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70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
"""
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Program that predicts wine quality based on variant2.csv data
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"""
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression, LogisticRegression
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from sklearn.metrics import mean_squared_error, accuracy_score, f1_score
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wine_df = pd.read_csv("variant2.csv")
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wine_df.head()
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wine_df.describe()
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wine_df.info()
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X = wine_df.iloc[:, :-1].values
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y = wine_df.iloc[:, -1].values
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=0)
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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y_pred = regressor.predict(X_test)
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mse = mean_squared_error(y_test, y_pred)
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print("MSE:", mse)
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classifier = LogisticRegression()
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classifier.fit(X_train, y_train)
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y_pred = classifier.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print("Accuracy:", accuracy)
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y_pred_train = regressor.predict(X_train)
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train_mse = mean_squared_error(y_train, y_pred_train)
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print("Training MSE:", train_mse)
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train_r_squared = regressor.score(X_train, y_train)
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print("Training R^2:", train_r_squared)
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test_r_squared = regressor.score(X_test, y_test)
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print("Testing R^2:", test_r_squared)
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y_pred_train = classifier.predict(X_train)
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train_accuracy = accuracy_score(y_train, y_pred_train)
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print("Training Accuracy:", train_accuracy)
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train_f1_score = f1_score(y_train, y_pred_train, average="weighted")
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print("Training F1 Score:", train_f1_score)
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test_f1_score = f1_score(y_test, y_pred, average="weighted")
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print("Testing F1 Score:", test_f1_score)
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Data1 = sns.countplot(x="quality", data=wine_df)
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plt.draw()
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plt.waitforbuttonpress(0)
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plt.close()
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Data2 = sns.heatmap(wine_df.corr(), annot=True)
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plt.draw()
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plt.waitforbuttonpress(0)
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plt.close()
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