https://github.com/himel-sarder/ml-exercise-regression-metrics
This repository contains an exercise on regression metrics using an income dataset to predict happiness. The exercise includes data preprocessing, model training, evaluation, and visualization.
https://github.com/himel-sarder/ml-exercise-regression-metrics
error-handling income-dataset machine-learning machine-learning-algorithms machinelearning ml ml-exercise ml-practice regression regression-metrics simple-linear-regression
Last synced: 3 months ago
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This repository contains an exercise on regression metrics using an income dataset to predict happiness. The exercise includes data preprocessing, model training, evaluation, and visualization.
- Host: GitHub
- URL: https://github.com/himel-sarder/ml-exercise-regression-metrics
- Owner: Himel-Sarder
- License: mit
- Created: 2024-12-28T16:55:27.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-12-30T20:04:42.000Z (9 months ago)
- Last Synced: 2025-05-24T00:11:03.031Z (5 months ago)
- Topics: error-handling, income-dataset, machine-learning, machine-learning-algorithms, machinelearning, ml, ml-exercise, ml-practice, regression, regression-metrics, simple-linear-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 1.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML-Exercise-Income-Dataset-Regression-Metrics
This repository contains an exercise on regression metrics using an income dataset to predict happiness. The exercise includes data preprocessing, model training, evaluation, and visualization.
## Overview
- **Coded by**: Himel Sarder
- **Contact**: info.himelcse@gmail.com
- **LinkedIn**: [Himel Sarder](https://www.linkedin.com/in/himel-sarder/)
## Files in the Repository
- `Exercise ~ Regression Metrics.ipynb`: Jupyter notebook containing the regression analysis.
- `LICENSE`: License information.
- `Mymodel.pkl`: Serialized model file.
- `README.md`: This README file.
- `Regression Metrics.ipynb`: Additional notebook for regression metrics.
- `income.csv`: Dataset containing income and happiness data.
## Dataset
The dataset `income.csv` contains the following columns:
- `Unnamed: 0`: Index column.
- `income`: Income values.
- `happiness`: Happiness scores.
## Getting Started
### Prerequisites
- Python 3.x
- Jupyter Notebook
- Required Python libraries:
- pandas
- numpy
- matplotlib
- scikit-learn
### Installation
1. Clone the repository:
```sh
git clone https://github.com/Himel-Sarder/ML-Exercise-Income-Dataset-Regression-Metrics.git
```
2. Navigate to the project directory:
```sh
cd ML-Exercise-Income-Dataset-Regression-Metrics
```
3. Install the required libraries:
```sh
pip install pandas numpy matplotlib scikit-learn
```
## Usage
### 1. Load and Explore the Dataset
Load the dataset using pandas and display its structure:
```python
import pandas as pd
df = pd.read_csv('income.csv')
print(df.head())
print(df.shape)
print(df.info())
```
### 2. Data Visualization
Visualize the relationship between income and happiness:
```python
import matplotlib.pyplot as plt
plt.scatter(df['income'], df['happiness'], c=df['happiness'], cmap='coolwarm')
plt.xlabel('Income')
plt.ylabel('Happiness')
plt.colorbar(label='Happiness')
plt.show()
```
### 3. Data Splitting
Split the data into training and test sets:
```python
from sklearn.model_selection import train_test_split
X = df.iloc[:, 1:2]
y = df.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)
```
### 4. Model Training
Train a Linear Regression model:
```python
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
```
### 5. Model Evaluation
Evaluate the model using various metrics:
```python
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
y_pred = lr.predict(X_test)
print("Mean Absolute Error:", mean_absolute_error(y_test, y_pred))
print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
print("R-squared:", r2_score(y_test, y_pred))
print("Root Mean Squared Error:", np.sqrt(mean_squared_error(y_test, y_pred)))
```
### 6. Save the Model
Save the trained model to a file:
```python
import pickle
pickle.dump(lr, open('Mymodel.pkl', 'wb'))
```
## Additional Experiment
Test the impact of adding random features and recalculating R² and adjusted R² scores.
## License
This project is licensed under the MIT License - see the `LICENSE` file for details.
## Acknowledgments
- Thank you to everyone who contributed to this project.
## Contact
If you have any questions or feedback, feel free to contact me at info.himelcse@gmail.com.
---
Happy coding! 😺