{"id":18075636,"url":"https://github.com/tavneetsingh01/data-science-and-analytics-lab-practicals","last_synced_at":"2026-02-15T19:31:52.859Z","repository":{"id":259125846,"uuid":"875563192","full_name":"Tavneetsingh01/data-science-and-analytics-lab-practicals","owner":"Tavneetsingh01","description":"This Repository contains the lab Programs for On-Going Data Science and Analytics Lab (CSL DC205) Fall 2024","archived":false,"fork":false,"pushed_at":"2024-12-08T15:32:21.000Z","size":8403,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-12-08T16:27:16.914Z","etag":null,"topics":["conda","data-science","jupyter-notebook","linear-regression","logistic-regression","matplotlib-pyplot","naive-bayes-classifier","nlp","pandas","polars","python","scikit-learn","seaborn","sentiment-analysis"],"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/Tavneetsingh01.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":"2024-10-20T10:19:18.000Z","updated_at":"2024-12-08T15:32:24.000Z","dependencies_parsed_at":"2024-11-24T18:01:06.348Z","dependency_job_id":null,"html_url":"https://github.com/Tavneetsingh01/data-science-and-analytics-lab-practicals","commit_stats":null,"previous_names":["tavneetsingh01/data-science-and-analytics-lab-practicals"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tavneetsingh01%2Fdata-science-and-analytics-lab-practicals","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tavneetsingh01%2Fdata-science-and-analytics-lab-practicals/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tavneetsingh01%2Fdata-science-and-analytics-lab-practicals/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tavneetsingh01%2Fdata-science-and-analytics-lab-practicals/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Tavneetsingh01","download_url":"https://codeload.github.com/Tavneetsingh01/data-science-and-analytics-lab-practicals/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230364852,"owners_count":18214858,"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":["conda","data-science","jupyter-notebook","linear-regression","logistic-regression","matplotlib-pyplot","naive-bayes-classifier","nlp","pandas","polars","python","scikit-learn","seaborn","sentiment-analysis"],"created_at":"2024-10-31T11:06:45.377Z","updated_at":"2026-02-15T19:31:52.845Z","avatar_url":"https://github.com/Tavneetsingh01.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# This Repository contains the lab Programs for On-Going Data Science and Analytics Lab (CSL DC205) Fall 2024\n![Python Version](https://img.shields.io/badge/Python-3.10-fdcd3d.svg)\n![Conda Version](https://img.shields.io/badge/conda-24.9-43b02a.svg)\n## The following are the list of programs with their Jupyter-Notebook and Lecture notes (*Will be updated each week*)\n\n\u003ctable style=\"border-collapse: collapse; width: 100%;\"\u003e\n    \u003ctr\u003e\n        \u003cth style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eProgram\u003c/b\u003e\u003c/th\u003e\n        \u003cth style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eJupyter Notebook\u003c/b\u003e\u003c/th\u003e\n        \u003cth style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eLecture Notes\u003c/b\u003e\u003c/th\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ1. Write a Program to Explore various Data Manipulation Functions provided by Pandas and Visualize the Data Using Seaborn\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"pandas_librabry_functions.ipynb\"\u003ePandas Library Functions \u0026 Visualization Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ2. To predict if a person will purchase a product on a specific combination of Day, Discount and Free delivery using Naïve Bayesian Classifier.\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"naive_bayes_classifier.ipynb\"\u003eNaive Bayes Classifer Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"Lecture%20Notes/Naive_Bayesian_Classifier_Notes.pdf\"\u003eNaive Bayes Classifier Lecture Notes\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ3. Predict Employee Salary based on Year of Experience using Linear Regression.\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"linear_regression.ipynb\"\u003eLinear Regression Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n     \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ4. Predict if a person will buy an SUV based on their Age and Estimated Salary using Logistic Regression.\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"Logistic_Regression.ipynb\"\u003eLogistic Regression Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ5. Does Kyphosis exist after surgery using Decision Tree?\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/decision_tree.ipynb\"\u003eDecision Tree Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ6. Write a Program to Demonstrate Random Forest Algorithm\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/Random_Forest_algorithm_on_Iris_Dataset.ipynb\"\u003eRandom Forest Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ7. Predict if a person will buy a SUV based on Age and Estimated Salary using KNN?\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/K_Nearest_Neighbors_Buy_Suv.ipynb\"\u003eKNN Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ8. Features Extraction from Text using Word Vectorization for Text Semantics?\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/Feature_Extraction_and_Evaluation_Using_SST2.ipynb\"\u003e1. Feature Extraction and Evalutaion Notebook Using SST2 Dataset\u003c/a\u003e\u003chr\u003e \n        \u003ca href=\"/Feature_Extraction_and_Evaluation_From_Basic_Text_Example.ipynb\"\u003e2. Feature Extraction and Evalutaion Notebook Using Basic Example\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ9. Sentiment Analysis from online news website using simple natural language processing.