So I have my type error says “float() argument must be a string or a real number, not ‘dict'”.
How could I fix this problem?

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

def get_user_input():
  features = {}
  features["Relative Compactness"] = float(input("Enter Relative Compactness: "))
  features["Surface Area"] = float(input("Enter Surface Area (m^2): "))
  features["Wall Area"] = float(input("Enter Wall Area (m^2): "))
  features["Roof Area"] = float(input("Enter Roof Area (m^2): "))
  features["Overall Height"] = float(input("Enter Overall Height (m): "))
  features["Orientation"] = input("Enter Orientation (e.g., North, South): ")
  features["Glazing Area"] = float(input("Enter Glazing Area (m^2): "))
  features["Glazing Area Distribution"] = float(input("Enter Glazing Area Distribution: "))
  return features

 def build_models(data_path):
 
  data = pd.read_csv(data_path)
  features = ["Relative Compactness", "Surface Area", "Wall Area", 
          "Roof Area", "Overall Height", "Orientation", "Glazing Area", 
          "Glazing Area Distribution"]
  heating_load = data["Heating Load"]
  cooling_load = data["Cooling Load"]

  X_train, X_test, y_train_heating, y_test_heating = train_test_split(
      data[features], heating_load, test_size=0.2, random_state=42)
  X_train, X_test, y_train_cooling, y_test_cooling = train_test_split(
      data[features], cooling_load, test_size=0.2, random_state=42)

  heating_model = LinearRegression()
  heating_model.fit(X_train, y_train_heating)

  cooling_model = LinearRegression()
  cooling_model.fit(X_train, y_train_cooling)

  return heating_model, cooling_model


def predict_loads(features, heating_model, cooling_model):
 
  features_reshaped = np.array([features])  # Reshape to 2D array
  heating_prediction = heating_model.predict(features_reshaped)[0]
  cooling_prediction = cooling_model.predict(features_reshaped)[0]
  return heating_prediction, cooling_prediction


def main():
  data_path = "ENB2012data.csv"  # Replace with your actual data path
  heating_model, cooling_model = build_models(data_path)
  user_features = get_user_input()
  heating_load, cooling_load = predict_loads(user_features, heating_model, cooling_model)

  
  print(f"nPredicted Heating Load: {heating_load:.2f}")
  print(f"Predicted Cooling Load: {cooling_load:.2f}")

if __name__ == "__main__":
  main()

I have tried searching for results but most of the results are for when the argument is a “list” and not a “dict”