How to predicate gender using logistic algorithm from excel file ?

i get error on lines of standard scalar 

scaler = StandardScaler()

df[['numerical_feature1', 'numerical_feature2']] = scaler.fit_transform(df[['numerical_feature1', 'numerical_feature2']])

what you mean of these two columns these columns not exist

data exist on file below csv 

https://www.mediafire.com/file/8h65adtolvx6nyo/Data.csv/file

Load and Explore the Data:

• Load the dataset into a DataFrame.
• Check the first few rows using df.head() to understand the structure of the data.
• Use df.info() to get information about the data types and missing values.

import pandas as pd

# Load the dataset into a DataFrame
df = pd.read_csv('Data.csv')

# Explore the dataset
print(df.head())
print(df.info())

Data Preprocessing:

• Handle missing values: You can either remove rows with missing values or use imputation techniques.
• Convert categorical variables into numerical representations (e.g., one-hot encoding).
•  
• Standardize/normalize numerical features if necessary.

# Handle missing values (you can use other techniques as well)
df = df.dropna()

# Convert categorical variables into numerical representations
df = pd.get_dummies(df, columns=['favourite Color', 'Favorite Music Genr', 'Favorite Beverage', 'Favorite Soft Drink'], drop_first=True)

# Standardize/normalize numerical features if necessary
# (e.g., using StandardScaler from sklearn)
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df[['numerical_feature1', 'numerical_feature2']] = scaler.fit_transform(df[['numerical_feature1', 'numerical_feature2']])

Exploratory Data Analysis (EDA):

• Visualize the data using plots to gain insights.
• Check the distribution of the target variable ('Gender').

import seaborn as sns
import matplotlib.pyplot as plt

# Example of EDA - pairplot
sns.pairplot(df, hue='Gender')
plt.show()

# Check the distribution of the target variable
sns.countplot(x='Gender', data=df)
plt.show()

Train a Logistic Regression Model:

• Split the data into training and testing sets.
• Train a logistic regression model on the training data.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Split the data into features (X) and target variable (y)
X = df.drop('Gender', axis=1)
y = df['Gender']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

Evaluate the Model:

• Use the trained model to make predictions on the test set.
• Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

This is a basic outline, and you might need to adjust it based on the specifics of your dataset. Additionally, you can explore more advanced techniques and feature engineering depending on the characteristics of your data.