### Summary

Hello Community!

So, I want to show the *classification_score* from *sklearn* in the streamlit project I have with expectation the score will show as neatly arranged as in Jupyter notebook. But, it seems the behavior isn’t the same, leading the arrangement become odd and put out of the place.

I have tried several ways to re-arrange the placement namely : with st.column, st.expaner, etc but sadly to no avail.

I was wondering if anyone had the same problem previously and found the right way to do the arrangement so the score become neatly arrange like in the Jupyter notebook?

Thank you in advance.

**Expected behavior:**

**Actual behavior:**

### Steps to reproduce

**Code snippet:**

```
st.markdown("<br>", unsafe_allow_html=True)
if "scaled_data_train" in st.session_state:
st.write(st.session_state.scaled_data_train)
else:
# Setting the upload variabel
uploaded_file = st.file_uploader("Choose a file to upload for training data",
type="csv",
help="The file will be used for training the Machine Learning",
)
# Setting the upload options when there's file on uploader menu
if uploaded_file is not None:
try:
# Uploading Dataframe
dataframe = get_data(uploaded_file)
X = dataframe.drop(columns="Outcome")
y = dataframe["Outcome"]
# Storing dataframe to session state
if 'X' not in st.session_state:
st.session_state["X"] = X
if 'y' not in st.session_state:
st.session_state["y"] = y
except:
st.write("Please upload any data")
# Markdown to gice space
st.markdown("<br>", unsafe_allow_html=True)
st.markdown("<br>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align: center; color: cyan;'>Model Setting</h3>",
unsafe_allow_html=True)
st.markdown("<br>", unsafe_allow_html=True)
# Selecting Model for Classification
model_selection = st.selectbox(
"Select Machine Learning Model for Classification Task",
("Logistic Regression", "Random Forest")
)
st.write("Model selected:", model_selection)
# Setting Logistic Regression Model
if model_selection == "Logistic Regression":
col1, col2, col3 = st.columns(3)
with col1:
# Setting Logistic Regression Penalty
log_res_penalty = st.radio(
"Norm of the penalty",
('l1', 'l2', 'none'))
with col2:
# Setting Logistis Regression Solver
log_res_solver = st.radio(
"Algorithm optimization",
("lbfgs", "liblinear", "newton-cg", "newton-cholesky", "sag", "saga"
))
with col3:
# Inverse of regularization strength
log_res_inverse = st.number_input(
"Inverse of regularization",
min_value=0.001,
value=1.0,
step=0.01)
# Logistic Regression Object
log_res_obj = LogisticRegression(
penalty=log_res_penalty, C=log_res_inverse, solver=log_res_solver)
# Fitting Data to Logistic Regression Model
if st.button("Fit Data to Logistic Re Model"):
# Initiating variable to fir data
X_train = st.session_state.scaled_data_train
X_test = st.session_state.scaled_data_test
y_train = st.session_state.y_train
y_test = st.session_state.y_test
# Fitting model to data
log_res_obj.fit(X_train, y_train)
st.write("Training Success")
# Predicting train data
y_train_predict = log_res_obj.predict(X_train)
y_train_predict_df = pd.DataFrame(y_train_predict)
# Predicting test data
y_test_predict = log_res_obj.predict(X_test)
y_test_predict_df = pd.DataFrame(y_train_predict)
# Predicting F1 score
classification_report_train = classification_report(
y_train, y_train_predict, labels=[0, 1])
classification_report_test = classification_report(
y_test, y_test_predict, labels=[0, 1])
```