A Step-by-Step Guide to Data Science Project Lifecycle

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…


This content originally appeared on DEV Community and was authored by orioninsist

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 🚀


This content originally appeared on DEV Community and was authored by orioninsist


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