Delivery Data Preprocessing

First Task: Data Preprocessing for Delivery Dataset

Preprocessing the dataset involves preparing it for analysis and modeling. Here’s a detailed breakdown of steps tailored to the columns in the dataset.

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Step 1: Understand the Dataset

• Columns to review:

  • Package Information: package_id

  • Location Data: lng, lat, region_id, city, aoi_id, aoi_type

  • Courier Data: courier_id

  • Task-Event Information: accept_time, accept_gps_time, accept_gps_lng, accept_gps_lat, delivery_time, delivery_gps_time, delivery_gps_lng, delivery_gps_lat

  • Context Information: ds

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Step 2: Data Cleaning

1. Handle Missing Values:

   • Key Columns: Check for missing values in essential columns (accept_time, delivery_time, lng, lat, courier_id).

   • Action:

     • Drop rows with missing accept_time or delivery_time as these are crucial for the target variable.

     • Impute missing lng/lat values with the region or city mean.

2. Remove Duplicates:

   • Use package_id as a unique identifier to ensure no duplicate entries exist.

   • Example:

     df = df.drop_duplicates(subset='package_id', keep='first')

3. Verify Data Types:

   • Ensure columns have the correct data type:

     • accept_time & delivery_time: Convert to datetime.

     • lng, lat: Ensure they are float.

     • aoi_type: Ensure it’s categorical.

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Step 3: Outlier Detection

1. Delivery Duration:

   • Identify abnormally long or short delivery durations.

   • Use z-score or IQR to detect and remove these outliers.

2. Geospatial Anomalies:

   • Validate lng/lat values for any out-of-bound entries:

     • Latitude range: -90 to 90.

     • Longitude range: -180 to 180.

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Step 4: Data Transformation

1. Encode Categorical Variables:

   • Convert city and aoi_type to numeric using one-hot or label encoding.

   • Example:

     df = pd.get_dummies(df, columns=['aoi_type'], drop_first=True)

2. Standardize Numerical Features:

   • Normalize features like distance and delivery_duration to ensure all variables are on a similar scale.

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Step 5: Save Cleaned Data

• Save the preprocessed dataset for further modeling and analysis.

• Example:

  df.to_csv('cleaned_delivery_data.csv', index=False)

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