https://github.com/praju-1/data_science_projects

It contains the necessary code, datasets, and documentation to understand, replicate, and build upon the project's findings and methodologies.
https://github.com/praju-1/data_science_projects

data-science datasets deep-learning exploratory-data-analysis machine-learning matplotlib numpy pandas predictive-analytics python seaborn sklearn statistics visualization

Last synced: 13 days ago
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It contains the necessary code, datasets, and documentation to understand, replicate, and build upon the project's findings and methodologies.

• Host: GitHub
• URL: https://github.com/praju-1/data_science_projects
• Owner: praju-1
• Created: 2023-06-06T18:09:29.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-05-13T06:56:49.000Z (9 months ago)
• Last Synced: 2024-12-15T14:17:37.128Z (2 months ago)
• Topics: data-science, datasets, deep-learning, exploratory-data-analysis, machine-learning, matplotlib, numpy, pandas, predictive-analytics, python, seaborn, sklearn, statistics, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 10.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
DATA SCIENCE PROJECTS

This repository contains the code and documentation for a data science project aimed at [briefly describe the objective or problem being addressed].

PROJECT STRUCTURE

* The project is organized as follows:

1. data: This directory contains the datasets used for analysis and modeling. Provide brief descriptions of the datasets.

2. notebooks: This directory contains Jupyter notebooks used for data exploration, preprocessing, modeling, and analysis. Provide a brief overview of each notebook.

3. scripts: This directory contains any supporting scripts or utility functions used in the project.

4. reports: This directory contains reports or visualizations generated during the project.

requirements.txt: This file lists the dependencies required to run the project. Include the command to install these dependencies.

PROJECT WORKFLOW

* Data Collection: Explain how the data was collected or obtained, including any sources or APIs used.

* Data Exploration: Describe the initial exploration and analysis of the datasets, including summary statistics, data visualization, and identifying any data quality issues.

* Data Preprocessing: Explain the steps taken to preprocess and clean the data, including handling missing values, feature engineering, and data transformation techniques applied.

* Modeling: Describe the modeling approach used, including the selection of algorithms, parameter tuning, and evaluation metrics chosen.

* Model Evaluation: Discuss the evaluation results of the trained models, including performance metrics, accuracy, and any other relevant evaluation criteria.

* Results and Insights: Summarize the key findings, insights, and conclusions drawn from the project.

* Future Work: Mention any potential areas for further improvement, future enhancements, or additional analysis that could be pursued.

CONTRIBUTION

Specify if and how contributions to the project are welcome, including guidelines for submitting pull requests or raising issues.

Feel free to reach out if you have any questions or suggestions regarding this project.