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DataProject/1d.py

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import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
# references: https://towardsdatascience.com/linear-regression-in-6-lines-of-python-5e1d0cd05b8d, https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
# Read data
df_data = pd.read_csv('Trips_Full_Data.csv', dtype={'Trips 1-25 Miles': 'float64',
'Trips 1-3 Miles': 'float64',
'Trips 10-25 Miles': 'float64',
'Trips 100-250 Miles': 'float64',
'Trips 25-50 Miles': 'float64',
'Trips 250-500 Miles': 'float64',
'Trips 3-5 Miles': 'float64',
'Trips 5-10 Miles': 'float64',
'Trips 50-100 Miles': 'float64',
'Trips <1 Mile': 'float64',
'Trips 500+ Miles': 'float64',
'Population Not Staying at Home': 'float64',
'Population Staying at Home': 'float64',
'Week': 'float64'})
df_distance = pd.read_csv('Trips_by_Distance.csv', dtype={'County Name': 'object',
'Number of Trips': 'float64',
'Number of Trips 1-3': 'float64',
'Number of Trips 10-25': 'float64',
'Number of Trips 100-250': 'float64',
'Number of Trips 25-50': 'float64',
'Number of Trips 250-500': 'float64',
'Number of Trips 3-5': 'float64',
'Number of Trips 5-10': 'float64',
'Number of Trips 50-100': 'float64',
'Number of Trips <1': 'float64',
'Number of Trips >=500': 'float64',
'Population Not Staying at Home': 'float64',
'Population Staying at Home': 'float64',
'Week': 'float64'})
# Data cleaning
df_data.dropna(inplace=True)
df_data.fillna(0, inplace=True)
# People vs Trips
Trips = ['Trips 1-25 Miles', 'Trips 1-3 Miles', 'Trips 10-25 Miles', 'Trips 100-250 Miles',
'Trips 25-50 Miles', 'Trips 250-500 Miles', 'Trips 3-5 Miles', 'Trips 5-10 Miles',
'Trips 50-100 Miles', 'Trips <1 Mile', 'Trips 500+ Miles']
# Group and sum
peoplevtrips = df_data[Trips].sum()
print("People v trips", peoplevtrips)
# People vs Distance
Trips = ['Number of Trips <1', 'Number of Trips 1-3', 'Number of Trips 3-5', 'Number of Trips 5-10',
'Number of Trips 10-25', 'Number of Trips 25-50', 'Number of Trips 50-100', 'Number of Trips 100-250',
'Number of Trips 250-500', 'Number of Trips >=500']
# Group and sum
peoplevdistance = df_distance[Trips].sum()
print("People vs Distance:", peoplevdistance)
# Reshape data for scikit-learn
X = peoplevtrips[:-1].values.reshape(-1, 1) # I need to exclude the last element because peoplevtrips have 11 entries and the other has 10
y = peoplevdistance.values
# Linear model
model = LinearRegression()
model.fit(X, y)
print("Linear model:")
r_sq = model.score(X, y)
print("Coefficient of determination (R^2):", r_sq)
print("Intercept:", model.intercept_)
print("Coefficient(slope):", model.coef_)
y_pred = model.predict(X)
print("Predicted response:\n", y_pred)
# Results:
# Linear model:
# ('Coefficient of determination (R^2):', 0.2815917064454585)
# ('Intercept:', 146845315292.5589)
# ('Coefficient(slope):', array([63.90150459]))
# ('Predicted response:\n', array([6.01113971e+11, 3.12116115e+11, 2.50209167e+11, 1.49909451e+11,
# 1.77780923e+11, 1.47663555e+11, 2.28057152e+11, 2.51267483e+11,
# 1.55289788e+11, 2.92657976e+11]))
print("Training linear model")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
y_pred = linear_model.predict(X_test)
r_squared = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
print("Trained linear model R-squared value (R^2):", r_squared)
print("Mean Squared Error (MSE):", mse)
# Results
# Training linear model
# ('Trained linear model R-squared value (R^2):', 0.24003353124357718)
# ('Mean Squared Error (MSE):', 7.723222074854258e+22)
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)
poly_model = LinearRegression()
poly_model.fit(X_poly, y)
print("Polynomial model:")
y_pred = poly_model.predict(X_poly)
print("Predicted response:\n", y_pred)
print("Intercept:", poly_model.intercept_)
print("Coefficients:", poly_model.coef_)
r_sq = poly_model.score(X_poly, y)
print("Coefficient of determination (R^2):", r_sq)
# Results:
# Polynomial model:
# ('Predicted response:\n', array([6.48303516e+11, 2.62015333e+11, 2.21370704e+11, 1.87051715e+11,
# 1.92675848e+11, 1.86729617e+11, 2.10435900e+11, 2.21940724e+11,
# 1.87902869e+11, 2.47639354e+11]))
# ('Intercept:', 186617127217.98172)
# ('Coefficients:', array([0.00000000e+00, 8.68369156e+00, 7.91421328e-09]))
# ('Coefficient of determination (R^2):', 0.30388237649541094)
# Model training polynomial
print("Training polynomial model")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
poly = PolynomialFeatures(degree=2)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.transform(X_test)
# Initialize polynomial regression model
poly_model = LinearRegression()
poly_model.fit(X_train_poly, y_train)
y_pred = poly_model.predict(X_test_poly)
# Evaluate the model
r_squared = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
print("Trained model R value (R^2):", r_squared)
print("Mean squared error:", mse)
#Results
# Training polynomial model
# ('Trained model R value (R^2):', -1.0532504215161564)
# ('Mean squared error:', 2.086632717704806e+23)