Data Analysis with Python

Welcome to the Data Analysis with Python repository! This project contains an in-depth analysis of cab investments, leveraging Python for data exploration, visualization, and business insights. It is a comprehensive resource for anyone interested in data-driven decision-making in the transportation sector.

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Repository Overview

This repository includes all materials related to the Cab Investment Analysis project, including Python notebooks, reports, presentation slides, and raw datasets.

Key Files

• CAB INVESTMENT ANALYSIS - CHUKWUJEKWU J. EZEMA (revised).ipynb
  The main Jupyter Notebook containing the full analysis pipeline.

  • Data cleaning and exploration
  • Statistical analysis and hypothesis testing
  • Data visualization
  • Insights and business recommendations

• Cab Investment Analysis Project - Chukwujekwu Joseph Ezema.pdf
  Detailed PDF report summarizing the entire project, methodology, and findings.

• Cab Investment Analysis Project - Chukwujekwu Joseph Ezema.pptx
  Presentation slides for a quick overview of key findings and business implications.

• Data Intake Report_VI - C.J Ezema.pdf
  Documentation on data collection, intake, and preprocessing.

Datasets

• Cab_Data.csv
  The primary dataset containing cab transaction records.

• City.csv
  List of cities and associated metadata relevant to the cab data.

• Customer_ID.csv
  Customer identifiers, useful for customer segmentation and analysis.

• Transaction_ID.csv
  Transaction records, including IDs and associated metadata.

• US Holiday Dates (2004-2021).csv
  Supplemental dataset used for contextual time-based analysis.

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Getting Started

1. Clone the repository:

   git clone https://github.com/Jeks042/Data-Analysis-with-Python.git

2. Open the Jupyter notebook:
   Launch JupyterLab or Jupyter Notebook and open the main .ipynb file for a step-by-step walkthrough of the analysis.

3. Review the PDF and PPTX documents for summarized insights and presentations.

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Project Highlights

• End-to-End Cab Investment Analysis: From data intake and cleaning, through exploratory data analysis (EDA), to business recommendations.
• Reproducible Analysis: All code is available in the notebook for transparency and reproducibility.
• Comprehensive Documentation: Includes both technical and business-facing reports for diverse audiences.

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About

Author: Chukwujekwu Joseph Ezema
For questions, feedback, or collaboration, please open an issue or contact via GitHub.

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Note: This README summarizes the first 10 files in the repository.
To view the full file listing and explore more resources, visit the repository’s GitHub contents page.