https://github.com/tavneetsingh01/data-science-and-analytics-lab-practicals
This Repository contains the lab Programs for On-Going Data Science and Analytics Lab (CSL DC205) Fall 2024
https://github.com/tavneetsingh01/data-science-and-analytics-lab-practicals
conda data-science jupyter-notebook linear-regression logistic-regression matplotlib-pyplot naive-bayes-classifier nlp pandas polars python scikit-learn seaborn sentiment-analysis
Last synced: 6 months ago
JSON representation
This Repository contains the lab Programs for On-Going Data Science and Analytics Lab (CSL DC205) Fall 2024
- Host: GitHub
- URL: https://github.com/tavneetsingh01/data-science-and-analytics-lab-practicals
- Owner: Tavneetsingh01
- License: mit
- Created: 2024-10-20T10:19:18.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-08T15:32:21.000Z (7 months ago)
- Last Synced: 2024-12-08T16:27:16.914Z (7 months ago)
- Topics: conda, data-science, jupyter-notebook, linear-regression, logistic-regression, matplotlib-pyplot, naive-bayes-classifier, nlp, pandas, polars, python, scikit-learn, seaborn, sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 8.01 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: License
Awesome Lists containing this project
README
# This Repository contains the lab Programs for On-Going Data Science and Analytics Lab (CSL DC205) Fall 2024
## The following are the list of programs with their Jupyter-Notebook and Lecture notes (*Will be updated each week*)
Program
Jupyter Notebook
Lecture Notes
Q1. Write a Program to Explore various Data Manipulation Functions provided by Pandas and Visualize the Data Using Seaborn
Pandas Library Functions & Visualization Notebook
Currently Under Development
Q2. To predict if a person will purchase a product on a specific combination of Day, Discount and Free delivery using Naïve Bayesian Classifier.
Naive Bayes Classifer Notebook
Naive Bayes Classifier Lecture Notes
Q3. Predict Employee Salary based on Year of Experience using Linear Regression.
Linear Regression Notebook
Currently Under Development
Q4. Predict if a person will buy an SUV based on their Age and Estimated Salary using Logistic Regression.
Logistic Regression Notebook
Currently Under Development
Q5. Does Kyphosis exist after surgery using Decision Tree?
Decision Tree Notebook
Currently Under Development
Q6. Write a Program to Demonstrate Random Forest Algorithm
Random Forest Notebook
Currently Under Development
Q7. Predict if a person will buy a SUV based on Age and Estimated Salary using KNN?
KNN Notebook
Currently Under Development
Q8. Features Extraction from Text using Word Vectorization for Text Semantics?
1. Feature Extraction and Evalutaion Notebook Using SST2 Dataset
2. Feature Extraction and Evalutaion Notebook Using Basic Example
Currently Under Development
Q9. Sentiment Analysis from online news website using simple natural language processing.
Sentiement Analysis Notebook
Currently Under Development
Q10. Use of KmeansClustering algorithm for classifying persons into 5 categories according to their salary.
K-Means Clustering Notebook
Currently Under Development
Q11. Write a program for demonstrating (Support Vector Machine Classifier) SVM algorithm.
SVM Notebook
Currently Under Development
## Environment Setup Guide
This 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.
>[!NOTE]
> 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)
## Table of Contents
- [Using `required-env-packages-list.yml`](#using-required-env-packages-listyml)
- [Using `required-packages-list.txt`](#using-required-packages-listtxt)
- [Using `pip`](#using-pip)
## Using [`required-env-packages-list.yml`](/required-env-packages-list.yml)
To create a Conda environment from a YAML file, follow these steps:
1. **Open your terminal** (Anaconda Prompt or command line).
2. **Navigate to your project directory** where the `required-env-packages-list.yml` file is located:
```bash
cd path/to/your/project
```
3. Create the environment by running the following command:
```bash
conda env create -f required-env-packages-list.yml
```
4. Activate the environment with:
```bash
conda activate
```
Replace with the name specified in the YAML file.
## Using [`required-packages-list.txt`](/required-packages-list.txt)
To create a Conda environment from a text file, follow these steps:
1. Open your terminal (Anaconda Prompt or command line).
2. Navigate to your project directory where the [`required-packages-list.txt`](/required-packages-list.txt) file is located:
```bash
cd path/to/your/project
```
3. Create a new environment (replace with your desired name):
```bash
conda create --name --file required-packages-list.txt
```
4. Activate the environment with:
```bash
conda activate
```
## Using pip
If you prefer to use pip to create a virtual environment, follow these steps:
1. Open your terminal.
2. Navigate to your project directory where the [`required-packages-list.txt`](/required-packages-list.txt) file is located:
```bash
cd path/to/your/project
```
3. Create a virtual environment (replace with your desired name):
```bash
python -m venv
```
4. Activate the virtual environment:
1. On Windows:
- In `PowerShell` :
```powershell
\Scripts\Activate.ps1
```
- In `cmd` :
```cmd
\Scripts\activate.bat
```
2. On macOS/Linux:
```bash
source /bin/activate
```
5. Install the required packages using:
```bash
pip install -r required-packages-list.txt
```
>[!NOTE]
> - Ensure you have Conda or Python installed on your system.
> - 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.
> - It is recommended to regularly update your environment files as you add new packages to ensure reproducibility for other users.
> - 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.
## Contributing
> :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
## License
This project is licensed under the MIT License. See the [LICENSE](/License) file for details.