:00:00             9.0   \n",
-       "20398  1714428000000  2024-04-29 2024-04-29 22:00:00             8.6   \n",
-       "20399  1714431600000  2024-04-29 2024-04-29 23:00:00             8.1   \n",
+       "           timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "20419  1714503600000 2024-04-30 19:00:00  2024-04-30    19            13.8   \n",
+       "20420  1714507200000 2024-04-30 20:00:00  2024-04-30    20            13.5   \n",
+       "20421  1714510800000 2024-04-30 21:00:00  2024-04-30    21            13.4   \n",
+       "20422  1714514400000 2024-04-30 22:00:00  2024-04-30    22            12.8   \n",
+       "20423  1714518000000 2024-04-30 23:00:00  2024-04-30    23            12.0   \n",
        "\n",
        "       relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
-       "20395                  76.0            0.0   0.0       0.0           0.0   \n",
-       "20396                  80.0            0.0   0.0       0.0           0.0   \n",
-       "20397                  82.0            0.0   0.0       0.0           0.0   \n",
-       "20398                  83.0            0.0   0.0       0.0           0.0   \n",
-       "20399                  86.0            0.0   0.0       0.0           0.0   \n",
+       "20419                  64.0            0.0   0.0       0.0           0.0   \n",
+       "20420                  66.0            0.0   0.0       0.0           0.0   \n",
+       "20421                  67.0            0.0   0.0       0.0           0.0   \n",
+       "20422                  67.0            0.0   0.0       0.0           0.0   \n",
+       "20423                  70.0            0.0   0.0       0.0           0.0   \n",
        "\n",
        "       cloud_cover  wind_speed_10m  wind_gusts_10m  \n",
-       "20395          1.0            12.9            21.2  \n",
-       "20396          0.0            13.4            23.8  \n",
-       "20397          0.0            11.3            22.7  \n",
-       "20398          0.0            11.2            19.1  \n",
-       "20399          0.0            12.0            20.2  "
+       "20419          6.0            15.3            26.3  \n",
+       "20420          2.0            18.7            32.8  \n",
+       "20421         13.0            21.1            38.2  \n",
+       "20422          9.0            21.0            38.5  \n",
+       "20423         18.0            20.7            38.5  "
       ]
      },
      "execution_count": 14,
@@ -965,24 +1011,25 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 20400 entries, 0 to 20399\n",
-      "Data columns (total 12 columns):\n",
+      "Int64Index: 20424 entries, 0 to 20423\n",
+      "Data columns (total 13 columns):\n",
       " #   Column                Non-Null Count  Dtype         \n",
       "---  ------                --------------  -----         \n",
-      " 0   timestamp             20400 non-null  int64         \n",
-      " 1   date                  20400 non-null  object        \n",
-      " 2   time                  20400 non-null  datetime64[ns]\n",
-      " 3   temperature_2m        20400 non-null  float64       \n",
-      " 4   relative_humidity_2m  20400 non-null  float64       \n",
-      " 5   precipitation         20400 non-null  float64       \n",
-      " 6   rain                  20400 non-null  float64       \n",
-      " 7   snowfall              20400 non-null  float64       \n",
-      " 8   weather_code          20400 non-null  float64       \n",
-      " 9   cloud_cover           20400 non-null  float64       \n",
-      " 10  wind_speed_10m        20400 non-null  float64       \n",
-      " 11  wind_gusts_10m        20400 non-null  float64       \n",
-      "dtypes: datetime64[ns](1), float64(9), int64(1), object(1)\n",
-      "memory usage: 2.0+ MB\n"
+      " 0   timestamp             20424 non-null  int64         \n",
+      " 1   datetime              20424 non-null  datetime64[ns]\n",
+      " 2   date                  20424 non-null  object        \n",
+      " 3   hour                  20424 non-null  int64         \n",
+      " 4   temperature_2m        20424 non-null  float64       \n",
+      " 5   relative_humidity_2m  20424 non-null  float64       \n",
+      " 6   precipitation         20424 non-null  float64       \n",
+      " 7   rain                  20424 non-null  float64       \n",
+      " 8   snowfall              20424 non-null  float64       \n",
+      " 9   weather_code          20424 non-null  float64       \n",
+      " 10  cloud_cover           20424 non-null  float64       \n",
+      " 11  wind_speed_10m        20424 non-null  float64       \n",
+      " 12  wind_gusts_10m        20424 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(9), int64(2), object(1)\n",
+      "memory usage: 2.2+ MB\n"
      ]
     }
    ],
@@ -1038,8 +1085,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
-       "      <th>time</th>\n",
+       "      <th>hour</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>relative_humidity_2m</th>\n",
        "      <th>precipitation</th>\n",
@@ -1054,104 +1102,109 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1714521600000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 00:00:00</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>70</td>\n",
+       "      <td>1714608000000</td>\n",
+       "      <td>2024-05-02 00:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>66.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>46</td>\n",
-       "      <td>20.9</td>\n",
-       "      <td>36.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>41.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1714525200000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 01:00:00</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>73</td>\n",
+       "      <td>1714611600000</td>\n",
+       "      <td>2024-05-02 01:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>71.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>35.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>37.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1714528800000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 02:00:00</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>75</td>\n",
+       "      <td>1714615200000</td>\n",
+       "      <td>2024-05-02 02:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>73.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>54</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>31.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>36.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1714532400000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 03:00:00</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>76</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1714618800000</td>\n",
+       "      <td>2024-05-02 03:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
        "      <td>3</td>\n",
-       "      <td>97</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>33.1</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>22.3</td>\n",
+       "      <td>39.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1714536000000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 04:00:00</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>78</td>\n",
+       "      <td>1714622400000</td>\n",
+       "      <td>2024-05-02 04:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>73.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>96</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>33.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>38.9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       timestamp        date                time  temperature_2m  \\\n",
-       "0  1714521600000  2024-05-01 2024-05-01 00:00:00            13.4   \n",
-       "1  1714525200000  2024-05-01 2024-05-01 01:00:00            12.6   \n",
-       "2  1714528800000  2024-05-01 2024-05-01 02:00:00            12.0   \n",
-       "3  1714532400000  2024-05-01 2024-05-01 03:00:00            11.5   \n",
-       "4  1714536000000  2024-05-01 2024-05-01 04:00:00            11.2   \n",
+       "       timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "0  1714608000000 2024-05-02 00:00:00  2024-05-02     0            14.9   \n",
+       "1  1714611600000 2024-05-02 01:00:00  2024-05-02     1            14.2   \n",
+       "2  1714615200000 2024-05-02 02:00:00  2024-05-02     2            13.4   \n",
+       "3  1714618800000 2024-05-02 03:00:00  2024-05-02     3            13.2   \n",
+       "4  1714622400000 2024-05-02 04:00:00  2024-05-02     4            12.7   \n",
        "\n",
        "   relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
-       "0                    70            0.0   0.0       0.0             1   \n",
-       "1                    73            0.0   0.0       0.0             0   \n",
-       "2                    75            0.0   0.0       0.0             2   \n",
-       "3                    76            0.0   0.0       0.0             3   \n",
-       "4                    78            0.0   0.0       0.0             3   \n",
+       "0                  66.0            0.0   0.0       0.0           0.0   \n",
+       "1                  71.0            0.0   0.0       0.0           0.0   \n",
+       "2                  73.0            0.0   0.0       0.0           2.0   \n",
+       "3                  72.0            0.1   0.1       0.0          51.0   \n",
+       "4                  73.0            0.0   0.0       0.0           2.0   \n",
        "\n",
        "   cloud_cover  wind_speed_10m  wind_gusts_10m  \n",
-       "0           46            20.9            36.4  \n",
-       "1           18            18.0            35.6  \n",
-       "2           54            18.0            31.0  \n",
-       "3           97            19.4            33.1  \n",
-       "4           96            18.0            33.5  "
+       "0         13.0            21.6            41.4  \n",
+       "1          4.0            20.5            37.1  \n",
+       "2         70.0            21.2            36.7  \n",
+       "3         51.0            22.3            39.2  \n",
+       "4         78.0            21.6            38.9  "
       ]
      },
      "execution_count": 17,
@@ -1191,8 +1244,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
-       "      <th>time</th>\n",
+       "      <th>hour</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>relative_humidity_2m</th>\n",
        "      <th>precipitation</th>\n",
@@ -1207,104 +1261,109 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>115</th>\n",
-       "      <td>1714935600000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 19:00:00</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>79</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>83</td>\n",
-       "      <td>11.3</td>\n",
-       "      <td>25.9</td>\n",
+       "      <td>1715022000000</td>\n",
+       "      <td>2024-05-06 19:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>19</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>32.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>116</th>\n",
-       "      <td>1714939200000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 20:00:00</td>\n",
-       "      <td>11.7</td>\n",
-       "      <td>83</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>91</td>\n",
+       "      <td>1715025600000</td>\n",
+       "      <td>2024-05-06 20:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>20</td>\n",
        "      <td>10.1</td>\n",
-       "      <td>23.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>37.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117</th>\n",
-       "      <td>1714942800000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 21:00:00</td>\n",
-       "      <td>11.1</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>98</td>\n",
+       "      <td>1715029200000</td>\n",
+       "      <td>2024-05-06 21:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>21</td>\n",
        "      <td>9.5</td>\n",
-       "      <td>20.5</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>42.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
-       "      <td>1714946400000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 22:00:00</td>\n",
-       "      <td>10.9</td>\n",
-       "      <td>87</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1715032800000</td>\n",
+       "      <td>2024-05-06 22:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>98</td>\n",
-       "      <td>10.2</td>\n",
-       "      <td>22.3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>41.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>119</th>\n",
-       "      <td>1714950000000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 23:00:00</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>88</td>\n",
+       "      <td>1715036400000</td>\n",
+       "      <td>2024-05-06 23:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>23</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>97</td>\n",
-       "      <td>11.9</td>\n",
-       "      <td>24.1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>40.3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         timestamp        date                time  temperature_2m  \\\n",
-       "115  1714935600000  2024-05-05 2024-05-05 19:00:00            12.6   \n",
-       "116  1714939200000  2024-05-05 2024-05-05 20:00:00            11.7   \n",
-       "117  1714942800000  2024-05-05 2024-05-05 21:00:00            11.1   \n",
-       "118  1714946400000  2024-05-05 2024-05-05 22:00:00            10.9   \n",
-       "119  1714950000000  2024-05-05 2024-05-05 23:00:00            11.0   \n",
+       "         timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "115  1715022000000 2024-05-06 19:00:00  2024-05-06    19            10.7   \n",
+       "116  1715025600000 2024-05-06 20:00:00  2024-05-06    20            10.1   \n",
+       "117  1715029200000 2024-05-06 21:00:00  2024-05-06    21             9.5   \n",
+       "118  1715032800000 2024-05-06 22:00:00  2024-05-06    22             9.3   \n",
+       "119  1715036400000 2024-05-06 23:00:00  2024-05-06    23             9.1   \n",
        "\n",
        "     relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
-       "115                    79            0.0   0.0       0.0             3   \n",
-       "116                    83            0.0   0.0       0.0             3   \n",
-       "117                    86            0.0   0.0       0.0             3   \n",
-       "118                    87            0.0   0.0       0.0             3   \n",
-       "119                    88            0.0   0.0       0.0             3   \n",
