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https://github.com/kelvintechnical/linear-regression-model


https://github.com/kelvintechnical/linear-regression-model

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README 

        



Linear Regression Model: Predicting Test Scores Based on Study Hours



Project Overview


This project demonstrates the application of a Linear Regression Model to predict test scores based on the hours studied. Linear regression is one of the most fundamental machine learning algorithms, and building this project offers a solid introduction to machine learning. In this project, we use Python libraries like NumPy, Matplotlib, and scikit-learn to build, train, and visualize our model.



What I Learned


In building this project, I learned the following:


    

      
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    Data Manipulation: How to use NumPy arrays to structure data for machine learning models.
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    Model Training: How to apply scikit-learn’s LinearRegression to train a model with simple, small datasets.
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    Data Visualization: How to use Matplotlib to plot both the original data and the regression line, helping to visualize the relationship between study hours and test scores.
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    Model Prediction: How to use a trained model to make predictions, such as predicting a test score based on new input hours.
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Why This Project is Important


Understanding and implementing linear regression is essential for machine learning and data science beginners. It serves as a stepping stone to more complex machine learning algorithms and concepts. Linear regression also provides insight into how data points relate to each other, allowing us to make informed predictions.



Why This Project is Great for a Machine Learning Portfolio


Linear regression is widely used in various industries for predictive analysis. By showcasing this project, I can demonstrate my knowledge of machine learning basics and my ability to apply that knowledge to real-world data. This project is a fantastic foundation for more advanced machine learning projects and techniques.



Code Walkthrough



# Importing necessary libraries

import numpy as np                     # For handling arrays and performing math operations

import matplotlib.pyplot as plt         # For plotting graphs

from sklearn.linear_model import LinearRegression  # For creating the linear regression model



# Data Preparation

X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)  # Independent variable: hours studied

y = np.array([2, 4, 5, 4, 5])                 # Dependent variable: test scores



# Model Initialization and Training

model = LinearRegression()              # Creating the model

model.fit(X, y)                         # Training the model with the data



# Making Predictions

predicted_score = model.predict([[6]])  # Predicting the score for 6 hours of study

print("Predicted score for 6 hours of study:", predicted_score[0])



# Data Visualization

plt.scatter(X, y, color='blue', label='Original Data')    # Plotting original data points

plt.plot(X, model.predict(X), color='red', label='Regression Line')  # Plotting the regression line

plt.xlabel("Hours Studied")                               # X-axis label

plt.ylabel("Test Score")                                  # Y-axis label

plt.title("Hours Studied vs. Test Score")                 # Plot title

plt.legend()                                              # Showing the legend

plt.show()                                                # Display the plot




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