Universal Data Science Python Template
This template provides a streamlined workflow for common data science tasks, including data preprocessing, modeling, and visualization.
Step 1: Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.cluster import KMeans, DBSCAN
from sklearn.metrics import accuracy_score, r2_score, silhouette_score
Step 2: Load Dataset
df = pd.read_csv('your_dataset.csv') # Replace with your dataset name
print(df.head())
Step 3: Data Cleaning
df = df.dropna()
Step 4: Feature Selection
X = df.drop(
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