input: Write a python function to fit a linear regression model and return the intercept and coefficients.
  assert fit_linear_regression(df) == {'Intercept': 3.2, 'Coefficient of feature1': 1.5, 'Coefficient of feature2': -0.7}
  assert fit_linear_regression(another_df) == {'Intercept': 5.1, 'Coefficient of featureA': 2.3, 'Coefficient of featureB': -1.4}
output: def fit_linear_regression(df):
      from sklearn.linear_model import LinearRegression
  
      X = df.drop(columns=['target'])
      y = df['target']
      
      model = LinearRegression()
      model.fit(X, y)
      
      intercept = model.intercept_
      coefficients = model.coef_
      
      result = {'Intercept': intercept}
      for feature, coef in zip(X.columns, coefficients):
          result[f'Coefficient of {feature}'] = coef
      
      return result