23rd June 2025

Author: HammadAli08

Data Cleaning.ipynb

@@ -0,0 +1,37 @@
+{
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+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
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+   },
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
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+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Data maker.py

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+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 - 3):  # -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.")