Data Analysis Web Application

This repository contains a web application built with Streamlit in Python, designed to perform and display data analysis in an interactive and user-friendly interface.

Features Of Data Analysis Web App

• Supports Multiple file formates(csv, txt, xls, xlsx, ods, odt).
• Gives a detailed overview of your data.
• You have the option to plot graphs for your data.
• You can see Outliers in your data in a visulization way.
• You can drop multiple columns and rename columns.
• You can drop multiple categorical Rows and Numerical Rows by giving multiple conditions.
• Handling Handling Missing Data, Merging On Index and Concatenating On Axis
• You can download/export your edited data.

Built With

• Python: The programming language used for development.
• Streamlit: Framework for creating web applications.
• Pandas: Library for data manipulation and analysis.
• Matplotlib/Seaborn: Libraries for visualization (if applicable).
• Scikit-Learn: For implementing machine learning models (if applicable).

Getting Started

Prerequisites

Ensure you have Python installed, preferably version 3.7 or above. You can download it from Python's official website.

Installation

1. Clone the repository: https://github.com/manishdevdi/Data-Analysis-Web-Application.git

2. Install the required packages: bash pip install -r requirements.txt

How to run the project?

1. Clone or download this repository to your local machine.
2. Install all the libraries mentioned in the requirements.txt file with the command pip3 install -r requirements.txt
3. Open your terminal/command prompt from your project directory and run the file app.py by executing the command streamlit run app.py.
4. You will be automatically redirected the your localhost in brower where you can see you WebApp in live.

5. Live Project : https://data-analysis-automating-web-application-by-manishdevdi.streamlit.app/

Usage

• Upload a Dataset: Use the upload button to add your own dataset (CSV format).
• Data Overview: The app provides an overview of the dataset with basic details like shape, missing values, and data types.
• Exploratory Data Analysis (EDA): Visualize data distribution, correlations, and relationships.
• Data Cleaning: Options for handling missing values and other basic cleaning operations.
• Machine Learning (Optional): If the dataset is compatible, apply predefined models to make predictions or classifications. Screenshots

License

Distributed under the MIT License. See LICENSE for more information.

Upcomming Features for the WebApp:

• More Supporting data Formates.
• Improving the UI of the WEB APP.
• More Detailed Analysis of The data.
• And many more...

Contact

Manish Devdi - GitHub Profile

Project Link: https://github.com/manishdevdi/Data-Analysis-Web-Application