Create Week4_Assignment.py

SoM-2024/WEEK4/23BCS108/Week4_Assignment.py

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+!wget https://archive.ics.uci.edu/static/public/186/wine+quality.zip
+
+import zipfile
+
+with zipfile.ZipFile('wine+quality.zip', 'r') as zip_ref:
+    zip_ref.extractall()
+
+"""# Importing the Libraries"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+df1 = pd.read_csv('winequality-red.csv', sep=';')
+df1.columns = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']
+df1.head()
+
+df1.columns
+
+# Making a heatmap so as to select the feature vectors
+
+correlation_matrix = df1.corr()
+sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
+plt.show()
+
+plt.scatter(df1['fixed acidity'], df1['quality'])
+plt.xlabel('fixed acidity')
+plt.ylabel('quality')
+plt.show()
+
+"""# Making the X and y columns"""
+
+X = df1.drop('quality', axis=1)
+y = df1['quality']
+
+X_train,X_test, y_train, Y_test = train_test_split(df1.drop('quality', axis=1), df1['quality'], test_size=0.2, random_state=42)
+
+"""# Training with Decision Tree"""
+
+from sklearn.tree import DecisionTreeClassifier
+Model1 = DecisionTreeClassifier()
+Model1.fit(X_train, y_train)
+
+from sklearn.metrics import accuracy_score
+Y_pred1 = Model.predict(X_test)
+accuracy_score(Y_test, Y_pred1)
+
+"""# Training with Random Forest Classifier"""
+
+from sklearn.ensemble import RandomForestClassifier
+Model2 = RandomForestClassifier()
+Model2.fit(X_train, y_train)
+
+y_pred2 = Model.predict(X_test)
+accuracy_score(Y_test, y_pred2)
+
+"""# Training with XGBoost"""
+
+from xgboost import XGBClassifier
+from sklearn.preprocessing import LabelEncoder
+
+le = LabelEncoder()
+y_train_encoded = le.fit_transform(y_train)
+
+Model3 = XGBClassifier(n_estimators=100, learning_rate=0.01)
+Model3.fit(X_train, y_train_encoded)
+
+y_test_encoded = le.transform(Y_test)
+y_pred3 = model.predict(X_test)
+accuracy_score(y_test_encoded, y_pred3)
+
+def get_score(model, X_train, X_test, y_train, y_test):
+
+    le = LabelEncoder()
+    y_train_encoded = le.fit_transform(y_train)
+    y_test_encoded = le.transform(y_test)
+
+    model.fit(X_train, y_train_encoded)
+    y_pred = model.predict(X_test)
+    return accuracy_score(y_test_encoded, y_pred)
+
+"""# Making folds (used stratified K-fold so as to make normal distribution)"""
+
+from sklearn.model_selection import StratifiedKFold
+
+folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
+
+for train_index, test_index in folds.split(X, y):
+    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
+    y_train, y_test = y[train_index], y[test_index]
+    print(get_score(Model1, X_train, X_test, y_train, y_test))
+    print(get_score(Model2, X_train, X_test, y_train, y_test))
+    print(get_score(Model3, X_train, X_test, y_train, y_test))
+