Unlocking Efficiency: Productivity Automation with scikit-learn

#productivity #selfimprovement #wellness #lifestyle

We spend half our day on garbage tasks. Setting reminders, sorting files, context-switching between twelve different projects. It drains the battery fast.

Let's fix that with scikit-learn.

I want to look at reminders first. We miss them constantly or set them too late. We can train a K-Means clustering model to spot patterns in your calendar data instead of guessing.

Here's a quick way to group similar events using K-Means:

from sklearn.cluster import KMeans
import pandas as pd
import numpy as np

# Sample data
data = {
 'Event': ['Meeting', 'Lunch', 'Meeting', 'Gym', 'Lunch', 'Meeting'],
 'Time': [8, 12, 14, 16, 18, 20]
}

df = pd.DataFrame(data)

# One-hot encoding for categorical data
df = pd.get_dummies(df, columns=['Event'])

# Scale data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_data = scaler.fit_transform(df)

# K-Means clustering
kmeans = KMeans(n_clusters=2)
kmeans.fit(scaled_data)

# Print cluster labels
print(kmeans.labels_)

The script splits your schedule into two distinct buckets. Work and personal. You spot the habits and automate the pings.

Email management is the next boss fight. Train a simple Naive Bayes classifier to separate the noise from actual priority messages. CountVectorizer handles the text parsing.

Check this out:

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Sample email data
emails = [
 {'subject': 'Urgent: Project Deadline', 'body': 'We need to finalize the project by Friday.'},
 {'subject': 'Meeting Invitation', 'body': 'I\'d like to schedule a meeting with you.'},
 {'subject': 'Personal Email', 'body': 'Hey, how was your weekend?'}
]

# CountVectorizer
vectorizer = CountVectorizer()
email_vectors = vectorizer.fit_transform([email['subject'] + email['body'] for email in emails])

# Naive Bayes classifier
classifier = MultinomialNB()
classifier.fit(email_vectors, [0, 1, 0]) # Important/unimportant labels

# Classify new email
new_email = {'subject': 'Upcoming Event', 'body': 'I\'d like to invite you to a networking event.'}
new_email_vector = vectorizer.transform([new_email['subject'] + new_email['body']])
print(classifier.predict(new_email_vector))