This repository contains a Jupyter Notebook file (life_expectancy_linear_regression.ipynb) that demonstrates a simple linear regression model for predicting life expectancy based on certain features. Users can run this notebook to understand the process of preprocessing, loading, training, and evaluating a linear regression model using Python and popular data science libraries.
Before you begin, ensure you have the following installed on your local machine:
- Python (>=3.6)
- Jupyter Notebook
- Required Python libraries: NumPy, Pandas, Matplotlib, Scikit-Learn
- Clone the repository:
git clone [email protected]:Arjunpixel28/Linear-Regression.git cd life-expectancy-linear-regression
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Install dependencies:
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Run the Jupyter Notebook:
This will open the Jupyter Notebook in your browser. You can run each cell in the notebook to understand the step-by-step process of preprocessing the data, loading the dataset, training the linear regression model, and evaluating its performance.
- 1. Data Preprocessing: Exploring and cleaning the dataset, handling missing values, and feature engineering.
- 2. Data Loading: Loading the preprocessed data into the notebook.
- 3. Model Training: Splitting the data into training and testing sets, training the linear regression model using Scikit-Learn.
- 4. Model Evaluation: Evaluating the trained model's performance using appropriate metrics.
- 5. Conclusion: Summary of the findings and potential areas of improvement.
The dataset used in this project contains information about various factors that might influence life expectancy, such as GDP, healthcare expenditure, education, etc. The dataset is available in the data folder.
If you find any issues or have suggestions for improvements, feel free to create an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.