+       "115                  91.0            1.4   1.4       0.0          61.0   \n",
+       "116                  90.0            1.4   1.4       0.0          61.0   \n",
+       "117                  88.0            1.4   1.4       0.0          61.0   \n",
+       "118                  86.0            0.6   0.6       0.0           3.0   \n",
+       "119                  84.0            0.6   0.6       0.0           3.0   \n",
        "\n",
        "     cloud_cover  wind_speed_10m  wind_gusts_10m  \n",
-       "115           83            11.3            25.9  \n",
-       "116           91            10.1            23.0  \n",
-       "117           98             9.5            20.5  \n",
-       "118           98            10.2            22.3  \n",
-       "119           97            11.9            24.1  "
+       "115        100.0            16.6            32.0  \n",
+       "116        100.0            19.5            37.1  \n",
+       "117        100.0            21.6            42.1  \n",
+       "118        100.0            22.0            41.0  \n",
+       "119        100.0            21.3            40.3  "
       ]
      },
      "execution_count": 18,
@@ -1328,23 +1387,24 @@
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 120 entries, 0 to 119\n",
-      "Data columns (total 12 columns):\n",
+      "Data columns (total 13 columns):\n",
       " #   Column                Non-Null Count  Dtype         \n",
       "---  ------                --------------  -----         \n",
       " 0   timestamp             120 non-null    int64         \n",
-      " 1   date                  120 non-null    object        \n",
-      " 2   time                  120 non-null    datetime64[ns]\n",
-      " 3   temperature_2m        120 non-null    float64       \n",
-      " 4   relative_humidity_2m  120 non-null    int64         \n",
-      " 5   precipitation         120 non-null    float64       \n",
-      " 6   rain                  120 non-null    float64       \n",
-      " 7   snowfall              120 non-null    float64       \n",
-      " 8   weather_code          120 non-null    int64         \n",
-      " 9   cloud_cover           120 non-null    int64         \n",
-      " 10  wind_speed_10m        120 non-null    float64       \n",
-      " 11  wind_gusts_10m        120 non-null    float64       \n",
-      "dtypes: datetime64[ns](1), float64(6), int64(4), object(1)\n",
-      "memory usage: 11.4+ KB\n"
+      " 1   datetime              120 non-null    datetime64[ns]\n",
+      " 2   date                  120 non-null    object        \n",
+      " 3   hour                  120 non-null    int64         \n",
+      " 4   temperature_2m        120 non-null    float64       \n",
+      " 5   relative_humidity_2m  120 non-null    float64       \n",
+      " 6   precipitation         120 non-null    float64       \n",
+      " 7   rain                  120 non-null    float64       \n",
+      " 8   snowfall              120 non-null    float64       \n",
+      " 9   weather_code          120 non-null    float64       \n",
+      " 10  cloud_cover           120 non-null    float64       \n",
+      " 11  wind_speed_10m        120 non-null    float64       \n",
+      " 12  wind_gusts_10m        120 non-null    float64       \n",
+      "dtypes: datetime64[ns](1), float64(9), int64(2), object(1)\n",
+      "memory usage: 12.3+ KB\n"
      ]
     }
    ],
@@ -1365,97 +1425,14 @@
    "cell_type": "code",
    "execution_count": 20,
    "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>type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>01/01/2022</td>\n",
-       "      <td>Not a Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>02/01/2022</td>\n",
-       "      <td>Not a Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>03/01/2022</td>\n",
-       "      <td>Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>04/01/2022</td>\n",
-       "      <td>Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>05/01/2022</td>\n",
-       "      <td>Workday</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         date           type\n",
-       "0  01/01/2022  Not a Workday\n",
-       "1  02/01/2022  Not a Workday\n",
-       "2  03/01/2022        Workday\n",
-       "3  04/01/2022        Workday\n",
-       "4  05/01/2022        Workday"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Read csv file with calender\n",
-    "calender_df = pd.read_csv('https://raw.githubusercontent.com/Camillahannesbo/MLOPs-Assignment-/main/data/calendar_incl_holiday.csv', delimiter=';', usecols=['date', 'type'])\n",
-    " \n",
-    "# Display the DataFrame\n",
-    "calender_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
    "outputs": [],
    "source": [
-    "# Formatting the date column to 'YYYY-MM-DD' dateformat\n",
-    "calender_df[\"date\"] = calender_df[\"date\"].map(lambda x: datetime.strptime(x, '%d/%m/%Y').strftime(\"%Y-%m-%d\"))"
+    "calender_df = calendar.get_calendar()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1480,49 +1457,61 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
-       "      <th>type</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>holiday</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2022-01-01</td>\n",
-       "      <td>Not a Workday</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2022-01-02</td>\n",
-       "      <td>Not a Workday</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2022-01-03</td>\n",
-       "      <td>Workday</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2022-01-04</td>\n",
-       "      <td>Workday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2022-01-05</td>\n",
-       "      <td>Workday</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         date           type\n",
-       "0  2022-01-01  Not a Workday\n",
-       "1  2022-01-02  Not a Workday\n",
-       "2  2022-01-03        Workday\n",
-       "3  2022-01-04        Workday\n",
-       "4  2022-01-05        Workday"
+       "         date  day  month  holiday\n",
+       "0  2022-01-01    5      1        1\n",
+       "1  2022-01-02    6      1        1\n",
+       "2  2022-01-03    0      1        0\n",
+       "3  2022-01-04    1      1        0\n",
+       "4  2022-01-05    2      1        0"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1534,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1559,49 +1548,61 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
-       "      <th>type</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>holiday</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1091</th>\n",
        "      <td>2024-12-27</td>\n",
-       "      <td>Workday</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1092</th>\n",
        "      <td>2024-12-28</td>\n",
-       "      <td>Not a Workday</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1093</th>\n",
        "      <td>2024-12-29</td>\n",
-       "      <td>Not a Workday</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1094</th>\n",
        "      <td>2024-12-30</td>\n",
-       "      <td>Workday</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1095</th>\n",
        "      <td>2024-12-31</td>\n",
-       "      <td>Workday</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "            date           type\n",
-       "1091  2024-12-27        Workday\n",
-       "1092  2024-12-28  Not a Workday\n",
-       "1093  2024-12-29  Not a Workday\n",
-       "1094  2024-12-30        Workday\n",
-       "1095  2024-12-31        Workday"
+       "            date  day  month  holiday\n",
+       "1091  2024-12-27    4     12        0\n",
+       "1092  2024-12-28    5     12        1\n",
+       "1093  2024-12-29    6     12        1\n",
+       "1094  2024-12-30    0     12        0\n",
+       "1095  2024-12-31    1     12        0"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1611,6 +1612,34 @@
     "calender_df.tail(5)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1096 entries, 0 to 1095\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   date     1096 non-null   object\n",
+      " 1   day      1096 non-null   int64 \n",
+      " 2   month    1096 non-null   int64 \n",
+      " 3   holiday  1096 non-null   int64 \n",
+      "dtypes: int64(3), object(1)\n",
+      "memory usage: 34.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Showing the information for the calender dataframe\n",
+    "calender_df.info()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -1622,13 +1651,15 @@
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 1096 entries, 0 to 1095\n",
-      "Data columns (total 2 columns):\n",
-      " #   Column  Non-Null Count  Dtype \n",
-      "---  ------  --------------  ----- \n",
-      " 0   date    1096 non-null   object\n",
-      " 1   type    1096 non-null   object\n",
-      "dtypes: object(2)\n",
-      "memory usage: 17.3+ KB\n"
+      "Data columns (total 4 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   date     1096 non-null   object\n",
+      " 1   day      1096 non-null   int64 \n",
+      " 2   month    1096 non-null   int64 \n",
+      " 3   holiday  1096 non-null   int64 \n",
+      "dtypes: int64(3), object(1)\n",
+      "memory usage: 34.4+ KB\n"
      ]
     }
    ],
@@ -1680,9 +1711,9 @@
     "### <span style=\"color:#2656a3;\"> 🪄 Creating Feature Groups\n",
     "A feature group can be seen as a collection of conceptually related features. In this case we create feature groups for the \n",
     "- eletricity price data,\n",
+    "- forecast_renewable_energy,\n",
     "- weather data,\n",
-    "- calender data, and\n",
-    "- forecast_renewable_energy.\n",
+    "- calender data.\n",
     "\n",
     "We specify a `primary_key` as `date`, so we are able to join them when we create a dataset for training later in part 03 the training_pipeline.\n",
     "We define a name and a short describtion of the feature group's contents and a version number. \n",
@@ -1698,14 +1729,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Creating the feature group for the weather data\n",
-    "weather_fg = fs.get_or_create_feature_group(\n",
-    "    name=\"weather_measurements\",\n",
+    "# Creating the feature group for the electricity prices\n",
+    "electricity_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"electricity_prices\",\n",
     "    version=1,\n",
-    "    description=\"Weather measurements from Open Meteo API\",\n",
-    "    primary_key=[\"date\",\"timestamp\"], # ,\"temperature_2m\",\"relative_humidity_2m\",\"precipitation\",\"rain\",\"snowfall\",\"weather_code\",\"cloud_cover\",\"wind_speed_10m\",\"wind_gusts_10m\"\n",
-    "    event_time=\"timestamp\",\n",
+    "    description=\"Electricity prices from Energidata API\",\n",
+    "    primary_key=[\"date\",\"timestamp\"], \n",
     "    online_enabled=True,\n",
+    "    event_time=\"timestamp\",\n",
     ")"
    ]
   },
@@ -1726,18 +1757,18 @@
      "output_type": "stream",
      "text": [
       "Feature Group created successfully, explore it at \n",
-      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/769394\n"
+      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/774496\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f5ca05f9abd42d7b45ef34c1a6cf382",
+       "model_id": "a746b736b329402299dc55c4ebd07d53",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Uploading Dataframe: 0.00% |          | Rows 0/20400 | Elapsed Time: 00:00 | Remaining Time: ?"
+       "Uploading Dataframe: 0.00% |          | Rows 0/20445 | Elapsed Time: 00:00 | Remaining Time: ?"
       ]
      },
      "metadata": {},
@@ -1747,15 +1778,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Launching job: weather_measurements_1_offline_fg_materialization\n",
+      "Launching job: electricity_prices_1_offline_fg_materialization\n",
       "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/554133/jobs/named/weather_measurements_1_offline_fg_materialization/executions\n"
+      "https://c.app.hopsworks.ai/p/554133/jobs/named/electricity_prices_1_offline_fg_materialization/executions\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x17ef77890>, None)"
+       "(<hsfs.core.job.Job at 0x16ff23950>, None)"
       ]
      },
      "execution_count": 27,
@@ -1764,8 +1795,8 @@
     }
    ],
    "source": [
-    "# Inserting the weather_df into the feature group named weather_fg\n",
-    "weather_fg.insert(historical_weather_df)"
+    "# Inserting the electricity_df into the feature group named electricity_fg\n",
+    "electricity_fg.insert(electricity_df)"
    ]
   },
   {
@@ -1781,32 +1812,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# List of descriptions for weather features\n",
-    "weather_feature_descriptions = [\n",
+    "# List of descriptions for electricity features\n",
+    "electricity_feature_descriptions = [\n",
     "    {\"name\": \"timestamp\", \"description\": \"Timestamp for the event_time\"},\n",
-    "    {\"name\": \"date\", \"description\": \"Date of the weather measurement\"},\n",
-    "    {\"name\": \"time\", \"description\": \"Time of the weather measurement\"},\n",
-    "    {\"name\": \"temperature_2m\", \"description\": \"Temperature at 2m above ground\"},\n",
-    "    {\"name\": \"relative_humidity_2m\", \"description\": \"Relative humidity at 2m above ground\"},\n",
-    "    {\"name\": \"precipitation\", \"description\": \"Precipitation\"},\n",
-    "    {\"name\": \"rain\", \"description\": \"Rain\"},\n",
-    "    {\"name\": \"snowfall\", \"description\": \"Snowfall\"},   \n",
-    "    {\"name\": \"weather_code\", \"description\": \"Weather code\"},   \n",
-    "    {\"name\": \"cloud_cover\", \"description\": \"Cloud cover\"},   \n",
-    "    {\"name\": \"wind_speed_10m\", \"description\": \"Wind speed at 10m above ground\"},   \n",
-    "    {\"name\": \"wind_gusts_10m\", \"description\": \"Wind gusts at 10m above ground\"},   \n",
+    "    {\"name\": \"date\", \"description\": \"Date of the electricity measurement\"},\n",
+    "    {\"name\": \"datetime\", \"description\": \"Date and time of the electricity measurement\"},\n",
+    "    {\"name\": \"hour\", \"description\": \"Hour of day\"},\n",
+    "    {\"name\": \"dk1_spotpricedkk_kwh\", \"description\": \"Spot price in DKK per KWH\"}, \n",
     "]\n",
     "\n",
     "# Updating feature descriptions\n",
-    "for desc in weather_feature_descriptions: \n",
-    "    weather_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
+    "for desc in electricity_feature_descriptions: \n",
+    "    electricity_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We replicate the process for both the `electricity_fg`, `forecast_renewable_energy_fg` and `danish_holidays_fg` by establishing feature groups and inserting the dataframes into their respective feature groups."
