This repository contains two data science projects: Iris Flower Classification and Unemployment Analysis in India. The Iris classification project aims to classify iris flowers into species based on their physical characteristics, while This project aims to analyze unemployment trends in India. The analysis includes a comparison of unemployment rates before and after the lockdown, utilizing various data visualization techniques to provide insights into the impact of the lockdown on employment across different states.
The Iris dataset is a classic dataset in machine learning, containing information about three species of iris flowers (Setosa, Versicolor, and Virginica) with the following features:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
- Data Exploration: Visualizations to understand the data distribution and relationships between features.
- Model Training: Implementation of various classification algorithms (e.g., Logistic Regression, Decision Trees, SVM).
- Model Evaluation: Accuracy evaluation of different algorithm using graph for model performance assessment.
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
The unemployment analysis dataset provides detailed insights into unemployment rates across different regions in India. The dataset includes the following features:
- Region: The name of the state or union territory in India where the data was collected.
- Date: The date on which the unemployment data was recorded, indicating the specific time frame of the analysis.
- Frequency: The frequency of the data collection (e.g., monthly, quarterly, annually), which indicates how often the data is updated.
- Estimated Unemployment Rate (%): The percentage of the labor force that is unemployed but actively seeking employment within the specified region and time frame.
- Estimated Employed: The estimated number of individuals employed in the region, providing insight into the size of the workforce.
- Estimated Labour Participation Rate (%): The percentage of the working-age population that is either employed or actively looking for work, reflecting the overall engagement of individuals in the labor market.
- Longitude: The geographical coordinate representing the east-west position of the region, useful for mapping and spatial analysis.
- Latitude: The geographical coordinate representing the north-south position of the region, also useful for mapping and spatial analysis.
- Data Filtering: Analyzes unemployment data before and after the lockdown (months 1-4 and 4-7).
- Statistical Analysis: Computes mean unemployment rates by state and calculates percentage changes.
- Data Visualization: Includes various plots such as bar charts, box plots, and geospatial scatter plots to visually represent data.
- Interactive Visuals: Utilizes Plotly for interactive graphs, enabling users to explore data dynamically.
- Python
- Pandas
- NumPy
- Plotly Express
- Seaborn
- Jupyter Notebook