Live Application

Data Automation with Imama Kainat:)

https://data-analysis-automation-imamakainat.streamlit.app/

"Data with Imama Kainat" is a comprehensive Streamlit application designed to assist users in data exploration, visualization, preprocessing, and machine learning tasks. This app provides an interactive platform for users to analyze their datasets effectively.

Features

Home

• Introduction to the app and its functionalities.

Exploratory Data Analysis (EDA)

• Upload a dataset and perform EDA tasks:
  • View the first few rows of the dataset.
  • Handle missing values.
  • View dataset shape.
  • View column names.
  • View summary statistics.
  • View selected columns.
  • View value counts for a selected column.
  • Plot a correlation matrix.

Data Visualization

• Upload a dataset and create various plots to visualize the data:
  • View value counts as a bar plot.
  • Create customizable plots (area, bar, line, scatter) based on selected columns.

Data Preprocessing

• Upload a dataset for preprocessing tasks:
  • Handle missing values.
  • Encode categorical data.
  • Scale numerical data.
  • Download the preprocessed dataset.

Machine Learning

• Upload a dataset for clustering analysis:
  • Perform label encoding and PCA for dimensionality reduction.
  • Apply KMeans clustering.
  • Visualize clusters.
  • View cluster details.
  • Download the clustering results.

How to Use

1. Clone the Repository

   git clone https://github.com/yourusername/data-with-imama-kainat.git
   cd data-with-imama-kainat

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Application

   streamlit run app.py

How to Contribute

1. Fork the Repository

   • Go to the GitHub page of the repository and click on the "Fork" button in the top right corner.

2. Clone Your Fork

   git clone https://github.com/yourusername/data-with-imama-kainat.git
   cd data-with-imama-kainat

3. Create a Branch

   git checkout -b feature-name

4. Make Changes and Commit

   • Make the necessary changes to the codebase.

   git add .
   git commit -m "Description of the changes"

5. Push to Your Fork

   git push origin feature-name

6. Create a Pull Request

   • Go to the original repository on GitHub and click on the "New Pull Request" button.
   • Select the branch you created with the changes and submit the pull request for review.

Custom CSS

The application includes custom CSS for styling various elements of the Streamlit interface, including the sidebar, main content, header, buttons, and background.