HOW TO TRAIN A MODEL STEP BY STEP IN PYTHON

 

HOW TO TRAIN A MODEL STEP BY STEP IN PYTHON:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error, r2_score

data = {

'Hours': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

'Scores': [10, 20, 30, 40, 50, 60, 70, 80, 85, 95]

}

df = pd.DataFrame(data)

print(df.head())

   Hours  Scores
0      1      10
1      2      20
2      3      30
3      4      40
4      5      50




plt.scatter(df['Hours'], df['Scores'])

plt.xlabel('Hours Studied')

plt.ylabel('Scores')

plt.title('Hours vs Scores')

plt.show()





X = df[['Hours']]  # Independent variable

y = df['Scores']   # Dependent variable

#Now split into training and testing sets:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


model = LinearRegression()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print(y_pred)

[88.01724138 20.73275862]
print("Mean Squared Error:", mean_squared_error(y_test, y_pred))

print("R2 Score:", r2_score(y_test, y_pred))
Mean Squared Error: 4.820340368608807
R2 Score: 0.9954363641480627
y_pred = model.predict(X_test)

print(y_pred)


[88.01724138 20.73275862]

print("Mean Squared Error:", mean_squared_error(y_test, y_pred))

print("R2 Score:", r2_score(y_test, y_pred))

Mean Squared Error: 4.820340368608807
R2 Score: 0.9954363641480627


plt.scatter(X, y, color='blue')

plt.plot(X, model.predict(X), color='red')

plt.xlabel('Hours Studied')

plt.ylabel('Scores')

plt.title('Linear Regression - Hours vs Scores')

plt.show()




hours = np.array([[7.5]])

predicted_score = model.predict(hours)

print(f"Predicted Score for 7.5 hours: {predicted_score[0]}")
Predicted Score for 7.5 hours: 73.59913793103448