<reponame>NeelkanthMehta/ga-dsmp
# --------------
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

df = pd.read_csv(path)

print(df.iloc[:5,:5])

X = df.drop(labels='Price', axis=1)
y = df['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=6)

corr = X_train.corr()
print(corr)


# --------------
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

# Code starts here
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
r2 = r2_score(y_test, y_pred)


# --------------
from sklearn.linear_model import Lasso

# Code starts here
lasso = Lasso()
lasso.fit(X_train, y_train)
lasso_pred = lasso.predict(X_test)
r2_lasso = r2_score(y_test, lasso_pred)


# --------------
from sklearn.linear_model import Ridge

# Code starts here
ridge = Ridge()
ridge.fit(X_train, y_train)
ridge_pred = ridge.predict(X_test)
r2_ridge = r2_score(y_test, ridge_pred)

# Code ends here


# --------------
from sklearn.model_selection import cross_val_score

#Code starts here
regressor = LinearRegression()
score = cross_val_score(estimator=regressor, X=X_train, y=y_train, cv=10)
mean_score = score.mean()
print(mean_score)


# --------------
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

#Code starts here
model = make_pipeline(PolynomialFeatures(2), LinearRegression())

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

r2_poly = r2_score(y_test, y_pred)



# --------------
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

df = pd.read_csv(path)

print(df.iloc[:5,:5])

X = df.drop(labels='Price', axis=1)
y = df['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=6)

corr = X_train.corr()
print(corr)


# --------------
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

# Code starts here
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
r2 = r2_score(y_test, y_pred)


# --------------
from sklearn.linear_model import Lasso

# Code starts here
lasso = Lasso()
lasso.fit(X_train, y_train)
lasso_pred = lasso.predict(X_test)
r2_lasso = r2_score(y_test, lasso_pred)


# --------------
from sklearn.linear_model import Ridge

# Code starts here
ridge = Ridge()
ridge.fit(X_train, y_train)
ridge_pred = ridge.predict(X_test)
r2_ridge = r2_score(y_test, ridge_pred)

# Code ends here


# --------------
from sklearn.model_selection import cross_val_score

#Code starts here
regressor = LinearRegression()
score = cross_val_score(estimator=regressor, X=X_train, y=y_train, cv=10)
mean_score = score.mean()
print(mean_score)


# --------------
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

#Code starts here
model = make_pipeline(PolynomialFeatures(2), LinearRegression())

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

r2_poly = r2_score(y_test, y_pred)
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