Data Science Projects

Table of Contents

1. Folder Hierarchy
2. Introduction
3. Purpose
4. Dependencies
5. Installation
6. Code Explanation
7. Usages
8. Examples
9. Error Handling
10. Best Practices
11. Troubleshooting
12. Limitations
13. Conclusion

Folder Hierarchy

• EDA & ML on House Pricing Dataset
• EDA & ML on Titanic Dataset
• EDA on 911 dataset Risk Analysis
• EDA on Credit Score Dataset
• EDA on Facebook Friends Networks
• EDA on Instagram Coding Influencers Dataset
• Project on Decision Tree and Random Forest
• Project on K Means Clustering
• Project on K Nearest Neighbors
• Project on Linear Regression
• Project on Logistic Regression
• Project on Support Vector Machines

Introduction

The Data Science Projects folder contains various projects related to data analysis, machine learning, and exploratory data analysis (EDA). Each project focuses on a specific dataset and utilizes different techniques and algorithms to extract insights and make predictions.

Purpose

The purpose of this documentation is to provide an overview of the projects and their functionalities. It serves as a guide for understanding the file hierarchy, dependencies, installation process, code explanation, usages, examples, error handling, best practices, troubleshooting, and limitations of the projects.

Dependencies

The projects in this folder have dependencies on the following libraries:

• numpy
• pandas
• matplotlib
• seaborn
• sklearn
• category_encoders
• networkx
• cufflinks
• warnings
• json

Installation

To run the code in these projects, the following libraries need to be installed:

!pip install numpy pandas matplotlib seaborn sklearn category_encoders networkx cufflinks

Code Explanation

Each project contains a Jupyter Notebook file (.ipynb) that includes code for data preprocessing, exploratory data analysis, machine learning algorithms, and visualizations. The code is well-documented and includes explanations for each step.

Usages

These projects can be used for various purposes, including:

• Exploring and analyzing different datasets
• Implementing machine learning algorithms
• Gaining insights from data through visualizations
• Predicting outcomes based on given features

Error Handling

Possible errors that may occur include missing dependencies, incorrect file paths, or incompatible data formats. To resolve these errors, ensure that all dependencies are installed correctly, check the file paths in the code, and verify that the data is in the expected format.

Best Practices

To ensure the correct usage of the code, follow these best practices:

• Install the required dependencies before running the code.
• Read the documentation and code comments for a better understanding of each project.
• Use appropriate data preprocessing techniques based on the dataset.
• Evaluate the performance of machine learning models using appropriate metrics.
• Handle missing values and outliers appropriately.
• Visualize the data to gain insights and validate the results.

Troubleshooting

For troubleshooting or further reference, you can visit the official documentation of the libraries used in the projects:

• numpy: https://numpy.org/doc/
• pandas: https://pandas.pydata.org/docs/
• matplotlib: https://matplotlib.org/stable/contents.html
• seaborn: https://seaborn.pydata.org/tutorial.html
• sklearn: https://scikit-learn.org/stable/user_guide.html
• category_encoders: https://contrib.scikit-learn.org/category_encoders/
• networkx: https://networkx.org/documentation/stable/
• cufflinks: https://plotly.com/python/cufflinks/

Limitations

1. The projects may have limitations in terms of the size and complexity of the datasets they can handle.
2. The code may not cover all possible edge cases or handle all types of data.
3. The performance of machine learning models may vary depending on the dataset and the chosen parameters.

Conclusion

This documentation provides an overview of the Data Science Projects folder, including the file hierarchy, purpose, dependencies, installation process, code explanation, usages, examples, error handling, best practices, troubleshooting, and limitations. It serves as a comprehensive guide for understanding and utilizing the projects effectively.