+    "We replicate the process for both the `forecast_renewable_energy_fg`, `weather_fg` and `danish_holidays_fg` by establishing feature groups and inserting the dataframes into their respective feature groups."
    ]
   },
   {
@@ -1815,67 +1839,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Creating the feature group for the electricity prices\n",
-    "electricity_fg = fs.get_or_create_feature_group(\n",
-    "    name=\"electricity_prices\",\n",
-    "    version=1,\n",
-    "    description=\"Electricity prices from Energidata API\",\n",
-    "    primary_key=[\"date\",\"timestamp\"], # \"dk1_spotpricedkk_kwh\"\n",
-    "    online_enabled=True,\n",
-    "    event_time=\"timestamp\",\n",
-    ")"
+    "# # Creating the feature group for the electricity prices\n",
+    "# forecast_renewable_energy_fg = fs.get_or_create_feature_group(\n",
+    "#     name=\"forecast_renewable_energy\",\n",
+    "#     version=1,\n",
+    "#     description=\"Forecast on Renewable Energy on ForecastType from Energidata API\",\n",
+    "#     primary_key=[\"date\",\"timestamp\"], \n",
+    "#     online_enabled=True,\n",
+    "#     event_time=\"timestamp\",\n",
+    "# )"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature Group created successfully, explore it at \n",
-      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/768394\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a07f970e652943e58fd587eacb087548",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Uploading Dataframe: 0.00% |          | Rows 0/20421 | Elapsed Time: 00:00 | Remaining Time: ?"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Launching job: electricity_prices_1_offline_fg_materialization\n",
-      "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/554133/jobs/named/electricity_prices_1_offline_fg_materialization/executions\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<hsfs.core.job.Job at 0x17ef31850>, None)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Inserting the electricity_df into the feature group named electricity_fg\n",
-    "electricity_fg.insert(electricity_df)"
+    "# # Inserting the electricity_df into the feature group named electricity_fg\n",
+    "# forecast_renewable_energy_fg.insert(forecast_renewable_energy_df)"
    ]
   },
   {
@@ -1884,17 +1866,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# List of descriptions for electricity features\n",
-    "electricity_feature_descriptions = [\n",
-    "    {\"name\": \"timestamp\", \"description\": \"Timestamp for the event_time\"},\n",
-    "    {\"name\": \"date\", \"description\": \"Date of the electricity measurement\"},\n",
-    "    {\"name\": \"time\", \"description\": \"Time of the electricity measurement\"},\n",
-    "    {\"name\": \"dk1_spotpricedkk_kwh\", \"description\": \"Spot price in DKK per KWH\"}, \n",
-    "]\n",
+    "# # List of descriptions for forecast_renewable_energy features\n",
+    "# forecast_renewable_energy_feature_descriptions = [\n",
+    "#     {\"name\": \"timestamp\", \"description\": \"Timestamp for the event_time\"},\n",
+    "#     {\"name\": \"date\", \"description\": \"Date of the forecast\"},\n",
+    "#     {\"name\": \"datetime\", \"description\": \"Date and time for the forecast\"},\n",
+    "#     {\"name\": \"hour\", \"description\": \"Hour of day\"},\n",
+    "#     {\"name\": \"dk1_offshore_wind_forecastintraday_kwh\", \"description\": \"The forecast for the coming day at 6am Danish time zone\"},\n",
+    "# ]\n",
     "\n",
-    "# Updating feature descriptions\n",
-    "for desc in electricity_feature_descriptions: \n",
-    "    electricity_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
+    "# # Updating feature descriptions\n",
+    "# for desc in forecast_renewable_energy_feature_descriptions: \n",
+    "#     forecast_renewable_energy_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
    ]
   },
   {
@@ -1903,14 +1886,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Creating the feature group for the electricity prices\n",
-    "forecast_renewable_energy_fg = fs.get_or_create_feature_group(\n",
-    "    name=\"forecast_renewable_energy\",\n",
+    "# Creating the feature group for the weather data\n",
+    "weather_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"weather_measurements\",\n",
     "    version=1,\n",
-    "    description=\"Forecast on Renewable Energy on ForecastType from Energidata API\",\n",
-    "    primary_key=[\"date\",\"timestamp\"], # ,\"dk1_offshore_wind_forecastintraday_kwh\",\"dk1_onshore_wind_forecastintraday_kwh\",\"dk1_solar_power_forecastintraday_kwh\"\n",
-    "    online_enabled=True,\n",
+    "    description=\"Weather measurements from Open Meteo API\",\n",
+    "    primary_key=[\"date\", \"timestamp\"], \n",
     "    event_time=\"timestamp\",\n",
+    "    online_enabled=True,\n",
     ")"
    ]
   },
@@ -1924,18 +1907,18 @@
      "output_type": "stream",
      "text": [
       "Feature Group created successfully, explore it at \n",
-      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/768395\n"
+      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/775512\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01f05ec67a224ab182df4309c9f67293",
+       "model_id": "a172ef7a47d14b8ab9643d864c8189d1",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Uploading Dataframe: 0.00% |          | Rows 0/14407 | Elapsed Time: 00:00 | Remaining Time: ?"
+       "Uploading Dataframe: 0.00% |          | Rows 0/20424 | Elapsed Time: 00:00 | Remaining Time: ?"
       ]
      },
      "metadata": {},
@@ -1945,15 +1928,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Launching job: forecast_renewable_energy_1_offline_fg_materialization\n",
+      "Launching job: weather_measurements_1_offline_fg_materialization\n",
       "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/554133/jobs/named/forecast_renewable_energy_1_offline_fg_materialization/executions\n"
+      "https://c.app.hopsworks.ai/p/554133/jobs/named/weather_measurements_1_offline_fg_materialization/executions\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x17dba60d0>, None)"
+       "(<hsfs.core.job.Job at 0x16ffda610>, None)"
       ]
      },
      "execution_count": 33,
@@ -1962,8 +1945,8 @@
     }
    ],
    "source": [
-    "# Inserting the electricity_df into the feature group named electricity_fg\n",
-    "forecast_renewable_energy_fg.insert(forecast_renewable_energy_df)"
+    "# Inserting the weather_df into the feature group named weather_fg\n",
+    "weather_fg.insert(historical_weather_df)"
    ]
   },
   {
@@ -1972,30 +1955,39 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# List of descriptions for forecast_renewable_energy features\n",
-    "forecast_renewable_energy_feature_descriptions = [\n",
-    "    {\"name\": \"timestamp\", \"description\": \"Timestamp for the event_time\"},\n",
-    "    {\"name\": \"date\", \"description\": \"Date\"},\n",
-    "    {\"name\": \"time\", \"description\": \"Time for the event_time\"},\n",
-    "    {\"name\": \"dk1_offshore_wind_forecastintraday_kwh\", \"description\": \"The forecast for the coming day at 6am Danish time zone\"},\n",
+    "# List of descriptions for weather features\n",
+    "weather_feature_descriptions = [\n",
+    "    {\"name\": \"timestamp\", \"description\": \"Timestamp for the weather measurement\"},\n",
+    "    {\"name\": \"date\", \"description\": \"Date of the weather measurement\"},\n",
+    "    {\"name\": \"datetime\", \"description\": \"Date and time of the weather measurement\"},\n",
+    "    {\"name\": \"hour\", \"description\": \"Hour of day\"},\n",
+    "    {\"name\": \"temperature_2m\", \"description\": \"Temperature at 2m above ground\"},\n",
+    "    {\"name\": \"relative_humidity_2m\", \"description\": \"Relative humidity at 2m above ground\"},\n",
+    "    {\"name\": \"precipitation\", \"description\": \"Precipitation\"},\n",
+    "    {\"name\": \"rain\", \"description\": \"Rain\"},\n",
+    "    {\"name\": \"snowfall\", \"description\": \"Snowfall\"},   \n",
+    "    {\"name\": \"weather_code\", \"description\": \"Weather code\"},   \n",
+    "    {\"name\": \"cloud_cover\", \"description\": \"Cloud cover\"},   \n",
+    "    {\"name\": \"wind_speed_10m\", \"description\": \"Wind speed at 10m above ground\"},   \n",
+    "    {\"name\": \"wind_gusts_10m\", \"description\": \"Wind gusts at 10m above ground\"},   \n",
     "]\n",
     "\n",
     "# Updating feature descriptions\n",
-    "for desc in forecast_renewable_energy_feature_descriptions: \n",
-    "    forecast_renewable_energy_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
+    "for desc in weather_feature_descriptions: \n",
+    "    weather_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Creating the feature group for the danish holidays\n",
-    "danish_holidays_fg = fs.get_or_create_feature_group(\n",
-    "    name=\"danish_holidayss\",\n",
-    "    version=1,\n",
-    "    description=\"Danish holidays calendar.\",\n",
+    "# Creating the feature group for the danish calendar\n",
+    "danish_calendar_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"danish_calendar\",\n",
+    "    version=2,\n",
+    "    description=\"Danish calendar.\",\n",
     "    online_enabled=True,\n",
     "    primary_key=[\"date\"],\n",
     ")"
@@ -2003,7 +1995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2011,13 +2003,13 @@
      "output_type": "stream",
      "text": [
       "Feature Group created successfully, explore it at \n",
-      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/768396\n"
+      "https://c.app.hopsworks.ai:443/p/554133/fs/549956/fg/774498\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b74b96c4b1642699979968395c6ea22",
+       "model_id": "3b61548e0184416180285726772cd490",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2032,42 +2024,44 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Launching job: danish_holidayss_1_offline_fg_materialization\n",
+      "Launching job: danish_calendar_2_offline_fg_materialization\n",
       "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/554133/jobs/named/danish_holidayss_1_offline_fg_materialization/executions\n"
+      "https://c.app.hopsworks.ai/p/554133/jobs/named/danish_calendar_2_offline_fg_materialization/executions\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x17ef334d0>, None)"
+       "(<hsfs.core.job.Job at 0x16ff71050>, None)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Inserting the calender_df into the feature group named danish_holidays_fg\n",
-    "danish_holidays_fg.insert(calender_df)"
+    "# Inserting the calender_df into the feature group named danish_calendar_fg\n",
+    "danish_calendar_fg.insert(calender_df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# List of descriptions for danish_holidays features\n",
-    "danish_holidays_feature_descriptions = [\n",
+    "# List of descriptions for danish_calendar features\n",
+    "danish_calendar_feature_descriptions = [\n",
     "    {\"name\": \"date\", \"description\": \"Date in the calendar\"},\n",
-    "    {\"name\": \"type\", \"description\": \"Holyday or not holyday\"},\n",
+    "    {\"name\": \"day\", \"description\": \"Day number of the week. Monday is 0 and Sunday is 6\"},\n",
+    "    {\"name\": \"month\", \"description\": \"Month number of the year\"},\n",
+    "    {\"name\": \"holiday\", \"description\": \"Holiday or not holiday\"},\n",
     "]\n",
     "\n",
     "# Updating feature descriptions\n",
-    "for desc in danish_holidays_feature_descriptions: \n",
-    "    danish_holidays_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
+    "for desc in danish_calendar_feature_descriptions: \n",
+    "    danish_calendar_fg.update_feature_description(desc[\"name\"], desc[\"description\"])"
    ]
   },
   {

notebooks/2_feature_pipeline.ipynb

@@ -45,7 +45,7 @@
     "\n",
     "# Now we import the functions from the features folder\n",
     "# This is the functions we have created to generate features for electricity prices and weather measures\n",
-    "from features import electricity_prices, weather_measures \n",
+    "from features import electricity_prices, weather_measures, calendar\n",
     "\n",
     "# We go back into the notebooks folder\n",
     "%cd notebooks"
@@ -126,210 +126,235 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
-       "      <th>time</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
+       "      <th>hour</th>\n",
        "      <th>dk1_spotpricedkk_kwh</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1714521600000</td>\n",
-       "      <td>2024-05-01 00:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.36285</td>\n",
+       "      <td>1714608000000</td>\n",
+       "      <td>2024-05-02 00:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.10859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1714525200000</td>\n",
-       "      <td>2024-05-01 01:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.36315</td>\n",
+       "      <td>1714611600000</td>\n",
+       "      <td>2024-05-02 01:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.08160</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1714528800000</td>\n",
-       "      <td>2024-05-01 02:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.36330</td>\n",
+       "      <td>1714615200000</td>\n",
+       "      <td>2024-05-02 02:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.07458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1714532400000</td>\n",
-       "      <td>2024-05-01 03:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.32809</td>\n",
+       "      <td>1714618800000</td>\n",
+       "      <td>2024-05-02 03:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.05818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1714536000000</td>\n",
-       "      <td>2024-05-01 04:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.35659</td>\n",
+       "      <td>1714622400000</td>\n",
+       "      <td>2024-05-02 04:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.07928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>1714539600000</td>\n",
-       "      <td>2024-05-01 05:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.33332</td>\n",
