Yellow Cab Case Study: Data-Driven Business Insights

Table of Contents

1. Introduction
2. Instructions
3. Technologies and Tools
4. Installation and Setup
5. Data Sources
6. Data Preprocessing
7. Exploratory Data Analysis (EDA)
8. Modeling and Algorithms
9. Results and Findings
10. Code Snippets
11. Conclusion and Recommendations

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Introduction

This repository houses the Jupyter Notebook and associated resources for a comprehensive case study of the Yellow Cab company. The objective is to employ data analysis techniques and Python programming to derive actionable business insights.

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Instructions

The notebook aligns with the sections outlined in the case document. It's highly recommended to consult the case document concurrently while going through the notebook.

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Technologies and Tools

• Python 3.x: The primary programming language used for analysis.
• Jupyter Notebook: An open-source web application that allows the creation and sharing of documents containing live code.
• pandas: A data manipulation library.
• matplotlib and seaborn: Libraries for data visualization.
• scikit-learn: Used for machine learning algorithms.

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Installation and Setup

Clone the repository and navigate to the project directory.

git clone https://github.com/zhangqi0210/Yellow_Cab.git my-project
cd my-project

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Data Sources

The data for this project is sourced from Kaggle.

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Data Preprocessing

Data preprocessing involves:

• Data Cleaning: Removal of null values and outliers.
• Feature Engineering: Creating new features that better represent the problem space.
• Data Transformation: Scaling and normalization.

python
# Example code snippet for data cleaning
df.dropna(inplace=True)

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Exploratory Data Analysis (EDA)

EDA is performed using various statistical graphics, plots, and information tables. Key techniques include:

• Distribution Analysis
• Correlation Analysis
• Time Series Analysis

python
# Example code snippet for EDA
import seaborn as sns
sns.heatmap(df.corr(), annot=True)

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Modeling and Algorithms

We employ machine learning algorithms to understand patterns and make predictions. Algorithms used include:

• Linear Regression
• Random Forest
• Clustering Algorithms

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Results and Findings

The results are presented in a digestible format supported by:

• Charts and Graphs: For visual representation of data.
• Tables: For statistical analysis.
• Code Snippets: To underline the technical competency.

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Code Snippets

Here are some key code snippets that showcase the complexity and capabilities of the analysis:

# Example of a complex query using pandas
result = df.groupby(['Category'])['Revenue'].sum().reset_index()

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Conclusion and Recommendations

The project concludes by summarizing the key findings and proposing data-driven recommendations for Yellow Cab. The methods and analyses conducted showcase a strong competency in data analysis and Python programming.

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