How to predicate gender using logistic algorithm from excel file ?

Question

/forums/uploadfile/e2e758/01252024204521PM/Data.csv

I'm beginner work on machine learning using python colab
I have excel file have 5 columns
1-Favorite Color: Favorite color (colors reported by respondents were mapped to either warm, cool or neutral).
2. Favorite Music Genr: Favorite broad music genre.
3. Favorite Beverage: Favorite alcoholic drink.
4. Favorite Soft Drink: Favorite fizzy drink
5. Gender: Binary gender as reported by the respondents

My question How to Predict Gender using logisic algorithm with preprocessing and Exploratory Data Analysis?

I attached my files excel attached with post

what i try using python colab

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())
# ... perform further exploratory analysis

# Step 2: Preprocessing

# Handle missing values
df = df.dropna()  # remove rows with missing values, or use imputation techniques

how to complete code until i predicate with gender please ?

Data.csv file added as below

ahmed salah · Answer

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

Mayooran Navamany · Answer

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:
 classification_report(y_test y_pred)) print(Confusion Matrix:
 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.