Upload 1046_159.py

1046_159.py

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+# -*- coding: utf-8 -*-
+"""1046.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1uxVrUlNk5jB6t_CKcD_4ZvpDnJfiKqyu
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+import seaborn as sns
+import warnings
+
+warnings.filterwarnings('ignore')
+
+data = pd.read_csv('/content/Credit_Data.csv')
+
+data.head()
+
+data.drop('ID',axis=1,inplace=True)
+
+data.shape
+
+def get_summary(df):
+  df_desc = pd.DataFrame(df.describe(include='all').transpose())
+  df_summary = pd.DataFrame({
+      'dtype': df.dtypes,
+      '#missing': df.isnull().sum().values,
+      '#duplicates': df.duplicated().sum(),
+      '#unique': df.nunique().values,
+      'min': df_desc['min'].values,
+      'max': df_desc['max'].values,
+      'avg': df_desc['mean'].values,
+      'std dev': df_desc['std'].values,
+  })
+  return df_summary
+
+get_summary(data).style.background_gradient()
+
+target_col = 'Balance'
+feature = data.drop('Balance', axis=1).columns
+
+fig, ax = plt.subplots(2, 5, figsize=(20, 10))
+axes = ax.flatten()
+
+for i, col in enumerate(data[feature].columns):
+  sns.scatterplot(data=data, x=col, y='Balance', hue='Gender', ax=axes[i])
+
+  fig.suptitle('Interactions between Target Column and Features')
+  plt.tight_layout()
+  plt.show()
+
+fig, ax = plt.subplots(2, 6, figsize=(20, 10))
+axes = ax.flatten()
+
+for i, col in enumerate(data.columns):
+  sns.histplot(data=data, x=col, hue='Gender', ax=axes[i])
+
+  fig.suptitle("Gender-Based Distribution of Financial and Demographic Features in the Dataset")
+  plt.tight_layout()
+
+for ax in axes:
+  if not ax.has_data():
+      fig.delaxes(ax)
+
+plt.show()
+
+sns.pairplot(data, kind='scatter', diag_kind='hist', hue='Gender', palette='colorblind')
+
+numeric_columns = data.select_dtypes(include='number').columns
+
+fig, ax = plt.subplots(len(numeric_columns), 2, figsize=(12, len(numeric_columns)*2))
+ax = ax.flatten()
+
+for i, col in enumerate(numeric_columns):
+  sns.boxplot(data=data, x=col, width=0.6, ax=ax[2*i])
+  sns.violinplot(data=data, x=col, ax=ax[2*i + 1])
+
+plt.tight_layout()
+plt.show()
+
+corr = data.select_dtypes(exclude='object').corr(method='spearman')
+mask = np.triu(np.ones_like(corr))
+
+sns.heatmap(corr, annot=True, mask=mask, cmap='YlGnBu',cbar=True)
+plt.title('Correlation Matrix',fontdict={'color': 'blue', 'fontsize': 12})
+
+from sklearn.preprocessing import OneHotEncoder
+
+cat_columns = data.select_dtypes(include='O').columns.to_list()
+
+dummie_df = pd.get_dummies(data=data[cat_columns], drop_first=True).astype('int8')
+
+df = data.join(dummie_df)
+df.drop(cat_columns,axis=1,inplace=True)
+
+df.head()
+
+from imblearn.over_sampling import SMOTE
+from collections import Counter
+
+X_train = df.drop('Student_Yes',axis=1)
+y_train = df['Student_Yes']
+
+sm = SMOTE(sampling_strategy='minority',random_state=14, k_neighbors=5, n_jobs=-1)
+sm_X_train, sm_Y_train = sm.fit_resample(X_train,y_train)
+
+print('Before sampling class distribution', Counter(y_train))
+print('\nAfter sampling class distribution', Counter(sm_Y_train))
+
+sm_df = pd.concat([sm_X_train,sm_Y_train],axis=1)
+sm_df.head()
+
+get_summary(sm_df).style.background_gradient()
+
+!pip install ydata_profiling
+
+from ydata_profiling import ProfileReport
+
+profile_report = ProfileReport(
+    sm_df,
+    sort=None,
+    progress_bar=False,
+    html = {'style': {'full_width': True}},
+    correlations={
+        "auto": {"calculate": True},
+        "pearson": {"calculate": False},
+        "spearman": {"calculate": False},
+        "kendall": {"calculate": False},
+        "phi_k": {"calculate": True},
+        "cramers": {"calculate": True},
+    },
+    explorative=True,
+    title="Profiling Report"
+)
+
+profile_report.to_file('output.html')
+
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.preprocessing import StandardScaler
+from sklearn import metrics
+
+X = sm_df.drop('Balance',axis=1)
+y = sm_df.Balance
+
+train_x, valid_x, train_y, valid_y = train_test_split(X,y, test_size=0.2, random_state=16518, shuffle=True)
+
+scaler = StandardScaler()
+train_x = scaler.fit_transform(train_x)
+valid_x = scaler.transform(valid_x)
+
+lm = LinearRegression()
+history = lm.fit(train_x, train_y)
+pred = lm.predict(valid_x)
+r2 = metrics.r2_score(valid_y,pred)
+
+print('r2_score',r2)
+
+lm_df = pd.DataFrame(history.coef_.T, index= X.columns, columns=['coef_'])
+
+lm_df.loc['intercept_'] = lm.intercept_
+
+lm_df.sort_values(by='coef_')
+
+plt.barh(y= lm_df.index, width='coef_', data=lm_df)
+plt.show()
+
+from sklearn.model_selection import train_test_split, cross_val_score, KFold
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn import metrics
+
+X = sm_df.drop('Balance',axis=1)
+y = sm_df.Balance
+
+train_x, valid_x, train_y, valid_y = train_test_split(X,y, test_size=0.2, random_state=16518, shuffle=True)
+
+X_trainv, X_valid, Y_trainv, Y_valid = train_test_split(train_x, train_y, test_size=0.2, random_state=16518, shuffle=True)
+
+train_x.shape, valid_x.shape
+
+X_trainv.shape, X_valid.shape
+
+def create_polynomial_regression_model(degree):
+  "Create a polynomial regression model for the given degree"
+
+  poly_features = PolynomialFeatures(degree=degree, include_bias=False)
+
+  X_train_poly = poly_features.fit_transform(X_trainv)
+
+  poly_model = LinearRegression()
+  poly_model.fit(X_train_poly, Y_trainv)
+
+  y_train_predicted = poly_model.predict(X_train_poly)
+
+  y_valid_predict = poly_model.predict(poly_features.fit_transform(X_valid))
+
+  mse_train = metrics.mean_squared_error(Y_trainv, y_train_predicted)
+
+  mse_valid = metrics.mean_squared_error(Y_valid, y_valid_predict)
+
+  return (mse_train, mse_valid,degree)
+
+a=[]
+for i in range(1,8):
+  a.append(create_polynomial_regression_model(i))
+df = pd.DataFrame(a,columns=['Train Error', 'Validation Error', 'Degree'])
+df.sort_values(by='Validation Error')
+
+scaler = StandardScaler()
+train_x = scaler.fit_transform(train_x)
+valid_x = scaler.transform(valid_x)
+
+polynomial_features = PolynomialFeatures(degree=2, include_bias=False)
+train_x_poly = polynomial_features.fit_transform(train_x)
+valid_x_poly = polynomial_features.fit_transform(valid_x)
+
+polymodel = LinearRegression()
+polymodel.fit(train_x_poly, train_y)
+pred = polymodel.predict(valid_x_poly)
+r2 = metrics.r2_score(valid_y,pred)
+
+print('r2_score:', r2)