DATA SET COLLECTION

 In Python, datasets are commonly used for a wide range of applications, including data analysis, machine learning, statistics, and data visualization. Datasets usually consist of structured data, typically in tabular form, such as CSV files, Excel files, or databases. Python provides several libraries that make working with datasets easy and efficient. Below are the primary ways  datasets are used in Python.

1. Loading and Handling Data

import pandas as pd data = pd.read_csv("data.csv") 

 print(data.head())

2. Exploratory Data Analysis (EDA)

data.describe()  

3. Cleaning and Preprocessing

data.drop_duplicates()

4. Machine Learning and Model Training

5. Saving and Exporting Data

data.to_csv("processed_data.csv")  

6. Statistical Analysis

from scipy import stats t_stat, p_value = stats.ttest_ind(data['group1'], data['group2'])

7. Time Series Analysis

data['date'] = pd.to_datetime(data['date']) 

DATA SET COLLECTION:

• Bank_Personal_Loan_Modelling.xlsx
• boston_housing.npz
• concrete.csv
• diabetes.csv
• heart (1).csv
• Mall_Customers.csv
• Social_Network_Ads.csv
• titanic.csv