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

#datascience #machinelearning #ai

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)