+       "      <td>1714626000000</td>\n",
+       "      <td>2024-05-02 05:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.22920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>1714543200000</td>\n",
-       "      <td>2024-05-01 06:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.31683</td>\n",
+       "      <td>1714629600000</td>\n",
+       "      <td>2024-05-02 06:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.29699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>1714546800000</td>\n",
-       "      <td>2024-05-01 07:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.20943</td>\n",
+       "      <td>1714633200000</td>\n",
+       "      <td>2024-05-02 07:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.38605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>1714550400000</td>\n",
-       "      <td>2024-05-01 08:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.06302</td>\n",
+       "      <td>1714636800000</td>\n",
+       "      <td>2024-05-02 08:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.43729</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>1714554000000</td>\n",
-       "      <td>2024-05-01 09:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.00007</td>\n",
+       "      <td>1714640400000</td>\n",
+       "      <td>2024-05-02 09:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.23457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>1714557600000</td>\n",
-       "      <td>2024-05-01 10:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.00723</td>\n",
+       "      <td>1714644000000</td>\n",
+       "      <td>2024-05-02 10:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.03804</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>1714561200000</td>\n",
-       "      <td>2024-05-01 11:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.01805</td>\n",
+       "      <td>1714647600000</td>\n",
+       "      <td>2024-05-02 11:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-0.00060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>1714564800000</td>\n",
-       "      <td>2024-05-01 12:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.01827</td>\n",
+       "      <td>1714651200000</td>\n",
+       "      <td>2024-05-02 12:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-0.01290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>1714568400000</td>\n",
-       "      <td>2024-05-01 13:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.01559</td>\n",
+       "      <td>1714654800000</td>\n",
+       "      <td>2024-05-02 13:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>13</td>\n",
+       "      <td>-0.02014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>1714572000000</td>\n",
-       "      <td>2024-05-01 14:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.01536</td>\n",
+       "      <td>1714658400000</td>\n",
+       "      <td>2024-05-02 14:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>14</td>\n",
+       "      <td>-0.00037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>1714575600000</td>\n",
-       "      <td>2024-05-01 15:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.01827</td>\n",
+       "      <td>1714662000000</td>\n",
+       "      <td>2024-05-02 15:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>15</td>\n",
+       "      <td>-0.01037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>1714579200000</td>\n",
-       "      <td>2024-05-01 16:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.00731</td>\n",
+       "      <td>1714665600000</td>\n",
+       "      <td>2024-05-02 16:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.03013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>1714582800000</td>\n",
-       "      <td>2024-05-01 17:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>-0.00075</td>\n",
+       "      <td>1714669200000</td>\n",
+       "      <td>2024-05-02 17:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.26045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>1714586400000</td>\n",
-       "      <td>2024-05-01 18:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.11426</td>\n",
+       "      <td>1714672800000</td>\n",
+       "      <td>2024-05-02 18:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.29125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>1714590000000</td>\n",
-       "      <td>2024-05-01 19:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.37590</td>\n",
+       "      <td>1714676400000</td>\n",
+       "      <td>2024-05-02 19:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.31266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>1714593600000</td>\n",
-       "      <td>2024-05-01 20:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.37292</td>\n",
+       "      <td>1714680000000</td>\n",
+       "      <td>2024-05-02 20:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.31318</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>1714597200000</td>\n",
-       "      <td>2024-05-01 21:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.25366</td>\n",
+       "      <td>1714683600000</td>\n",
+       "      <td>2024-05-02 21:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.31266</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>1714600800000</td>\n",
-       "      <td>2024-05-01 22:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.22315</td>\n",
+       "      <td>1714687200000</td>\n",
+       "      <td>2024-05-02 22:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.28245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>1714604400000</td>\n",
-       "      <td>2024-05-01 23:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.16408</td>\n",
+       "      <td>1714690800000</td>\n",
+       "      <td>2024-05-02 23:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.25306</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        timestamp                time        date  dk1_spotpricedkk_kwh\n",
-       "0   1714521600000 2024-05-01 00:00:00  2024-05-01               0.36285\n",
-       "1   1714525200000 2024-05-01 01:00:00  2024-05-01               0.36315\n",
-       "2   1714528800000 2024-05-01 02:00:00  2024-05-01               0.36330\n",
-       "3   1714532400000 2024-05-01 03:00:00  2024-05-01               0.32809\n",
-       "4   1714536000000 2024-05-01 04:00:00  2024-05-01               0.35659\n",
-       "5   1714539600000 2024-05-01 05:00:00  2024-05-01               0.33332\n",
-       "6   1714543200000 2024-05-01 06:00:00  2024-05-01               0.31683\n",
-       "7   1714546800000 2024-05-01 07:00:00  2024-05-01               0.20943\n",
-       "8   1714550400000 2024-05-01 08:00:00  2024-05-01               0.06302\n",
-       "9   1714554000000 2024-05-01 09:00:00  2024-05-01               0.00007\n",
-       "10  1714557600000 2024-05-01 10:00:00  2024-05-01              -0.00723\n",
-       "11  1714561200000 2024-05-01 11:00:00  2024-05-01              -0.01805\n",
-       "12  1714564800000 2024-05-01 12:00:00  2024-05-01              -0.01827\n",
-       "13  1714568400000 2024-05-01 13:00:00  2024-05-01              -0.01559\n",
-       "14  1714572000000 2024-05-01 14:00:00  2024-05-01              -0.01536\n",
-       "15  1714575600000 2024-05-01 15:00:00  2024-05-01              -0.01827\n",
-       "16  1714579200000 2024-05-01 16:00:00  2024-05-01              -0.00731\n",
-       "17  1714582800000 2024-05-01 17:00:00  2024-05-01              -0.00075\n",
-       "18  1714586400000 2024-05-01 18:00:00  2024-05-01               0.11426\n",
-       "19  1714590000000 2024-05-01 19:00:00  2024-05-01               0.37590\n",
-       "20  1714593600000 2024-05-01 20:00:00  2024-05-01               0.37292\n",
-       "21  1714597200000 2024-05-01 21:00:00  2024-05-01               0.25366\n",
-       "22  1714600800000 2024-05-01 22:00:00  2024-05-01               0.22315\n",
-       "23  1714604400000 2024-05-01 23:00:00  2024-05-01               0.16408"
+       "        timestamp            datetime        date  hour  dk1_spotpricedkk_kwh\n",
+       "0   1714608000000 2024-05-02 00:00:00  2024-05-02     0               0.10859\n",
+       "1   1714611600000 2024-05-02 01:00:00  2024-05-02     1               0.08160\n",
+       "2   1714615200000 2024-05-02 02:00:00  2024-05-02     2               0.07458\n",
+       "3   1714618800000 2024-05-02 03:00:00  2024-05-02     3               0.05818\n",
+       "4   1714622400000 2024-05-02 04:00:00  2024-05-02     4               0.07928\n",
+       "5   1714626000000 2024-05-02 05:00:00  2024-05-02     5               0.22920\n",
+       "6   1714629600000 2024-05-02 06:00:00  2024-05-02     6               0.29699\n",
+       "7   1714633200000 2024-05-02 07:00:00  2024-05-02     7               0.38605\n",
+       "8   1714636800000 2024-05-02 08:00:00  2024-05-02     8               0.43729\n",
+       "9   1714640400000 2024-05-02 09:00:00  2024-05-02     9               0.23457\n",
+       "10  1714644000000 2024-05-02 10:00:00  2024-05-02    10               0.03804\n",
+       "11  1714647600000 2024-05-02 11:00:00  2024-05-02    11              -0.00060\n",
+       "12  1714651200000 2024-05-02 12:00:00  2024-05-02    12              -0.01290\n",
+       "13  1714654800000 2024-05-02 13:00:00  2024-05-02    13              -0.02014\n",
+       "14  1714658400000 2024-05-02 14:00:00  2024-05-02    14              -0.00037\n",
+       "15  1714662000000 2024-05-02 15:00:00  2024-05-02    15              -0.01037\n",
+       "16  1714665600000 2024-05-02 16:00:00  2024-05-02    16               0.03013\n",
+       "17  1714669200000 2024-05-02 17:00:00  2024-05-02    17               0.26045\n",
+       "18  1714672800000 2024-05-02 18:00:00  2024-05-02    18               0.29125\n",
+       "19  1714676400000 2024-05-02 19:00:00  2024-05-02    19               0.31266\n",
+       "20  1714680000000 2024-05-02 20:00:00  2024-05-02    20               0.31318\n",
+       "21  1714683600000 2024-05-02 21:00:00  2024-05-02    21               0.31266\n",
+       "22  1714687200000 2024-05-02 22:00:00  2024-05-02    22               0.28245\n",
+       "23  1714690800000 2024-05-02 23:00:00  2024-05-02    23               0.25306"
       ]
      },
      "execution_count": 4,
@@ -355,356 +380,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Fetching non-historical forecast of renewable energy data for area DK1\n",
-    "forecast_renewable_energy_df = electricity_prices.forecast_renewable_energy(\n",
-    "    historical=False,\n",
-    "    area=[\"DK1\"]\n",
-    ")"
+    "# # Fetching non-historical forecast of renewable energy data for area DK1\n",
+    "# forecast_renewable_energy_df = electricity_prices.forecast_renewable_energy(\n",
+    "#     historical=False,\n",
+    "#     area=[\"DK1\"]\n",
+    "# )"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "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>timestamp</th>\n",
-       "      <th>time</th>\n",
-       "      <th>date</th>\n",
-       "      <th>dk1_offshore_wind_forecastintraday_kwh</th>\n",
-       "      <th>dk1_onshore_wind_forecastintraday_kwh</th>\n",
-       "      <th>dk1_solar_forecastintraday_kwh</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1714521600000</td>\n",
-       "      <td>2024-05-01 00:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.794000</td>\n",
-       "      <td>1.710417</td>\n",
-       "      <td>0.000170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1714525200000</td>\n",
-       "      <td>2024-05-01 01:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.759667</td>\n",
-       "      <td>1.738500</td>\n",
-       "      <td>0.000169</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1714528800000</td>\n",
-       "      <td>2024-05-01 02:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.688625</td>\n",
-       "      <td>1.527958</td>\n",
-       "      <td>0.000175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1714532400000</td>\n",
-       "      <td>2024-05-01 03:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.577083</td>\n",
-       "      <td>1.347667</td>\n",
-       "      <td>0.000189</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1714536000000</td>\n",
-       "      <td>2024-05-01 04:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.605792</td>\n",
-       "      <td>1.227542</td>\n",
-       "      <td>0.000218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1714539600000</td>\n",
-       "      <td>2024-05-01 05:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.590542</td>\n",
-       "      <td>1.316083</td>\n",
-       "      <td>0.004533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1714543200000</td>\n",
-       "      <td>2024-05-01 06:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.627333</td>\n",
-       "      <td>1.346875</td>\n",
-       "      <td>0.144934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1714546800000</td>\n",
-       "      <td>2024-05-01 07:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.482625</td>\n",
-       "      <td>1.287417</td>\n",
-       "      <td>0.366274</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1714550400000</td>\n",
-       "      <td>2024-05-01 08:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.474042</td>\n",
-       "      <td>1.279458</td>\n",
-       "      <td>0.786889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1714554000000</td>\n",
-       "      <td>2024-05-01 09:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.493583</td>\n",
-       "      <td>1.367208</td>\n",
-       "      <td>1.208868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1714557600000</td>\n",
-       "      <td>2024-05-01 10:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.522458</td>\n",
-       "      <td>1.342625</td>\n",
-       "      <td>1.197411</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1714561200000</td>\n",
-       "      <td>2024-05-01 11:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.566417</td>\n",
-       "      <td>1.223500</td>\n",
-       "      <td>1.282508</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1714564800000</td>\n",
