پیش‌ پردازش داده‌ ها در پایتون

# importing libraries
import pandas as pd
import scipy
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset
df = pd.read_csv('Geeksforgeeks/Data/diabetes.csv')
print(df.head())

df.info()

df.isnull().sum()

df.describe()

# Box Plots
fig, axs = plt.subplots(9,1,dpi=95, figsize=(7,17))
i = 0
for col in df.columns:
	axs[i].boxplot(df[col], vert=False)
	axs[i].set_ylabel(col)
	i+=1
plt.show()

# Identify the quartiles
q1, q3 = np.percentile(df['Insulin'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (1.5 * iqr)
upper_bound = q3 + (1.5 * iqr)
# Drop the outliers
clean_data = df[(df['Insulin'] >= lower_bound) 
				& (df['Insulin'] <= upper_bound)]


# Identify the quartiles
q1, q3 = np.percentile(clean_data['Pregnancies'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (1.5 * iqr)
upper_bound = q3 + (1.5 * iqr)
# Drop the outliers
clean_data = clean_data[(clean_data['Pregnancies'] >= lower_bound) 
						& (clean_data['Pregnancies'] <= upper_bound)]


# Identify the quartiles
q1, q3 = np.percentile(clean_data['Age'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (1.5 * iqr)
upper_bound = q3 + (1.5 * iqr)
# Drop the outliers
clean_data = clean_data[(clean_data['Age'] >= lower_bound) 
						& (clean_data['Age'] <= upper_bound)]


# Identify the quartiles
q1, q3 = np.percentile(clean_data['Glucose'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (1.5 * iqr)
upper_bound = q3 + (1.5 * iqr)
# Drop the outliers
clean_data = clean_data[(clean_data['Glucose'] >= lower_bound) 
						& (clean_data['Glucose'] <= upper_bound)]


# Identify the quartiles
q1, q3 = np.percentile(clean_data['BloodPressure'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (0.75 * iqr)
upper_bound = q3 + (0.75 * iqr)
# Drop the outliers
clean_data = clean_data[(clean_data['BloodPressure'] >= lower_bound) 
						& (clean_data['BloodPressure'] <= upper_bound)]


# Identify the quartiles
q1, q3 = np.percentile(clean_data['BMI'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (1.5 * iqr)
upper_bound = q3 + (1.5 * iqr)
# Drop the outliers
clean_data = clean_data[(clean_data['BMI'] >= lower_bound) 
						& (clean_data['BMI'] <= upper_bound)]


# Identify the quartiles
q1, q3 = np.percentile(clean_data['DiabetesPedigreeFunction'], [25, 75])
# Calculate the interquartile range
iqr = q3 - q1
# Calculate the lower and upper bounds
lower_bound = q1 - (1.5 * iqr)
upper_bound = q3 + (1.5 * iqr)

# Drop the outliers
clean_data = clean_data[(clean_data['DiabetesPedigreeFunction'] >= lower_bound) 
						& (clean_data['DiabetesPedigreeFunction'] <= upper_bound)]

#correlation
corr = df.corr()

plt.figure(dpi=130)
sns.heatmap(df.corr(), annot=True, fmt= '.2f')
plt.show()

# separate array into input and output components
X = df.drop(columns =['Outcome'])
Y = df.Outcome

# initialising the MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))

# learning the statistical parameters for each of the data and transforming
rescaledX = scaler.fit_transform(X)
rescaledX[:5]

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler().fit(X)
rescaledX = scaler.transform(X)
rescaledX[:5]