https://github.com/professorlearncode/linear-regression-model
This code implements a linear regression model to predict diabetes progression based on the diabetes dataset. The model is trained and evaluated, with results visualized through scatter and residual plots.
https://github.com/professorlearncode/linear-regression-model
Last synced: about 1 year ago
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This code implements a linear regression model to predict diabetes progression based on the diabetes dataset. The model is trained and evaluated, with results visualized through scatter and residual plots.
- Host: GitHub
- URL: https://github.com/professorlearncode/linear-regression-model
- Owner: ProfessorlearnCode
- Created: 2024-08-18T00:16:58.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-18T00:18:58.000Z (almost 2 years ago)
- Last Synced: 2024-12-25T13:40:40.272Z (over 1 year ago)
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### Documentation for Linear Regression Model on Diabetes Dataset
#### Overview
This repository contains a Python implementation of a linear regression model used to predict diabetes progression based on a set of medical features. The model is trained on the diabetes dataset from the `sklearn` library and evaluated using various metrics. Visualizations are included to help assess the model's performance.
#### Prerequisites
Before running the code, ensure you have the following Python libraries installed:
- `numpy`
- `matplotlib`
- `pandas`
- `seaborn`
- `scikit-learn`
You can install the necessary libraries using the following command:
```bash
pip install numpy matplotlib pandas seaborn scikit-learn
```
#### Code Breakdown
1. **Importing Libraries**
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
```
This section imports all the necessary libraries for data handling, visualization, and model implementation.
2. **Loading the Dataset**
```python
diabetes = datasets.load_diabetes()
```
The diabetes dataset is loaded from the `sklearn` library. This dataset includes 10 baseline variables (age, sex, BMI, etc.) used to predict the progression of diabetes one year after baseline.
3. **Splitting the Data**
```python
X = diabetes.data
Y = diabetes.target
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
```
The dataset is split into features (`X`) and target (`Y`). The data is further divided into training (80%) and testing (20%) sets using `train_test_split`.
4. **Model Initialization and Training**
```python
model = LinearRegression()
model.fit(X_train, Y_train)
```
A linear regression model is initialized and trained on the training data.
5. **Making Predictions**
```python
Y_prediction = model.predict(X_test)
```
The model makes predictions on the test data.
6. **Model Evaluation**
```python
mse = mean_squared_error(Y_test, Y_prediction)
r2 = r2_score(Y_test, Y_prediction)
print("Coefficients:", model.coef_)
print("Intercept: ", model.intercept_)
print("Mean Square Error: %.2f" % mse)
print("R² Score: %.2f" % r2)
```
The model's performance is evaluated using Mean Square Error (MSE) and R² Score. The model's coefficients and intercept are also printed.
7. **Visualizing Results**
- **Actual vs Predicted Values**
```python
sns.scatterplot(x=Y_test, y=Y_prediction, alpha=0.7)
plt.xlabel('Actual Values')
plt.ylabel('Predicted Values')
plt.title('Actual vs Predicted Values')
plt.show()
```
A scatter plot is created to visualize the relationship between actual and predicted values.
- **Residual Plot**
```python
residuals = Y_test - Y_prediction
sns.scatterplot(x=Y_prediction, y=residuals, alpha=0.7)
plt.xlabel('Predicted Values')
plt.ylabel('Residuals')
plt.title('Residual Plot')
plt.axhline(0, color='red', linestyle='--')
plt.show()
```
A residual plot is used to check for any patterns in the residuals, which can indicate model bias.
#### Conclusion
This project demonstrates the implementation of a linear regression model to predict diabetes progression. The code includes steps for data preparation, model training, evaluation, and visualization, providing a comprehensive approach to understanding the model's performance.
#### Future Improvements
- **Feature Engineering**: Explore additional feature engineering techniques to improve model accuracy.
- **Advanced Models**: Experiment with more complex models like Ridge or Lasso regression for potentially better performance.
#### License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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