35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
# Importing the libraries
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import pandas as pd
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import numpy as np
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# Importing the dataset
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dataset = pd.read_csv('../datasets/50_Startups.csv')
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X = dataset.iloc[ : , :-1].values
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Y = dataset.iloc[ : , 4 ].values
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# Encoding Categorical data
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder
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labelencoder = LabelEncoder()
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X[: , 3] = labelencoder.fit_transform(X[ : , 3])
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onehotencoder = OneHotEncoder(categorical_features = [3])
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X = onehotencoder.fit_transform(X).toarray()
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# Avoiding Dummy Variable Trap
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X = X[: , 1:]
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# Splitting the dataset into the Training set and Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
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# Fitting Multiple Linear Regression to the Training set
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor.fit(X_train, Y_train)
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# Predicting the Test set results
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y_pred = regressor.predict(X_test)
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# regression evaluation
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from sklearn.metrics import r2_score
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print(r2_score(Y_test, y_pred))
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