swethac01/ct_project

CT_Project

Industrial Human Resource Geo-Visualization

Introduction

The "Industrial Human Resource Geo-Visualization" project aims to address the pressing need for updated and accurate data on the industrial classification of the workforce in India. Understanding the distribution of labor across various sectors is crucial for economic planning and policy-making. Traditionally, this classification has included main and marginal workers, excluding cultivators and agricultural laborers, and has been divided by sex, section, division, and class. However, the existing data is outdated and may not represent the current state of the workforce accurately.

This project seeks to update this information by employing modern data analysis techniques, including Exploratory Data Analysis (EDA), Visualization, and Natural Language Processing (NLP). By merging and analyzing multiple datasets, the project will provide a comprehensive overview of the workforce distribution across different industries. The final deliverable will be a Streamlit dashboard app, visualizing the population of workers across various industries and geographies, and a detailed PowerPoint presentation summarizing the findings and insights.

The insights gained from this updated classification will be invaluable for policy makers and planners in making informed decisions regarding employment and economic strategies, ensuring they reflect the current industrial landscape of the country.

Objective

1. The primary objective of the "Industrial Human Resource Geo-Visualization" project is to update and provide an accurate classification of the industrial workforce in India. This involves:

Updating Workforce Data:

Collecting and merging existing datasets to reflect the current distribution of main and marginal workers, excluding cultivators and agricultural laborers, categorized by sex, section, division, and class.

Data Analysis:

Performing comprehensive Exploratory Data Analysis (EDA) and applying Natural Language Processing (NLP) techniques to group and analyze core industries and business categories such as Retail, Poultry, Agriculture, Manufacturing, etc.

Visualization:

Developing an interactive Streamlit dashboard using Plotly to visualize the population of workers across various industries and geographies, providing clear and actionable insights.

Presentation of Findings:

Creating a detailed PowerPoint presentation summarizing the problem statement, tools used, methodologies, EDA insights, and overall findings to effectively communicate the results of the study

Technology used

1. Exploratory Data Analysis (EDA)

2. Visualization

3. Natural Language Processing (NLP)

Overview

The "Industrial Human Resource Geo-Visualization" project is designed to update the industrial classification of the workforce in India, providing a modern and accurate reflection of the labor force distribution across various sectors. By analyzing state-wise data on main and marginal workers (excluding cultivators and agricultural laborers) categorized by sex, section, division, and class, the project aims to provide valuable insights for policy makers and planners.

The project involves several key steps:

Data Collection and Merging:

Aggregating multiple CSV files into a single, comprehensive dataframe.

Data Preprocessing and Exploration:

Cleaning the data and performing exploratory data analysis (EDA) to uncover patterns and trends.

Natural Language Processing (NLP):

Analyzing and grouping core industries and business categories using NLP techniques.

Visualization:

Developing a Streamlit dashboard with Plotly to visually represent the workforce distribution across different industries and geographies.

Presentation:

Summarizing findings in a PowerPoint presentation to effectively communicate the results and insights.

Packages

The following Python packages are essential for the successful completion of this project:

1. Pandas: For data manipulation and analysis.
2. NumPy: For numerical operations and array processing.
3. Matplotlib: For creating static, interactive, and animated visualizations.
4. Seaborn: For statistical data visualization.
5. Plotly: For creating interactive plots and graphs, integrated into the Streamlit dashboard.
6. Streamlit: For building the interactive dashboard application.
7. NLTK: For Natural Language Processing tasks. Scikit-learn: For machine learning algorithms and data preprocessing.
8. OS: For interacting with the operating system to manage files and directories.