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

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:

   git clone https://github.com/Himel-Sarder/ML-Exercise-Income-Dataset-Regression-Metrics.git

2. Navigate to the project directory:

   cd ML-Exercise-Income-Dataset-Regression-Metrics

3. Install the required libraries:

   pip install pandas numpy matplotlib scikit-learn

Usage

1. Load and Explore the Dataset

Load the dataset using pandas and display its structure:

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:

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:

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:

from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)

5. Model Evaluation

Evaluate the model using various metrics:

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:

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.

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Happy coding! 😺