Data Cleaning and Filtering Project

This project focuses on data cleaning and filtering using Python’s data manipulation libraries. The data used in this notebook, customer_train.csv, contains customer-related information that was preprocessed to create a cleaner and more usable dataset for modeling purposes.

Overview

Raw data often requires extensive cleaning before it can be utilized effectively in analysis or modeling. This notebook showcases a full workflow for data cleaning, data type conversions, and filtering relevant information from a real-world customer dataset.

Key Steps in the Notebook

1. Data Loading and Initial Exploration
   The notebook begins by loading the raw dataset and exploring its structure, including summary statistics and data types. This initial exploration helps identify potential issues such as missing values or incorrectly formatted columns.

2. Data Cleaning Operations
   The data cleaning process addresses various common data issues:

   • Data type adjustments: Columns were converted to appropriate data types to improve memory efficiency and ensure accurate computations.
   • Outlier detection and treatment: Extreme values were identified and either removed or adjusted to reduce their impact on the analysis.

3. Data Filtering
   To focus on relevant subsets, data was filtered according to specific criteria, making it ready for downstream analysis or modeling tasks.

Requirements

This project requires the following Python libraries:

• pandas
• numpy

Running the Notebook

To run this notebook, simply open it in Jupyter Notebook or JupyterLab and execute the cells sequentially. Ensure that customer_train.csv is available in the working directory.

Project Goals

The main objective of this project is to demonstrate practical data cleaning techniques and improve data quality for further analysis. By following the procedures in this notebook, you will gain insights into essential data preprocessing steps that are crucial for reliable and insightful data analysis.