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Transformational Notes from 'Hey Jude': Embracing Vulnerability

Updated: at 04:12 AM

Welcome, my friend. We all have luggage to carry, don’t we? Heartbreak smeared with shades of disappointment. An unsettling feeling of stillness in the rough winds of this wild ride we call life. Often, we find ourselves waiting for someone to perform with.

I, the weather-worn traveler of this path, can resonate with your struggles. Yes, I’ve been there. I was trapped in a maze of emotional torment, overwhelmed by adversity, and questioned my worth. Much like a poignant verse in a Beatles song, I echoed, “Hey Jude, don’t make it bad. Take a sad song and make it better.” That Jude, my friend, was a metaphor for me, my soul choking under the weight of existence.

There’s power in being vulnerable, acknowledging your pain, and signing your lament. It’s like shedding the skin of pretense, peeling off the layers of imposed bravado. Through such a moment of raw candor with myself, I began my journey toward healing and self-discovery.

”Remember,” says our beloved song, “to let her into your heart”. Opening up and letting love and vulnerability co-exist in your heart might seem daunting, but it’s tremendously transformative. For me, it allowed light to penetrate the darkest cavities of my soul; it’s what paved the way for resilience.

Perseverance, rooted in the soil of patience and undying hope, bore the fruits of my growth. The world would sway and threaten to cast me asunder, but I chose to stay, fight, and conquer like an unyielding tree in the face of a storm. Amidst all, the song gave me refuge. The lyrics held onto me like a lover’s warm embrace - “Hey Jude, don’t be afraid. You were made to go out and get her.”

And onto that road, seek those who attribute to your strength as you transition from a fragile seedling into a stalwart oak. Much like those nurturing rains and guiding sun rays, I had my tribe, too. The ones who sang with me, “the minute you let her under your skin, then you begin to make it better.” They reaffirmed the importance of inner support or that one person who understands your silent prayers your echoed fears, and encourages you to become better, to rise above.

”Hey Jude” isn’t just a song; it’s a beacon illuminating the path for those lost in life’s intricate labyrinth. It’s an anthem that resonates with the hearts of millions because it effectively intertwines our shared human experiences, threading us all into one woven tapestry.

So, to you, embarking on your journey of self-discovery and resilience, remember this – love yourself, discover your strengths, welcome support, make yourself an emblem of perseverance, and embrace vulnerability. Because at the end of it all, it’s always about finding the ability to transform your dark moment into hues of dawn. After all, “Na-na-na, naa-naa, naa-naa, hey Jude” all starts and ends with you. Go out there. Embrace the wild, the storm, the calm. And make it better.

# Import required libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np

# Load data (Luggage to carry: Inputs or Features)
df = pd.read_csv('data.csv')

# We declare two empty lists to hold our categorical and numerical columns
categorical_cols = []
numerical_cols = []

# We then iterate over each column in our dataset
# and append the column name to the appropriate list
for c in df.columns:
    if df[c].dtype == object:  # if the column is categorical in nature
        categorical_cols.append(c)
    else:  # if the column is numerical in nature
        numerical_cols.append(c)

# Remove the target column from the list of numerical columns
numerical_cols.remove('target')

# Preprocessing (Vulnerability to acknowledge the pain: Data preprocessing)
# Set up the pipeline for handling numerical features
# This pipeline imputes missing values with the median and scales features to have zero mean and unit variance
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

# Set up the pipeline for handling categorical features
# This pipeline fills missing values with the string 'missing' and then performs one-hot-encoding
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine both numerical and categorical pipelines
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numerical_cols),
        ('cat', categorical_transformer, categorical_cols)
])

# Combine preprocessing and modeling steps into one pipeline
# We are using a random forest classifier for the modeling step (Healing and self-discovery: Modeling)
clf = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', RandomForestClassifier())
])

# Define target and features
features = df.drop('target', axis=1)
target = df['target']

# Split the data into train, validation, and test sets (Rain and the Sun: Training and Validation)
X, X_test, y, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.25, random_state=42)

# Define parameters for Grid Search (Fine-tuning our model)
param_grid = {
    'classifier__n_estimators': [10, 20, 50],
    'classifier__max_depth': [5, 10, 20],
    'classifier__min_samples_split': [2, 3, 4]
}

# Perform a grid search by training several models with different combinations of the hyperparameters specified above
grid = GridSearchCV(clf, param_grid=param_grid, cv=5, scoring='accuracy')
grid.fit(X_train, y_train)

# Print the parameters of the best model (Fine-tuning our model)
print('Best parameters:', grid.best_params_)

# Evaluate our final model on the validation data (Testing and Performance Evaluation)
y_val_pred = grid.predict(X_val)
accuracy_val = accuracy_score(y_val, y_val_pred)
print(f'Validation Accuracy: {accuracy_val}')

# Finally, evaluate our model on the test data (Resilience in the face of adversities: Testing and Performance Evaluation)
y_test_pred = grid.predict(X_test)
accuracy_test = accuracy_score(y_test, y_test_pred)
print(f'Test Accuracy: {accuracy_test}')