Merge remote-tracking branch 'origin/main'

Author: HammadAli08

Data maker.py

@@ -1,36 +1,29 @@
 import pandas as pd
 import numpy as np
 import random
-
 # Settings
 num_rows = 1000 # Factory workers
 num_columns = 12  # Data columns
-
 # Base columns (will be reused in patterns)
 worker_ids = [f"W{1000 + i}" for i in range(num_rows)]
 ages = np.random.randint(20, 60, size=num_rows)
 experience_years = [random.randint(0, age - 20) for age in ages]
 departments = np.random.choice(['Assembly', 'Maintenance', 'Packaging', 'QC', 'Logistics'], size=num_rows)
-
 # Start the dataframe
 df = pd.DataFrame({
     'WorkerID': worker_ids,
     'Age': ages,
     'Experience': experience_years,
     'Department': departments
 })
-
 # Generate 9996 additional columns with related sensor/factory data
 for i in range(1, num_columns - 4):  # -3 because we already added 4 columns above
     col_name = f"Sensor_{i}"
     # Example logic: related to experience and age, with noise
     df[col_name] = df['Experience'] * np.random.uniform(0.5, 1.5) + np.random.normal(0, 5, size=num_rows)
-
     # Insert NaNs randomly for data cleaning practice
     mask = np.random.rand(num_rows) < 0.05  # ~5% missing
     df.loc[mask, col_name] = np.nan
-
 # Save to CSV
 df.to_csv("factory_worker_data.csv", index=False)
-
 print("CSV with 10,000+ columns and realistic values generated successfully.")

Encoding.ipynb

@@ -24,7 +24,7 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "# ONE ENCODER(Nominal)",
+   "source": "# ONE ENCODER",
    "id": "fa40e0a6e557a93a"
   },
   {