-       "      <td>2024-05-01 12:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.609958</td>\n",
-       "      <td>1.231542</td>\n",
-       "      <td>1.362772</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1714568400000</td>\n",
-       "      <td>2024-05-01 13:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.637292</td>\n",
-       "      <td>1.213167</td>\n",
-       "      <td>1.375432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1714572000000</td>\n",
-       "      <td>2024-05-01 14:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.662375</td>\n",
-       "      <td>1.187667</td>\n",
-       "      <td>1.347302</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1714575600000</td>\n",
-       "      <td>2024-05-01 15:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.704667</td>\n",
-       "      <td>1.169292</td>\n",
-       "      <td>1.269182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1714579200000</td>\n",
-       "      <td>2024-05-01 16:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.728542</td>\n",
-       "      <td>1.286833</td>\n",
-       "      <td>1.147561</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1714582800000</td>\n",
-       "      <td>2024-05-01 17:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.744292</td>\n",
-       "      <td>1.393458</td>\n",
-       "      <td>1.062930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1714586400000</td>\n",
-       "      <td>2024-05-01 18:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.771792</td>\n",
-       "      <td>1.502625</td>\n",
-       "      <td>0.664276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1714590000000</td>\n",
-       "      <td>2024-05-01 19:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.816250</td>\n",
-       "      <td>1.382208</td>\n",
-       "      <td>0.272911</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1714593600000</td>\n",
-       "      <td>2024-05-01 20:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.848500</td>\n",
-       "      <td>1.388583</td>\n",
-       "      <td>0.046087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1714597200000</td>\n",
-       "      <td>2024-05-01 21:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.886042</td>\n",
-       "      <td>1.554792</td>\n",
-       "      <td>0.001339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1714600800000</td>\n",
-       "      <td>2024-05-01 22:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.919417</td>\n",
-       "      <td>1.698875</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1714604400000</td>\n",
-       "      <td>2024-05-01 23:00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.934708</td>\n",
-       "      <td>1.739375</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        timestamp                time        date  \\\n",
-       "0   1714521600000 2024-05-01 00:00:00  2024-05-01   \n",
-       "1   1714525200000 2024-05-01 01:00:00  2024-05-01   \n",
-       "2   1714528800000 2024-05-01 02:00:00  2024-05-01   \n",
-       "3   1714532400000 2024-05-01 03:00:00  2024-05-01   \n",
-       "4   1714536000000 2024-05-01 04:00:00  2024-05-01   \n",
-       "5   1714539600000 2024-05-01 05:00:00  2024-05-01   \n",
-       "6   1714543200000 2024-05-01 06:00:00  2024-05-01   \n",
-       "7   1714546800000 2024-05-01 07:00:00  2024-05-01   \n",
-       "8   1714550400000 2024-05-01 08:00:00  2024-05-01   \n",
-       "9   1714554000000 2024-05-01 09:00:00  2024-05-01   \n",
-       "10  1714557600000 2024-05-01 10:00:00  2024-05-01   \n",
-       "11  1714561200000 2024-05-01 11:00:00  2024-05-01   \n",
-       "12  1714564800000 2024-05-01 12:00:00  2024-05-01   \n",
-       "13  1714568400000 2024-05-01 13:00:00  2024-05-01   \n",
-       "14  1714572000000 2024-05-01 14:00:00  2024-05-01   \n",
-       "15  1714575600000 2024-05-01 15:00:00  2024-05-01   \n",
-       "16  1714579200000 2024-05-01 16:00:00  2024-05-01   \n",
-       "17  1714582800000 2024-05-01 17:00:00  2024-05-01   \n",
-       "18  1714586400000 2024-05-01 18:00:00  2024-05-01   \n",
-       "19  1714590000000 2024-05-01 19:00:00  2024-05-01   \n",
-       "20  1714593600000 2024-05-01 20:00:00  2024-05-01   \n",
-       "21  1714597200000 2024-05-01 21:00:00  2024-05-01   \n",
-       "22  1714600800000 2024-05-01 22:00:00  2024-05-01   \n",
-       "23  1714604400000 2024-05-01 23:00:00  2024-05-01   \n",
-       "\n",
-       "    dk1_offshore_wind_forecastintraday_kwh  \\\n",
-       "0                                 0.794000   \n",
-       "1                                 0.759667   \n",
-       "2                                 0.688625   \n",
-       "3                                 0.577083   \n",
-       "4                                 0.605792   \n",
-       "5                                 0.590542   \n",
-       "6                                 0.627333   \n",
-       "7                                 0.482625   \n",
-       "8                                 0.474042   \n",
-       "9                                 0.493583   \n",
-       "10                                0.522458   \n",
-       "11                                0.566417   \n",
-       "12                                0.609958   \n",
-       "13                                0.637292   \n",
-       "14                                0.662375   \n",
-       "15                                0.704667   \n",
-       "16                                0.728542   \n",
-       "17                                0.744292   \n",
-       "18                                0.771792   \n",
-       "19                                0.816250   \n",
-       "20                                0.848500   \n",
-       "21                                0.886042   \n",
-       "22                                0.919417   \n",
-       "23                                0.934708   \n",
-       "\n",
-       "    dk1_onshore_wind_forecastintraday_kwh  dk1_solar_forecastintraday_kwh  \n",
-       "0                                1.710417                        0.000170  \n",
-       "1                                1.738500                        0.000169  \n",
-       "2                                1.527958                        0.000175  \n",
-       "3                                1.347667                        0.000189  \n",
-       "4                                1.227542                        0.000218  \n",
-       "5                                1.316083                        0.004533  \n",
-       "6                                1.346875                        0.144934  \n",
-       "7                                1.287417                        0.366274  \n",
-       "8                                1.279458                        0.786889  \n",
-       "9                                1.367208                        1.208868  \n",
-       "10                               1.342625                        1.197411  \n",
-       "11                               1.223500                        1.282508  \n",
-       "12                               1.231542                        1.362772  \n",
-       "13                               1.213167                        1.375432  \n",
-       "14                               1.187667                        1.347302  \n",
-       "15                               1.169292                        1.269182  \n",
-       "16                               1.286833                        1.147561  \n",
-       "17                               1.393458                        1.062930  \n",
-       "18                               1.502625                        0.664276  \n",
-       "19                               1.382208                        0.272911  \n",
-       "20                               1.388583                        0.046087  \n",
-       "21                               1.554792                        0.001339  \n",
-       "22                               1.698875                        0.000000  \n",
-       "23                               1.739375                        0.000000  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Display the forecast_renewable_energy dataframe\n",
-    "forecast_renewable_energy_df"
+    "# # Display the forecast_renewable_energy dataframe\n",
+    "# forecast_renewable_energy_df"
    ]
   },
   {
@@ -789,8 +479,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
-       "      <th>time</th>\n",
+       "      <th>hour</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>relative_humidity_2m</th>\n",
        "      <th>precipitation</th>\n",
@@ -805,78 +496,83 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1714521600000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 00:00:00</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>70</td>\n",
+       "      <td>1714608000000</td>\n",
+       "      <td>2024-05-02 00:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>66.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>46</td>\n",
-       "      <td>20.9</td>\n",
-       "      <td>36.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>41.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1714525200000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 01:00:00</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>73</td>\n",
+       "      <td>1714611600000</td>\n",
+       "      <td>2024-05-02 01:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>71.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>35.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>37.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1714528800000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 02:00:00</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>75</td>\n",
+       "      <td>1714615200000</td>\n",
+       "      <td>2024-05-02 02:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>73.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>54</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>31.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>36.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1714532400000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 03:00:00</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>76</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1714618800000</td>\n",
+       "      <td>2024-05-02 03:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
        "      <td>3</td>\n",
-       "      <td>97</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>33.1</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>22.3</td>\n",
+       "      <td>39.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1714536000000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 04:00:00</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>78</td>\n",
+       "      <td>1714622400000</td>\n",
+       "      <td>2024-05-02 04:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>73.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>96</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>33.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>38.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -892,128 +588,134 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>115</th>\n",
-       "      <td>1714935600000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 19:00:00</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>79</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1715022000000</td>\n",
+       "      <td>2024-05-06 19:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>19</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>83</td>\n",
-       "      <td>11.3</td>\n",
-       "      <td>25.9</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>32.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>116</th>\n",
-       "      <td>1714939200000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 20:00:00</td>\n",
-       "      <td>11.7</td>\n",
-       "      <td>83</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>91</td>\n",
+       "      <td>1715025600000</td>\n",
+       "      <td>2024-05-06 20:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>20</td>\n",
        "      <td>10.1</td>\n",
-       "      <td>23.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>37.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117</th>\n",
-       "      <td>1714942800000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 21:00:00</td>\n",
-       "      <td>11.1</td>\n",
-       "      <td>86</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>98</td>\n",
+       "      <td>1715029200000</td>\n",
+       "      <td>2024-05-06 21:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>21</td>\n",
        "      <td>9.5</td>\n",
-       "      <td>20.5</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>42.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
-       "      <td>1714946400000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 22:00:00</td>\n",
-       "      <td>10.9</td>\n",
-       "      <td>87</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1715032800000</td>\n",
+       "      <td>2024-05-06 22:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>98</td>\n",
-       "      <td>10.2</td>\n",
-       "      <td>22.3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>41.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>119</th>\n",
-       "      <td>1714950000000</td>\n",
-       "      <td>2024-05-05</td>\n",
-       "      <td>2024-05-05 23:00:00</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>88</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1715036400000</td>\n",
+       "      <td>2024-05-06 23:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>23</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>97</td>\n",
-       "      <td>11.9</td>\n",
-       "      <td>24.1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>40.3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>120 rows × 12 columns</p>\n",
+       "<p>120 rows × 13 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         timestamp        date                time  temperature_2m  \\\n",
