ml.py

# Importing all required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle

# Reading data
df=pd.read_csv("student_scores.csv")
x=df.drop("Scores",axis=1)
y=df["Scores"]

# find relationship between the data using scatter plot
def plot():
    plt.scatter(x,y)
    plt.xlabel("Hours Studied")
    plt.ylabel("Percentage Score")
    plt.title("Hours studied Vs Scores")
    plt.savefig('static/scatter_plot.png')
# plot()

# splitting data into training and testing sets using Scikit-Learn's built-in train_test_split() method
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=0)

# training the model
def train_model():

    from sklearn.linear_model import LinearRegression
    model=LinearRegression()
    model.fit(x_train,y_train)

    import pickle
    with open('model_lr.pkl',"wb") as file:
        pickle.dump(model, file)

# testing the model
def test_model():
    filename = 'model_lr.pkl'
    with open(filename, 'rb') as file:
        model = pickle.load(file)
    from sklearn.model_selection import cross_val_score
    y_pred=model.predict(x_test)
    scores=cross_val_score(model,x_train,y_train,cv=5)
    print(f"accuracy={np.mean(scores)*100:.2f}%")

    from sklearn import metrics
    print(f"Mean Absolute Error={metrics.mean_absolute_error(y_test,y_pred)}")

train_model()

# Plotting the regression line
def reg_line():
    filename = 'model_lr.pkl'
    with open(filename, 'rb') as file:
        model = pickle.load(file)
    total_pred=model.predict(x)
    plt.scatter(x,y)
    plt.plot(x,total_pred)
    plt.tight_layout()
    plt.title("Hours studied  Vs Percentage score with regression line")
    plt.savefig('static/regression1.png')
# reg_line()

# making prediction using the number of hours provided
def predicts(h):

    filename = 'model_lr.pkl'
    with open(filename, 'rb') as file:
        model = pickle.load(file)

    scores = model.predict([[h]])
    return scores[0]

if __name__ == '__main__':
    train_model()