This project aims to analyze the unemployment trends in India using a dataset containing unemployment statistics. The analysis includes data preprocessing, exploratory data analysis (EDA), and applying machine learning models to predict unemployment rates. The project leverages libraries such as pandas, seaborn, matplotlib, and scikit-learn for data handling, visualization, and model building.
Unemployment is a critical economic indicator, and understanding its trends is vital for policymakers and economists. This project delves into the unemployment data of India to uncover patterns, trends, and potential predictive insights.
The dataset used in this analysis is titled "Unemployment in India" and contains information on unemployment rates across various states and regions in India.
The analysis is structured as follows:
- Data Loading: Importing the dataset.
- Data Preprocessing: Handling missing values, data cleaning, and feature engineering.
- Exploratory Data Analysis (EDA): Visualizing data trends and patterns using seaborn and matplotlib.
- Feature Selection: Identifying significant features for modeling.
Linear Regression - machine learning models is applied to predict unemployment rates. The models is evaluated based on metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
The results of the analysis and modeling provide insights into the factors influencing unemployment rates in India and the performance of different predictive models.
Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.
This project is licensed under the MIT License. See the LICENSE file for more details.