Unemployment Analysis and Prediction Project

Overview

This project focuses on analyzing unemployment trends and predicting unemployment rates using machine learning models. The dataset includes various features such as region, area (urban/rural), estimated employment, and labor participation rates.

Key objectives:

• Analyze trends in unemployment rates over time.
• Explore regional and area-wise unemployment disparities.
• Build and evaluate machine learning models to predict unemployment rates.

---

Dataset

The dataset contains the following key columns:

• Date: The date of record.
• Region: Geographical region.
• Area: Urban or rural classification.
• Estimated Unemployment Rate (%): The target variable.
• Estimated Employed: Estimated number of employed individuals.
• Estimated Labour Participation Rate (%): Labor participation rate.

---

Steps Performed

1. Data Cleaning and Preprocessing:

   • Removed missing values and stripped whitespaces from column names and values.
   • Converted the Date column to datetime format.
   • Encoded categorical variables (Region and Area) using one-hot encoding.

2. Data Visualization:

   • Trend analysis of unemployment rates over time.
   • Comparison of average unemployment rates across regions.
   • Rural vs. urban unemployment rate analysis.
   • Correlation heatmap for key variables.

3. Machine Learning Pipeline:

   • Data was split into training and testing sets.
   • Models evaluated: Random Forest, Linear Regression, Support Vector Regressor.
   • Random Forest Regressor was selected as the best-performing model based on R² score.

4. Model Evaluation:

   • Performance metrics: RMSE and R².
   • Visualized actual vs. predicted values.

5. Model Deployment:

   • Saved the trained model as a .pkl file for future use.

---

Key Findings

• Unemployment rates vary significantly across regions and between urban and rural areas.
• There is a notable correlation between labor participation rates and unemployment.

---

Usage

Prerequisites

• Python 3.x
• Required libraries: pandas, numpy, matplotlib, seaborn, sklearn, pickle

Run the Project

1. Clone this repository:

   git clone <repository-url>
   cd unemployment-analysis

2. Install the required dependencies:

   pip install -r requirements.txt