-       "0    1714521600000  2024-05-01 2024-05-01 00:00:00            13.4   \n",
-       "1    1714525200000  2024-05-01 2024-05-01 01:00:00            12.6   \n",
-       "2    1714528800000  2024-05-01 2024-05-01 02:00:00            12.0   \n",
-       "3    1714532400000  2024-05-01 2024-05-01 03:00:00            11.5   \n",
-       "4    1714536000000  2024-05-01 2024-05-01 04:00:00            11.2   \n",
-       "..             ...         ...                 ...             ...   \n",
-       "115  1714935600000  2024-05-05 2024-05-05 19:00:00            12.6   \n",
-       "116  1714939200000  2024-05-05 2024-05-05 20:00:00            11.7   \n",
-       "117  1714942800000  2024-05-05 2024-05-05 21:00:00            11.1   \n",
-       "118  1714946400000  2024-05-05 2024-05-05 22:00:00            10.9   \n",
-       "119  1714950000000  2024-05-05 2024-05-05 23:00:00            11.0   \n",
+       "         timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "0    1714608000000 2024-05-02 00:00:00  2024-05-02     0            14.9   \n",
+       "1    1714611600000 2024-05-02 01:00:00  2024-05-02     1            14.2   \n",
+       "2    1714615200000 2024-05-02 02:00:00  2024-05-02     2            13.4   \n",
+       "3    1714618800000 2024-05-02 03:00:00  2024-05-02     3            13.2   \n",
+       "4    1714622400000 2024-05-02 04:00:00  2024-05-02     4            12.7   \n",
+       "..             ...                 ...         ...   ...             ...   \n",
+       "115  1715022000000 2024-05-06 19:00:00  2024-05-06    19            10.7   \n",
+       "116  1715025600000 2024-05-06 20:00:00  2024-05-06    20            10.1   \n",
+       "117  1715029200000 2024-05-06 21:00:00  2024-05-06    21             9.5   \n",
+       "118  1715032800000 2024-05-06 22:00:00  2024-05-06    22             9.3   \n",
+       "119  1715036400000 2024-05-06 23:00:00  2024-05-06    23             9.1   \n",
        "\n",
        "     relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
-       "0                      70            0.0   0.0       0.0             1   \n",
-       "1                      73            0.0   0.0       0.0             0   \n",
-       "2                      75            0.0   0.0       0.0             2   \n",
-       "3                      76            0.0   0.0       0.0             3   \n",
-       "4                      78            0.0   0.0       0.0             3   \n",
+       "0                    66.0            0.0   0.0       0.0           0.0   \n",
+       "1                    71.0            0.0   0.0       0.0           0.0   \n",
+       "2                    73.0            0.0   0.0       0.0           2.0   \n",
+       "3                    72.0            0.1   0.1       0.0          51.0   \n",
+       "4                    73.0            0.0   0.0       0.0           2.0   \n",
        "..                    ...            ...   ...       ...           ...   \n",
-       "115                    79            0.0   0.0       0.0             3   \n",
-       "116                    83            0.0   0.0       0.0             3   \n",
-       "117                    86            0.0   0.0       0.0             3   \n",
-       "118                    87            0.0   0.0       0.0             3   \n",
-       "119                    88            0.0   0.0       0.0             3   \n",
+       "115                  91.0            1.4   1.4       0.0          61.0   \n",
+       "116                  90.0            1.4   1.4       0.0          61.0   \n",
+       "117                  88.0            1.4   1.4       0.0          61.0   \n",
+       "118                  86.0            0.6   0.6       0.0           3.0   \n",
+       "119                  84.0            0.6   0.6       0.0           3.0   \n",
        "\n",
        "     cloud_cover  wind_speed_10m  wind_gusts_10m  \n",
-       "0             46            20.9            36.4  \n",
-       "1             18            18.0            35.6  \n",
-       "2             54            18.0            31.0  \n",
-       "3             97            19.4            33.1  \n",
-       "4             96            18.0            33.5  \n",
+       "0           13.0            21.6            41.4  \n",
+       "1            4.0            20.5            37.1  \n",
+       "2           70.0            21.2            36.7  \n",
+       "3           51.0            22.3            39.2  \n",
+       "4           78.0            21.6            38.9  \n",
        "..           ...             ...             ...  \n",
-       "115           83            11.3            25.9  \n",
-       "116           91            10.1            23.0  \n",
-       "117           98             9.5            20.5  \n",
-       "118           98            10.2            22.3  \n",
-       "119           97            11.9            24.1  \n",
+       "115        100.0            16.6            32.0  \n",
+       "116        100.0            19.5            37.1  \n",
+       "117        100.0            21.6            42.1  \n",
+       "118        100.0            22.0            41.0  \n",
+       "119        100.0            21.3            40.3  \n",
        "\n",
-       "[120 rows x 12 columns]"
+       "[120 rows x 13 columns]"
       ]
      },
      "execution_count": 10,
@@ -1030,20 +732,6 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Converting to float type to align with Hopworks Feature Group as it converts the data to float automatically\n",
-    "\n",
-    "# Converting 'relative_humidity_2m', 'weather_code' and 'cloud_cover' columns to float type\n",
-    "weather_forecast_df['relative_humidity_2m'] = weather_forecast_df['relative_humidity_2m'].astype(float)\n",
-    "weather_forecast_df['weather_code'] = weather_forecast_df['weather_code'].astype(float)\n",
-    "weather_forecast_df['cloud_cover'] = weather_forecast_df['cloud_cover'].astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1067,8 +755,9 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>datetime</th>\n",
        "      <th>date</th>\n",
-       "      <th>time</th>\n",
+       "      <th>hour</th>\n",
        "      <th>temperature_2m</th>\n",
        "      <th>relative_humidity_2m</th>\n",
        "      <th>precipitation</th>\n",
@@ -1083,107 +772,112 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1714521600000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 00:00:00</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>70.0</td>\n",
+       "      <td>1714608000000</td>\n",
+       "      <td>2024-05-02 00:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>66.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>20.9</td>\n",
-       "      <td>36.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>41.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1714525200000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 01:00:00</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>73.0</td>\n",
+       "      <td>1714611600000</td>\n",
+       "      <td>2024-05-02 01:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>71.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>35.6</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>37.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1714528800000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 02:00:00</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>75.0</td>\n",
+       "      <td>1714615200000</td>\n",
+       "      <td>2024-05-02 02:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>73.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>31.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>36.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1714532400000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 03:00:00</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1714618800000</td>\n",
+       "      <td>2024-05-02 03:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>33.1</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>22.3</td>\n",
+       "      <td>39.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1714536000000</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>2024-05-01 04:00:00</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>78.0</td>\n",
+       "      <td>1714622400000</td>\n",
+       "      <td>2024-05-02 04:00:00</td>\n",
+       "      <td>2024-05-02</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>73.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>33.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>38.9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       timestamp        date                time  temperature_2m  \\\n",
-       "0  1714521600000  2024-05-01 2024-05-01 00:00:00            13.4   \n",
-       "1  1714525200000  2024-05-01 2024-05-01 01:00:00            12.6   \n",
-       "2  1714528800000  2024-05-01 2024-05-01 02:00:00            12.0   \n",
-       "3  1714532400000  2024-05-01 2024-05-01 03:00:00            11.5   \n",
-       "4  1714536000000  2024-05-01 2024-05-01 04:00:00            11.2   \n",
+       "       timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "0  1714608000000 2024-05-02 00:00:00  2024-05-02     0            14.9   \n",
+       "1  1714611600000 2024-05-02 01:00:00  2024-05-02     1            14.2   \n",
+       "2  1714615200000 2024-05-02 02:00:00  2024-05-02     2            13.4   \n",
+       "3  1714618800000 2024-05-02 03:00:00  2024-05-02     3            13.2   \n",
+       "4  1714622400000 2024-05-02 04:00:00  2024-05-02     4            12.7   \n",
        "\n",
        "   relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
-       "0                  70.0            0.0   0.0       0.0           1.0   \n",
-       "1                  73.0            0.0   0.0       0.0           0.0   \n",
-       "2                  75.0            0.0   0.0       0.0           2.0   \n",
-       "3                  76.0            0.0   0.0       0.0           3.0   \n",
-       "4                  78.0            0.0   0.0       0.0           3.0   \n",
+       "0                  66.0            0.0   0.0       0.0           0.0   \n",
+       "1                  71.0            0.0   0.0       0.0           0.0   \n",
+       "2                  73.0            0.0   0.0       0.0           2.0   \n",
+       "3                  72.0            0.1   0.1       0.0          51.0   \n",
+       "4                  73.0            0.0   0.0       0.0           2.0   \n",
        "\n",
        "   cloud_cover  wind_speed_10m  wind_gusts_10m  \n",
-       "0         46.0            20.9            36.4  \n",
-       "1         18.0            18.0            35.6  \n",
-       "2         54.0            18.0            31.0  \n",
-       "3         97.0            19.4            33.1  \n",
-       "4         96.0            18.0            33.5  "
+       "0         13.0            21.6            41.4  \n",
+       "1          4.0            20.5            37.1  \n",
+       "2         70.0            21.2            36.7  \n",
+       "3         51.0            22.3            39.2  \n",
+       "4         78.0            21.6            38.9  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1193,6 +887,164 @@
     "weather_forecast_df.head(5)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>timestamp</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>date</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>temperature_2m</th>\n",
+       "      <th>relative_humidity_2m</th>\n",
+       "      <th>precipitation</th>\n",
+       "      <th>rain</th>\n",
+       "      <th>snowfall</th>\n",
+       "      <th>weather_code</th>\n",
+       "      <th>cloud_cover</th>\n",
+       "      <th>wind_speed_10m</th>\n",
+       "      <th>wind_gusts_10m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>1715022000000</td>\n",
+       "      <td>2024-05-06 19:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>19</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>1715025600000</td>\n",
+       "      <td>2024-05-06 20:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>20</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>37.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>1715029200000</td>\n",
+       "      <td>2024-05-06 21:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>21</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>42.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>1715032800000</td>\n",
+       "      <td>2024-05-06 22:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>1715036400000</td>\n",
+       "      <td>2024-05-06 23:00:00</td>\n",
+       "      <td>2024-05-06</td>\n",
+       "      <td>23</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>40.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         timestamp            datetime        date  hour  temperature_2m  \\\n",
+       "115  1715022000000 2024-05-06 19:00:00  2024-05-06    19            10.7   \n",
+       "116  1715025600000 2024-05-06 20:00:00  2024-05-06    20            10.1   \n",
+       "117  1715029200000 2024-05-06 21:00:00  2024-05-06    21             9.5   \n",
+       "118  1715032800000 2024-05-06 22:00:00  2024-05-06    22             9.3   \n",
+       "119  1715036400000 2024-05-06 23:00:00  2024-05-06    23             9.1   \n",
+       "\n",
+       "     relative_humidity_2m  precipitation  rain  snowfall  weather_code  \\\n",
+       "115                  91.0            1.4   1.4       0.0          61.0   \n",
+       "116                  90.0            1.4   1.4       0.0          61.0   \n",
+       "117                  88.0            1.4   1.4       0.0          61.0   \n",
+       "118                  86.0            0.6   0.6       0.0           3.0   \n",
+       "119                  84.0            0.6   0.6       0.0           3.0   \n",
+       "\n",
+       "     cloud_cover  wind_speed_10m  wind_gusts_10m  \n",
+       "115        100.0            16.6            32.0  \n",
+       "116        100.0            19.5            37.1  \n",
