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linear regression python

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Innocent Iguana answered on May 14, 2021 Popularity 10/10 Helpfulness 6/10

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linear regression python

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from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression 
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score, KFold
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Construct model
lm=LinearRegression()
# Simple linear regression uses 1 column with eqn: y = mx + c
# Multiple linear regression uses multiple columns with eqn: z = mx + ny +c
# Fit the model
lm.fit(X_train, y_train)
# Predicted estimation
y_pred = lm.predict(X_test)
# This is intercept of the line (Also known as bias co-efficient)
intercept = lm.intercept_
# This is slope (m) of the line y=mx+c (Also known as relevant variable's co-efficient)
slope = lm.coef_
# Percentage of target values explained by the features
rsquared = lm.score(X_test, y_test)
# RMSE : Average error in prediction
mean_squared_error(y_test, y_pred, squared=False)
# Prediction of specific range
new_x = np.arange(1,101,1).reshape(-1,1) # Or you can make it dataframe
new_pred_y = lm.predict(new_x)

# Do k-fold cross validation
kf = KFold(n_splits=6, shuffle=True, random_state=42)
cv_results = cross_val_score(lm, X_train, y_train, cv=kf)
# Mean, std and confidence interval of the cross-validation
print(np.mean(cv_results), np.std(cv_results), np.quantile(cv_results, [0.025, 0.975]))

# Visualize Feature importance
names = df.drop("target", axis=1).columns
importance = lm.fit(X, y).coef_
plt.bar(names, importance)
plt.xticks(rotation=45)
plt.show()

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Contributed on Jan 29 2023

Innocent Iguana

0 Answers Avg Quality 2/10

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import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('tips')
sb.regplot(x = "total_bill", y = "tip", data = df)
plt.show()

Popularity 10/10 Helpfulness 3/10 Language python

Source: www.tutorialspoint.com

Tags: linear-regression linear-regressio

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Contributed on May 14 2021

Nasty Nightingale

0 Answers Avg Quality 2/10

linear regression python

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# You want a regression line : y = mx + c
from statsmodels.formula.api import ols
# Formula applied : y = x, finds m and c on its own for best fit
num_model = ols("y ~ x_num", data=df).fit()
# Calculate co-efficient of each category with relative to 0 instead of relative to intercept, its the mean for each category
cat_model = ols("y ~ x_cat + 0", data=df).fit()
# See model params : slope m and intercept c
print(cat_model.params)
# See prediction on original fitted data 
print(num_model.fittedvalues)
# See residuals
print(model.resid)
# Model summary
model.summary()
# R-squared
print(model.rsquared)
# Residual mean squared error
rse = np.sqrt(model.mse_resid)
# Create test data
test_data = pd.DataFrame({"x_num": np.arange(20, 41)})
# Predict on test data
print(num_model.predict(test_data))
# Summary values
summary_df = model.get_influence().summary_frame()
# Leverage
df["leverage"] = summary_df["hat_diag"]
# Cooks distance
summary_df["cooks_dist"] = summary_df["cooks_d"]
# Residual plot
sns.residplot(x="X", y="y", data=bream, lowess=True)
# QQ plot
from statsmodels.api import qqplot
qqplot(data=model.resid, fit=True, line="45")
# Scale location plot
residual_abs_squared = model.get_influence().resid_studentized_internal
residual_measured = np.sqrt(residual_abs_squared)
sns.regplot(x=model.fittedvalues, y=residual_measured, ci=None, lowess=True)
# NOTE : You can transform the X and y before fitting and then train. You can predict with the model and the predicted values should be back-transformed for y if y was transformed.

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Tags: linear-regression linear-regressio

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Contributed on Jan 17 2023

Innocent Iguana

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python linear regression

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-1

>>> from scipy import stats
>>> import numpy as np
>>> x = np.random.random(10)
>>> y = np.random.random(10)
>>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

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Source: docs.scipy.org

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Contributed on May 18 2021

HotFlow

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linear regression python code

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import numpy as np
import matplotlib.pyplot as plt
 
def estimate_coef(x, y):
    # number of observations/points
    n = np.size(x)
 
    # mean of x and y vector
    m_x = np.mean(x)
    m_y = np.mean(y)
 
    # calculating cross-deviation and deviation about x
    SS_xy = np.sum(y*x) - n*m_y*m_x
    SS_xx = np.sum(x*x) - n*m_x*m_x
 
    # calculating regression coefficients
    b_1 = SS_xy / SS_xx
    b_0 = m_y - b_1*m_x
 
    return (b_0, b_1)
 
def plot_regression_line(x, y, b):
    # plotting the actual points as scatter plot
    plt.scatter(x, y, color = "m",
               marker = "o", s = 30)
 
    # predicted response vector
    y_pred = b[0] + b[1]*x
 
    # plotting the regression line
    plt.plot(x, y_pred, color = "g")
 
    # putting labels
    plt.xlabel('x')
    plt.ylabel('y')
 
    # function to show plot
    plt.show()
 
def main():
    # observations / data
    x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12])
 
    # estimating coefficients
    b = estimate_coef(x, y)
    print("Estimated coefficients:\nb_0 = {}  \
          \nb_1 = {}".format(b[0], b[1]))
 
    # plotting regression line
    plot_regression_line(x, y, b)
 
if __name__ == "__main__":
    main()

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Source: www.geeksforgeeks.org

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Contributed on Feb 03 2022

Comfortable Cat

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