Data Science: Practice Repository

Welcome to the repository for my Data Science Assignments! This repository contains all the assignments I have completed as part of my data science coursework. Each assignment is organized into its own folder, containing the relevant files, code, and documentation.

Repository Structure

The repository is organized by assignment, with each assignment in its own directory. The structure is as follows:

.
├── Assignment1/
│   ├── problem_statement.docx or .txt
│   ├── assignment1.ipynb
│   ├── data.csv or .xlsx
├── Assignment2/
│   ├── problem_statement.docx or .txt
│   ├── assignment2.ipynb
│   ├── data.csv or .xlsx
├── Assignment3/
│   ├── problem_statement.docx or .txt
│   ├── assignment2.ipynb
│   ├── data.csv or .xlsx
├── README.md
└── LICENSE

Assignments

Each assignment folder contains the following:

• problem_statement.docx: A brief description of the assignment, the tasks involved, and any specific instructions or notes.

• assignmentX.ipynb: The Jupyter Notebook file with the code and explanations for the assignment tasks.

• data.csv: Dataset for the assignment

Problem Statement Structure

• Tasks:

  1. Data Cleaning and Preprocessing

     • Loading and exploring the dataset.
     • Handling missing values.
     • Data normalization and transformation.

  2. Exploratory Data Analysis (EDA)

     • Performing descriptive statistics.
     • Visualizing data distributions and relationships.
     • Identifying patterns and outliers.

  3. Machine Learning Model Development

     • Splitting data into training and test sets.
     • Training and Evaluating different machine learning models.
     • Hyperparameter tuning and model selection

  ... ... ...

How to Use

1. Clone the repository to your local machine:

git clone https://github.com/Priyanshu501/Data_Science_Practice.git

2. Navigate to the assignment directory you are interested in:

cd Data_Science_Practice/`assignment`

3. Open the Jupyter Notebook for the assignment:

jupyter notebook `assignment.ipynb`

4. Follow the instructions in the notebook to run the code and review the results.

Contributing

This repository is intended for educational purposes, and contributions are not expected. However, if you have suggestions or find any issues, please feel free to open an issue or submit a pull request.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.