🔧 A Step-by-Step Guide to Data Science Project Lifecycle
Nachrichtenbereich: 🔧 Programmierung
🔗 Quelle: dev.to
Data Science is more than just training ML models—it’s a structured process. Let’s explore the seven fundamental stages of a successful data science project.
🔹 Data Collection
import pandas as pd
df = pd.read_csv("dataset.csv")
print(df.info())
🔹 Data Cleaning & Feature Engineering
df.fillna(df.mean(), inplace=True) # Handle missing values
df = pd.get_dummies(df, drop_first=True) # Convert categorical data
Model Training
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(df.drop("target", axis=1), df["target"], test_size=0.2)
model = RandomForestClassifier().fit(X_train, y_train)
📌 Read more for a complete breakdown of all stages! 🚀
DataScience #Python #MachineLearning #BigData #Tech 🚀
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