+       "117        100.0            21.6            42.1  \n",
+       "118        100.0            22.0            41.0  \n",
+       "119        100.0            21.3            40.3  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather_forecast_df.tail(5)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1241,10 +1093,10 @@
     "    version=1,\n",
     ")\n",
     "\n",
-    "forecast_renewable_energy_fg = fs.get_feature_group(\n",
-    "    name=\"forecast_renewable_energy\",\n",
-    "    version=1,\n",
-    ")\n",
+    "# forecast_renewable_energy_fg = fs.get_feature_group(\n",
+    "#     name=\"forecast_renewable_energy\",\n",
+    "#     version=1,\n",
+    "# )\n",
     "\n",
     "weather_fg = fs.get_feature_group(\n",
     "    name=\"weather_measurements\",\n",
@@ -1268,7 +1120,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f5aa8974bf724e60bf0f29c7a58e83fa",
+       "model_id": "0620fe280bda4631b98959a633d0c782",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1291,7 +1143,7 @@
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x15f0bcd90>, None)"
+       "(<hsfs.core.job.Job at 0x1782b1810>, None)"
       ]
      },
      "execution_count": 15,
@@ -1307,58 +1159,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2c640cab44b4d66add7c785eb9fd16b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Uploading Dataframe: 0.00% |          | Rows 0/24 | Elapsed Time: 00:00 | Remaining Time: ?"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Launching job: forecast_renewable_energy_1_offline_fg_materialization\n",
-      "Job started successfully, you can follow the progress at \n",
-      "https://c.app.hopsworks.ai/p/554133/jobs/named/forecast_renewable_energy_1_offline_fg_materialization/executions\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(<hsfs.core.job.Job at 0x15f0fa910>, None)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Inserting the forecast_renewable_energy_df into the feature group named forecast_renewable_energy_fg\n",
-    "forecast_renewable_energy_fg.insert(forecast_renewable_energy_df, \n",
-    "                                    write_options={\"wait_for_job\" : False})"
+    "# # Inserting the forecast_renewable_energy_df into the feature group named forecast_renewable_energy_fg\n",
+    "# forecast_renewable_energy_fg.insert(forecast_renewable_energy_df, \n",
+    "#                                     write_options={\"wait_for_job\" : False})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "61148203d2ed42ce9dcb93d9b33b852b",
+       "model_id": "e39a33b92b7e48fab45022e590e78157",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1381,10 +1199,10 @@
     {
      "data": {
       "text/plain": [
-       "(<hsfs.core.job.Job at 0x30d030190>, None)"
+       "(<hsfs.core.job.Job at 0x1782e7ed0>, None)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }

notebooks/4_batch_inference copy.ipynb

@@ -0,0 +1,448 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# <span style=\"font-width:bold; font-size: 3rem; color:#2656a3;\">**Data Engineering and Machine Learning Operations in Business** </span> <span style=\"font-width:bold; font-size: 3rem; color:#333;\">- Part 04: Batch Inference</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#2656a3'> 🗒️ This notebook is divided into the following sections:\n",
+    "\n",
+    "1. Load batch data.\n",
+    "2. Predict using model from Model Registry."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#2656a3'> ⚙️ Import of libraries and packages\n",
+    "\n",
+    "First, we'll install the Python packages required for this notebook. We'll use the --quiet command after specifying the names of the libraries to ensure a silent installation process. Then, we'll proceed to import all the necessary libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing the packages for the needed libraries for the Jupyter notebook\n",
+    "import joblib\n",
+    "import inspect \n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import FuncFormatter\n",
+    "import os\n",
+    "\n",
+    "#%config InlineBackend.figure_format='retina'\n",
+    "#%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#2656a3;\"> 📡 Connecting to Hopsworks Feature Store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing the hopsworks module\n",
+    "import hopsworks\n",
+    "\n",
+    "# Logging in to the Hopsworks project\n",
+    "project = hopsworks.login()\n",
+    "\n",
+    "# Getting the feature store from the project\n",
+    "fs = project.get_feature_store() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <span style='color:#2656a3'> ⚙️ Feature View Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the 'electricity_feature_view' feature view\n",
+    "feature_view = fs.get_feature_view(\n",
+    "    name='electricity_feature_view',\n",
+    "    version=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <span style='color:#2656a3'> 🗄 Model Registry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the model registry\n",
+    "mr = project.get_model_registry()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#2656a3'> 📮 Retrieving model from Model Registry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieving the model from the Model Registry\n",
+    "retrieved_model = mr.get_model(\n",
+    "    name=\"electricity_price_prediction_model\", \n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "# Downloading the saved model to a local directory\n",
+    "saved_model_dir = retrieved_model.download()\n",
+    "\n",
+    "# Loading the saved XGB model\n",
+    "retrieved_xgboost_model = joblib.load(saved_model_dir + \"/dk_electricity_model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display the retrieved XGBoost regressor model\n",
+    "retrieved_xgboost_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style='color:#2656a3'> ✨ Load Batch Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "# Calculating the start date as 5 days ago from the current date\n",
+    "start_date = datetime.datetime.now() - datetime.timedelta(days=5)\n",
+    "\n",
+    "# Converting the start date to a timestamp in milliseconds\n",
+    "start_time = int(start_date.timestamp()) * 1000\n",
+    "\n",
+    "# Displaying the start date in timestamp format\n",
+    "start_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initializing batch scoring\n",
+    "feature_view.init_batch_scoring(1)\n",
+    "\n",
+    "# Retrieving batch data from the feature view starting from the specified start time\n",
+    "batch_data = feature_view.get_batch_data(\n",
+    "    start_time=start_time,\n",
+    ")\n",
+    "\n",
+    "batch_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we go one back in our directory to access the folder with our functions\n",
+    "%cd ..\n",
+    "\n",
+    "# Now we import the functions from the features folder\n",
+    "# This is the functions we have created to generate features for electricity prices and weather measures\n",
+    "from features import electricity_prices, weather_measures \n",
+    "\n",
+    "# We go back into the notebooks folder\n",
+    "%cd notebooks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fetching weather forecast measures for the next 5 days\n",
+    "weather_forecast_df = weather_measures.forecast_weather_measures(\n",
+    "    forecast_length=5\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read csv file with calender\n",
+    "calender_df = pd.read_csv('https://raw.githubusercontent.com/Camillahannesbo/MLOPs-Assignment-/main/data/calendar_incl_holiday.csv', delimiter=';', usecols=['date', 'type'])\n",
+    "\n",
+    "calender_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "# Formatting the date column to 'YYYY-MM-DD' dateformat\n",
+    "calender_df[\"date\"] = calender_df[\"date\"].map(lambda x: datetime.strptime(x, '%d/%m/%Y').strftime(\"%Y-%m-%d\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add features to the calender dataframe\n",
+    "calender_df['date_'] = pd.to_datetime(calender_df['date'])\n",
+    "calender_df['day'] = calender_df['date_'].dt.dayofweek\n",
+    "calender_df['month'] = calender_df['date_'].dt.month\n",
+    "calender_df['holiday'] = np.where(calender_df['type'] == 'Not a Workday', 1, 0)\n",
+    "\n",
+    "# Drop the columns 'type' and 'date_' to finalize the calender dataframe\n",
+    "calender_df = calender_df.drop(['type','date_'], axis=1)\n",
+    "\n",
+    "merged_df = pd.merge(weather_forecast_df, calender_df, how='inner', left_on='date', right_on='date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display the first 5 rows of the batch data\n",
+    "batch_data = merged_df\n",
+    "\n",
+    "batch_data.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <span style=\"color:#ff5f27;\">🤖 Making the predictions</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# # Create a LabelEncoder object\n",
+    "# label_encoder = LabelEncoder()\n",
+    "\n",
+    "# # Fit the encoder to the data in the 'city_name' column\n",
+    "# label_encoder.fit(batch_data[['type']])\n",
+    "\n",
+    "# # Transform the 'city_name' column data using the fitted encoder\n",
+    "# encoded = label_encoder.transform(batch_data[['type']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Convert the output of the label encoding to a dense array and concatenate with the original data\n",
+    "# X_batch = pd.concat([batch_data, pd.DataFrame(encoded)], axis=1)\n",
+    "\n",
+    "X_batch = batch_data\n",
+    "\n",
+    "# Drop columns 'date', 'city_name', 'unix_time' from the DataFrame 'X'\n",
+    "X_batch = X_batch.drop(columns=['date', 'time', 'timestamp'])\n",
+    "\n",
+    "# # Rename the newly added column with label-encoded city names to 'city_name_encoded'\n",
+    "# X_batch = X_batch.rename(columns={0: \"type_encoded\"})\n",
+    "\n",
+    "# Displaying the first 5 rows of the modified DataFrame\n",
+    "X_batch.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract the target variable 'dk1_spotpricedkk_kwh' from the batch data\n",
+    "y_batch = X_batch.pop('dk1_spotpricedkk_kwh')\n",
+    "\n",
+    "# Displaying the first 5 rows of the modified DataFrame\n",
+    "y_batch.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on the batch data using the retrieved XGBoost regressor model\n",
+    "predictions = retrieved_xgboost_model.predict(X_batch)\n",
+    "\n",
+    "# Display the first 5 predictions\n",
+    "predictions[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = batch_data[\"time\"]\n",
+    "y_pred = retrieved_xgboost_model.predict(X_batch)\n",
+    "\n",
+    "data = {\n",
+    "    'prediction': [y_pred],\n",
+    "    'time': [label],\n",
+    "}\n",
+    "\n",
+    "monitor_df = pd.DataFrame(data)\n",
+    "monitor_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = batch_data[\"time\"]\n",
+    "y_pred = retrieved_xgboost_model.predict(X_batch)\n",
+    "\n",
+    "data = {\n",
+    "    'prediction': y_pred,\n",
+    "    'time': label,\n",
+    "}\n",
+    "\n",
+    "monitor_df = pd.DataFrame(data)\n",
+    "monitor_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## <span style=\"color:#ff5f27;\">👾 Next is creating our Streamlit App?</span>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bds-mlops",
+   "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": "3.11.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/4_batch_inference.ipynb

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,20 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected. Call `.close()` to terminate connection gracefully.\n",
-      "\n",
-      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/554133\n",
-      "Connected. Call `.close()` to terminate connection gracefully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Importing the hopsworks module\n",
     "import hopsworks\n",
@@ -87,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,17 +96,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Connected. Call `.close()` to terminate connection gracefully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Retrieve the model registry\n",
     "mr = project.get_model_registry()"
@@ -132,17 +113,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading model artifact (0 dirs, 3 files)... DONE\r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Retrieving the model from the Model Registry\n",
     "retrieved_model = mr.get_model(\n",
@@ -159,457 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-       "             colsample_bylevel=None, colsample_bynode=None,\n",
-       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "             gamma=None, grow_policy=None, importance_type=None,\n",
-       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-       "             num_parallel_tree=None, random_state=None, ...)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-       "             colsample_bylevel=None, colsample_bynode=None,\n",
-       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "             gamma=None, grow_policy=None, importance_type=None,\n",
-       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
-      ],
-      "text/plain": [
-       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-       "             colsample_bylevel=None, colsample_bynode=None,\n",
-       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-       "             gamma=None, grow_policy=None, importance_type=None,\n",
-       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-       "             num_parallel_tree=None, random_state=None, ...)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Display the retrieved XGBoost regressor model\n",
     "retrieved_xgboost_model"
@@ -624,20 +149,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1714138328000"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import datetime\n",
     "\n",
@@ -653,17 +167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finished: Reading data from Hopsworks, using ArrowFlight (2.85s) \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initializing batch scoring\n",
     "feature_view.init_batch_scoring(training_dataset_version=1)\n",
@@ -676,199 +182,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "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>timestamp</th>\n",
