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)