mustafademir0/ai-group-assignment

Repository for keeping each phase of project

Certainly! Here's an updated README that reflects the changes made to the code to include encoding information in a text file:

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Data Preprocessing and Encoding

This code is designed to preprocess a dataset, handle missing values, perform categorical feature encoding using LabelEncoder, and save the encoding information to a text file. The dataset used is taken from the adult.csv file, which presumably contains information about individuals and their socio-economic attributes.

Table of Contents

• Objective
• Steps
• Code Explanation
• Dependencies

Objective

The main objectives of this code are:

1. Load the dataset from the adult.csv file.
2. Remove rows with missing values.
3. Save the cleaned dataset into a new CSV file named adult_dropped_missing.csv.
4. Encode categorical features using the LabelEncoder.
5. Print out the mapping of original categorical values to their integer-encoded counterparts.
6. Save the DataFrame with integer-encoded values into a new CSV file named adult_encoded.csv.
7. Save the encoding information for each column into a text file named encoding_info.txt.

Steps

1. Load and Clean Data:

   • The code starts by loading the dataset from the adult.csv file using the Pandas library.
   • It then creates a new DataFrame df_dropped by removing rows with missing values using the dropna() function.

2. Save Cleaned Data:

   • The cleaned DataFrame df_dropped is saved into a new CSV file named adult_dropped_missing.csv using the to_csv() function.

3. Define Feature Columns and Target Column:

   • A list of feature column names (cols) is defined. This list includes columns like age, workclass, education, etc.
   • The target column name (class) is also included in the list.

4. Encode Categorical Features:

   • The code then enters a loop that iterates through each column in the cols list.
   • If the column's data type is 'object', it indicates that the column contains categorical data that needs to be encoded.
   • For each categorical column, the code uses the LabelEncoder from scikit-learn to transform the original categorical values into integer-encoded values.
   • The mapping of original values to encoded values is saved into the encoding_info dictionary.

5. Save Encoded Data:

   • Finally, the DataFrame with integer-encoded values is saved into a new CSV file named adult_encoded.csv.

6. Save Encoding Information:

   • The encoding_info dictionary containing the encoding information for each column is saved into a text file named encoding_info.txt.

Dependencies

• Python 3.x
• pandas
• scikit-learn

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Feel free to customize this README to provide additional context or information as needed.