{"id":19321486,"url":"https://github.com/burhanahmed1/data-analysis-with-python","last_synced_at":"2025-02-24T05:25:23.599Z","repository":{"id":246800330,"uuid":"822205205","full_name":"burhanahmed1/Data-Analysis-with-Python","owner":"burhanahmed1","description":"Data-Acquisition and Basic Insights, Data Wrangling, Exploratory Data Analysis (EDA), and Training Prediction Models(Machine Learning) on two datasets.","archived":false,"fork":false,"pushed_at":"2024-07-06T06:10:25.000Z","size":934,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-06T05:29:09.851Z","etag":null,"topics":["data-analysis","data-aquisition","data-insights","data-science","data-wrangling","dataanalytics","datascience-machinelearning","eda","exploratory-data-analysis","machine-learning-models","matlpotlib","numpy","pandas","practice-programming","prediction-model","python","scikit-learn","scikitlearn-machine-learning","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/burhanahmed1.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-30T15:18:04.000Z","updated_at":"2024-08-08T15:49:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"69070ede-970b-4bc9-ab06-d87df7cd2f6a","html_url":"https://github.com/burhanahmed1/Data-Analysis-with-Python","commit_stats":null,"previous_names":["burhanahmed1/data-analysis-with-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FData-Analysis-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FData-Analysis-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FData-Analysis-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FData-Analysis-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/burhanahmed1","download_url":"https://codeload.github.com/burhanahmed1/Data-Analysis-with-Python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240422619,"owners_count":19798791,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-aquisition","data-insights","data-science","data-wrangling","dataanalytics","datascience-machinelearning","eda","exploratory-data-analysis","machine-learning-models","matlpotlib","numpy","pandas","practice-programming","prediction-model","python","scikit-learn","scikitlearn-machine-learning","seaborn"],"created_at":"2024-11-10T01:37:26.972Z","updated_at":"2025-02-24T05:25:23.576Z","avatar_url":"https://github.com/burhanahmed1.png","language":"Jupyter Notebook","readme":"# Data-Analysis-with-Python\n\nThis repository contains comprehensive notebooks for various stages of data analysis and machine learning model building, using two datasets: AutoMobiles and Laptop Pricing. The repository is organized into four main folders, each containing notebooks for both datasets.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Repository Structure](#repository-structure)\n- [Datasets](#datasets)\n- [Technologies Used](#technologies-used)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Introduction\n\nThis repository provides a structured approach to data acquisition, data wrangling, exploratory data analysis (EDA), and prediction model building. The analysis is performed on two datasets: AutoMobiles and Laptop Pricing. Each stage of the process is documented in Jupyter notebooks, offering a clear and reproducible workflow.\n\n## Repository Structure\n\nThe repository is organized into the following folders:\n\n1. **Data Acquisition and Basic Insights**: \n   - `AutoMobiles_data_acquisition.ipynb`\n   - `Laptop_data_acquisition.ipynb`\n\n2. **Data Wrangling**: \n   - `AutoMobiles_data_wrangling.ipynb`\n   - `Laptop_data_wrangling.ipynb`\n\n3. **Exploratory Data Analysis (EDA)**:\n   - `AutoMobiles_EDA.ipynb`\n   - `Laptop_EDA.ipynb`\n\n4. **Prediction Models**:\n   - `AutoMobiles_prediction_models.ipynb`\n   - `Laptop_prediction_models.ipynb`\n\nEach notebook in the folders is designed to handle the respective dataset, providing a step-by-step guide through the different phases of data science.\n\n\n## Datasets\n\nThe datasets used in this repository are included in the respective folders:\n\n- **AutoMobiles Dataset**: Contains data related to various car attributes and prices.\n- **Laptop Pricing Dataset**: Contains data related to laptop features and their corresponding prices.\n\n## Technologies Used\n- Scikit-learn\n- Scipy\n- Pandas\n- Numpy\n- Matplotlib\n- Seaborn\n- Jupyter Notebook\n\n## Usage\n1. Clone the repository:\n```bash\ngit clone https://github.com/burhanahmed1/machine-learning-analysis.git\ncd machine-learning-analysis\n```\n2. Run Jupyter Notebook:\n```bash\njupyter notebook\n```\n3. Navigate to the respective folder and open the notebook of your choice. Follow the instructions and run the cells to execute the analysis.\n\n## Contributing\nContributions are welcome! If you would like to contribute to this project, you can fork the repository and create a pull request with your improvements. Here's how you can do it:\n\n1. Fork the repository.\n2. Create a new branch for your feature or bugfix.\n3. Make your changes and commit them.\n4. Push your changes to your forked repository.\n5. Create a pull request from your branch to the main repository.\n\n## License\nThis project is licensed under the MIT License.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburhanahmed1%2Fdata-analysis-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fburhanahmed1%2Fdata-analysis-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburhanahmed1%2Fdata-analysis-with-python/lists"}