-       "      <th>time</th>\n",
-       "      <th>date</th>\n",
-       "      <th>dk1_spotpricedkk_kwh</th>\n",
-       "      <th>dk1_offshore_wind_forecastintraday_kwh</th>\n",
-       "      <th>dk1_onshore_wind_forecastintraday_kwh</th>\n",
-       "      <th>dk1_solar_forecastintraday_kwh</th>\n",
-       "      <th>temperature_2m</th>\n",
-       "      <th>relative_humidity_2m</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>rain</th>\n",
-       "      <th>snowfall</th>\n",
-       "      <th>weather_code</th>\n",
-       "      <th>cloud_cover</th>\n",
-       "      <th>wind_speed_10m</th>\n",
-       "      <th>wind_gusts_10m</th>\n",
-       "      <th>type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1714287600000</td>\n",
-       "      <td>2024-04-28 07:00:00+00:00</td>\n",
-       "      <td>2024-04-28</td>\n",
-       "      <td>0.00186</td>\n",
-       "      <td>0.959167</td>\n",
-       "      <td>0.771750</td>\n",
-       "      <td>0.184346</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>12.8</td>\n",
-       "      <td>22.3</td>\n",
-       "      <td>Not a Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1714392000000</td>\n",
-       "      <td>2024-04-29 12:00:00+00:00</td>\n",
-       "      <td>2024-04-29</td>\n",
-       "      <td>0.26984</td>\n",
-       "      <td>0.649292</td>\n",
-       "      <td>1.123000</td>\n",
-       "      <td>1.615064</td>\n",
-       "      <td>14.1</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>17.8</td>\n",
-       "      <td>39.2</td>\n",
-       "      <td>Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1714536000000</td>\n",
-       "      <td>2024-05-01 04:00:00+00:00</td>\n",
-       "      <td>2024-05-01</td>\n",
-       "      <td>0.35659</td>\n",
-       "      <td>0.605792</td>\n",
-       "      <td>1.227542</td>\n",
-       "      <td>0.000218</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>33.5</td>\n",
-       "      <td>Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1714172400000</td>\n",
-       "      <td>2024-04-26 23:00:00+00:00</td>\n",
-       "      <td>2024-04-26</td>\n",
-       "      <td>0.65829</td>\n",
-       "      <td>0.178042</td>\n",
-       "      <td>0.244625</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>8.6</td>\n",
-       "      <td>Workday</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1714258800000</td>\n",
-       "      <td>2024-04-27 23:00:00+00:00</td>\n",
-       "      <td>2024-04-27</td>\n",
-       "      <td>0.48644</td>\n",
-       "      <td>0.657625</td>\n",
-       "      <td>0.999583</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>29.9</td>\n",
-       "      <td>Not a Workday</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       timestamp                      time        date  dk1_spotpricedkk_kwh  \\\n",
-       "0  1714287600000 2024-04-28 07:00:00+00:00  2024-04-28               0.00186   \n",
-       "1  1714392000000 2024-04-29 12:00:00+00:00  2024-04-29               0.26984   \n",
-       "2  1714536000000 2024-05-01 04:00:00+00:00  2024-05-01               0.35659   \n",
-       "3  1714172400000 2024-04-26 23:00:00+00:00  2024-04-26               0.65829   \n",
-       "4  1714258800000 2024-04-27 23:00:00+00:00  2024-04-27               0.48644   \n",
-       "\n",
-       "   dk1_offshore_wind_forecastintraday_kwh  \\\n",
-       "0                                0.959167   \n",
-       "1                                0.649292   \n",
-       "2                                0.605792   \n",
-       "3                                0.178042   \n",
-       "4                                0.657625   \n",
-       "\n",
-       "   dk1_onshore_wind_forecastintraday_kwh  dk1_solar_forecastintraday_kwh  \\\n",
-       "0                               0.771750                        0.184346   \n",
-       "1                               1.123000                        1.615064   \n",
-       "2                               1.227542                        0.000218   \n",
-       "3                               0.244625                        0.000000   \n",
-       "4                               0.999583                        0.000000   \n",
-       "\n",
-       "   temperature_2m  relative_humidity_2m  precipitation  rain  snowfall  \\\n",
-       "0             8.5                  91.0            0.0   0.0       0.0   \n",
-       "1            14.1                  48.0            0.0   0.0       0.0   \n",
-       "2            11.2                  78.0            0.0   0.0       0.0   \n",
-       "3             3.9                  96.0            0.0   0.0       0.0   \n",
-       "4             6.9                  93.0            0.0   0.0       0.0   \n",
-       "\n",
-       "   weather_code  cloud_cover  wind_speed_10m  wind_gusts_10m           type  \n",
-       "0           2.0         62.0            12.8            22.3  Not a Workday  \n",
-       "1           1.0         32.0            17.8            39.2        Workday  \n",
-       "2           3.0         96.0            18.0            33.5        Workday  \n",
-       "3           1.0         36.0             3.9             8.6        Workday  \n",
-       "4           1.0         40.0            16.2            29.9  Not a Workday  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Display the first 5 rows of the batch data\n",
-    "batch_data.head(5)"
+    "batch_data"
    ]
   },
   {
@@ -880,215 +199,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from sklearn.preprocessing import LabelEncoder\n",
+    "# from sklearn.preprocessing import LabelEncoder\n",
     "\n",
-    "# Create a LabelEncoder object\n",
-    "label_encoder = LabelEncoder()\n",
+    "# # Create a LabelEncoder object\n",
+    "# label_encoder = LabelEncoder()\n",
     "\n",
-    "# Fit the encoder to the data in the 'city_name' column\n",
-    "label_encoder.fit(batch_data[['type']])\n",
+    "# # Fit the encoder to the data in the 'city_name' column\n",
+    "# label_encoder.fit(batch_data[['type']])\n",
     "\n",
-    "# Transform the 'city_name' column data using the fitted encoder\n",
-    "encoded = label_encoder.transform(batch_data[['type']])"
+    "# # Transform the 'city_name' column data using the fitted encoder\n",
+    "# encoded = label_encoder.transform(batch_data[['type']])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
-      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n"
-     ]
-    },
-    {
-     "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>dk1_spotpricedkk_kwh</th>\n",
-       "      <th>dk1_offshore_wind_forecastintraday_kwh</th>\n",
-       "      <th>dk1_onshore_wind_forecastintraday_kwh</th>\n",
-       "      <th>dk1_solar_forecastintraday_kwh</th>\n",
-       "      <th>temperature_2m</th>\n",
-       "      <th>relative_humidity_2m</th>\n",
-       "      <th>precipitation</th>\n",
-       "      <th>rain</th>\n",
-       "      <th>snowfall</th>\n",
-       "      <th>weather_code</th>\n",
-       "      <th>cloud_cover</th>\n",
-       "      <th>wind_speed_10m</th>\n",
-       "      <th>wind_gusts_10m</th>\n",
-       "      <th>type_encoded</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>0.48757</td>\n",
-       "      <td>0.428250</td>\n",
-       "      <td>1.065542</td>\n",
-       "      <td>0.712989</td>\n",
-       "      <td>7.9</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>24.5</td>\n",
-       "      <td>49.3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>0.52150</td>\n",
-       "      <td>0.374083</td>\n",
-       "      <td>0.968125</td>\n",
-       "      <td>0.740813</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>23.1</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>0.53478</td>\n",
-       "      <td>0.322542</td>\n",
-       "      <td>0.848917</td>\n",
-       "      <td>0.666078</td>\n",
-       "      <td>6.8</td>\n",
-       "      <td>73.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>21.2</td>\n",
-       "      <td>43.9</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>0.60012</td>\n",
-       "      <td>0.297750</td>\n",
-       "      <td>0.743667</td>\n",
-       "      <td>0.498373</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>19.6</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0.70021</td>\n",
-       "      <td>0.281875</td>\n",
-       "      <td>0.633917</td>\n",
-       "      <td>0.315199</td>\n",
-       "      <td>5.8</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>15.3</td>\n",
-       "      <td>37.1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    dk1_spotpricedkk_kwh  dk1_offshore_wind_forecastintraday_kwh  \\\n",
-       "48               0.48757                                0.428250   \n",
-       "38               0.52150                                0.374083   \n",
-       "33               0.53478                                0.322542   \n",
-       "23               0.60012                                0.297750   \n",
-       "18               0.70021                                0.281875   \n",
-       "\n",
-       "    dk1_onshore_wind_forecastintraday_kwh  dk1_solar_forecastintraday_kwh  \\\n",
-       "48                               1.065542                        0.712989   \n",
-       "38                               0.968125                        0.740813   \n",
-       "33                               0.848917                        0.666078   \n",
-       "23                               0.743667                        0.498373   \n",
-       "18                               0.633917                        0.315199   \n",
-       "\n",
-       "    temperature_2m  relative_humidity_2m  precipitation  rain  snowfall  \\\n",
-       "48             7.9                  67.0            0.1   0.1       0.0   \n",
-       "38             7.5                  67.0            0.1   0.1       0.0   \n",
-       "33             6.8                  73.0            0.1   0.1       0.0   \n",
-       "23             6.4                  76.0            0.2   0.2       0.0   \n",
-       "18             5.8                  81.0            0.1   0.1       0.0   \n",
-       "\n",
-       "    weather_code  cloud_cover  wind_speed_10m  wind_gusts_10m  type_encoded  \n",
-       "48          51.0        100.0            24.5            49.3             0  \n",
-       "38          51.0        100.0            23.1            47.5             0  \n",
-       "33          51.0        100.0            21.2            43.9             0  \n",
-       "23          51.0         81.0            19.6            40.0             0  \n",
-       "18          51.0         61.0            15.3            37.1             0  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Convert the output of the label encoding to a dense array and concatenate with the original data\n",
-    "X_batch = pd.concat([batch_data, pd.DataFrame(encoded)], axis=1)\n",
+    "batch_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Convert the output of the label encoding to a dense array and concatenate with the original data\n",
+    "# X_batch = pd.concat([batch_data, pd.DataFrame(encoded)], axis=1)\n",
+    "\n",
+    "X_batch = batch_data\n",
     "\n",
     "# Drop columns 'date', 'city_name', 'unix_time' from the DataFrame 'X'\n",
-    "X_batch = X_batch.drop(columns=['date', 'time', 'timestamp', 'type'])\n",
+    "X_batch = X_batch.drop(columns=['date', 'time', 'timestamp'])\n",
     "\n",
-    "# Rename the newly added column with label-encoded city names to 'city_name_encoded'\n",
-    "X_batch = X_batch.rename(columns={0: \"type_encoded\"})\n",
+    "# # Rename the newly added column with label-encoded city names to 'city_name_encoded'\n",
+    "# X_batch = X_batch.rename(columns={0: \"type_encoded\"})\n",
     "\n",
     "# Displaying the first 5 rows of the modified DataFrame\n",
     "X_batch.head()"
@@ -1096,25 +261,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48    0.48757\n",
-       "38    0.52150\n",
-       "33    0.53478\n",
-       "23    0.60012\n",
-       "18    0.70021\n",
-       "Name: dk1_spotpricedkk_kwh, dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Extract the target variable 'dk1_spotpricedkk_kwh' from the batch data\n",
     "y_batch = X_batch.pop('dk1_spotpricedkk_kwh')\n",
@@ -1125,67 +274,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "48    0.48757\n",
-       "38    0.52150\n",
-       "33    0.53478\n",
-       "23    0.60012\n",
-       "18    0.70021\n",
-       "       ...   \n",
-       "32    0.37590\n",
-       "47    0.37292\n",
-       "27    0.25366\n",
-       "64    0.22315\n",
-       "96    0.16408\n",
-       "Name: dk1_spotpricedkk_kwh, Length: 106, dtype: float64"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Display the target variable\n",
-    "y_batch"
+    "# Make predictions on the batch data using the retrieved XGBoost regressor model\n",
+    "predictions = retrieved_xgboost_model.predict(X_batch)\n",
+    "\n",
+    "# Display the first 5 predictions\n",
+    "predictions[:5]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
-      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([0.25547686, 0.37913612, 0.33905983, 0.3961694 , 0.5968245 ],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Make predictions on the batch data using the retrieved XGBoost regressor model\n",
-    "predictions = retrieved_xgboost_model.predict(X_batch)\n",
+    "label = batch_data[\"dk1_spotpricedkk_kwh\"]\n",
+    "y_pred = retrieved_xgboost_model.predict(X_batch)\n",
     "\n",
-    "# Display the first 5 predictions\n",
-    "predictions[:5]"
+    "data = {\n",
+    "    'prediction': [y_pred],\n",
+    "    'label': [label],\n",
+    "}\n",
+    "\n",
+    "monitor_df = pd.DataFrame(data)\n",
+    "monitor_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = batch_data[\"dk1_spotpricedkk_kwh\"]\n",
+    "y_pred = retrieved_xgboost_model.predict(X_batch)\n",
+    "\n",
+    "data = {\n",
+    "    'prediction': y_pred,\n",
+    "    'label': label,\n",
+    "}\n",
+    "\n",
+    "monitor_df = pd.DataFrame(data)\n",
+    "monitor_df"
    ]
   },
   {