\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/sentiment_analysis_of_online_news_article.ipynb\"\u003eSentiement Analysis Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ10. Use of KmeansClustering algorithm for classifying persons into 5 categories according to their salary.\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/K_Means_Clustering_Notebook.ipynb\"\u003eK-Means Clustering Notebook\u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003cb\u003eQ11. Write a program for demonstrating (Support Vector Machine Classifier) SVM algorithm.\u003c/b\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ca href=\"/SVM_On_Iris_Dataset.ipynb\"\u003e SVM Notebook \u003c/a\u003e\u003c/td\u003e\n        \u003ctd style=\"border: 1px solid black; padding: 8px;\"\u003e\u003ci\u003eCurrently Under Development\u003c/i\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n\u003c/table\u003e\n\n## Environment Setup Guide\n\nThis guide provides instructions on how to recreate the development environment for this project using Conda or pip. You can set up the environment using either a YAML file or a requirements text file.\n\n\u003e[!NOTE]\n\u003e Here in the lab while coding the notebooks we have used conda as a package manager and miniconda instead of anaconda as distribution because it has less number of pre installed packages and we can install packages as per our requirement. [Miniconda_3 Installation Guide](/Installation_Guides/Summary%20of%20Installation%20and%20Setup%20Steps%20For%20Miniconda3%20(a%20python%20distribution).pdf)\n\n## Table of Contents\n\n- [Using `required-env-packages-list.yml`](#using-required-env-packages-listyml)\n- [Using `required-packages-list.txt`](#using-required-packages-listtxt)\n- [Using `pip`](#using-pip)\n\n## Using [`required-env-packages-list.yml`](/required-env-packages-list.yml)\n\nTo create a Conda environment from a YAML file, follow these steps:\n\n1. **Open your terminal** (Anaconda Prompt or command line).\n\n2. **Navigate to your project directory** where the `required-env-packages-list.yml` file is located:\n   ```bash\n   cd path/to/your/project  \n   ```\n3. Create the environment by running the following command:\n    ```bash\n    conda env create -f required-env-packages-list.yml\n    ```\n4. Activate the environment with:\n    ```bash\n    conda activate \u003cenvironment-name\u003e\n    ```\n    Replace \u003cenvironment-name\u003e with the name specified in the YAML file.\n## Using [`required-packages-list.txt`](/required-packages-list.txt)\nTo create a Conda environment from a text file, follow these steps:\n1. Open your terminal (Anaconda Prompt or command line).\n2. Navigate to your project directory where the [`required-packages-list.txt`](/required-packages-list.txt) file is located:\n\n    ```bash\n    cd path/to/your/project\n    ```\n3. Create a new environment (replace \u003cenvironment-name\u003e with your desired name):\n\n    ```bash\n    conda create --name \u003cenvironment-name\u003e --file required-packages-list.txt\n    ```\n4. Activate the environment with:\n\n    ```bash\n    conda activate \u003cenvironment-name\u003e\n    ```\n## Using pip\nIf you prefer to use pip to create a virtual environment, follow these steps:\n1. Open your terminal.\n2. Navigate to your project directory where the [`required-packages-list.txt`](/required-packages-list.txt) file is located:\n\n    ```bash\n    cd path/to/your/project\n    ```\n3. Create a virtual environment (replace \u003cenv-name\u003e with your desired name):\n\n    ```bash\n    python -m venv \u003cenv-name\u003e\n    ```\n4. Activate the virtual environment:\n    1. On Windows:\n        - In `PowerShell` :\n\n        ```powershell \n        \u003cenv-name\u003e\\Scripts\\Activate.ps1\n        ```\n        - In `cmd` :\n\n        ```cmd\n        \u003cenv-name\u003e\\Scripts\\activate.bat\n        ```\n    2. On macOS/Linux:\n        ```bash\n        source \u003cenv-name\u003e/bin/activate\n        ```\n5. Install the required packages using:\n    ```bash\n    pip install -r required-packages-list.txt\n    ```\n\u003e[!NOTE]\n\u003e - Ensure you have Conda or Python installed on your system.\n\u003e - If you encounter any issues while creating the environment, check that all packages listed in the files are available in your Conda channels or PyPI.\n\u003e - It is recommended to regularly update your environment files as you add new packages to ensure reproducibility for other users.\n\u003e - By following these instructions, you will be able to set up the development environment required for this project effortlessly. If you have any questions or need further assistance, feel free to open an issue in this repository.\n\n## Contributing\n\u003e  :handshake: Feel free to open an issue in this repository if you encounter any issues with any of the notebook code and also if you want to add more programs to the repository then feel free to generate a pull request to this repository. Whole Community will appreciate your feedback and contributions. Thank you \n\n![jerry and duck handshake](https://tenor.com/en-GB/view/tom-and-jerry-jerry-the-mouse-jerry-shake-hands-handshake-gif-17827738.gif)\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](/License) file for details.\n   ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftavneetsingh01%2Fdata-science-and-analytics-lab-practicals","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftavneetsingh01%2Fdata-science-and-analytics-lab-practicals","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftavneetsingh01%2Fdata-science-and-analytics-lab-practicals/lists"}