@lamlaitudau6262: Bà nào mi thưa, mi mỏng thì nghía ngay mã mi này nhà #YUER nhennnn #lamdep #makeup #trending

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Saturday 26 September 2026 15:50:04 GMT

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Comments

Nhinhinhanh :

xin link mua ạ

2026-10-02 08:43:16

1

Lệ Hằng :

Mi xinh lắm nha

2026-09-27 10:17:51

1

karenreview08 :

Mi xinh quá

2026-09-27 13:43:28

1

Đội trưởng Twinkle✩ :

mi xinh lắm ó chòi

2026-09-26 16:28:22

1

Dâu 🍓 :

xinh quá nhaaaa

2026-09-27 03:47:28

1

Nhã🛍️ :

Dán lên tự nhiên xinh xỉu🥰

2026-09-27 02:29:10

1

Đậu rì viuu🫛 :

dùng okii lắm

2026-09-26 16:01:07

1

totorochil :

Mi đẹp zị

2026-09-26 17:07:56

1

Xoài Minh Anh. :

chéo lại mình nha

2026-09-27 11:31:12

1

Rì viu có tâm 🛒🛍️ :

tt chéo nè

2026-09-26 16:04:29

1

thỏ hay khócc :

🥰

2026-09-26 17:34:10

1

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After understanding single variables (Univariate), now we explore how variables relate to each other. 🔹 1. Bivariate Analysis (2 Variables) 👉 Comparing two variables at a time to check relationships. 📊 Numerical vs Numerical: • Use scatter plots or correlation heatmaps. • Example: Hours studied ⏳ vs Exam scores 📚 → Strong positive relation. 📊 Categorical vs Numerical: • Use boxplots or grouped bar charts. • Example: Gender vs Exam scores → Do males/females score differently? 📊 Categorical vs Categorical: • Use cross tables or stacked bar charts. • Example: Education level vs Job type → Which education leads to which jobs? 💡 Goal: Find meaningful connections between two variables. 🔹 2. Multivariate Analysis (3 or More Variables) 👉 Analyzing multiple variables together to see complex patterns. 📊 Techniques: • Pair plots → Scatter plots for all variable pairs. • Heatmaps → Show correlation between many features. • Multidimensional visualization (3D plots, PCA, etc.). 📚 Example (Student Dataset): • Hours studied + Attendance + Sleep hours → Predict Exam Scores. • Gender + Study hours + Internet usage → Relation with Performance. 💡 Goal: Understand hidden patterns & interactions among multiple factors. ✅ Takeaway: • Univariate = Single variable view 🔍 • Bivariate = Pairwise relationships 🔗 • Multivariate = Whole system view 🌐 #MachineLearning #BivariateAnalysis #MultivariateAnalysis #DataScience #100DaysOfML

After understanding single variables (Univariate), now we explore how variables relate to each other. 🔹 1. Bivariate Analysis (2 Variables) 👉 Comparing two variables at a time to check relationships. 📊 Numerical vs Numerical: • Use scatter plots or correlation heatmaps. • Example: Hours studied ⏳ vs Exam scores 📚 → Strong positive relation. 📊 Categorical vs Numerical: • Use boxplots or grouped bar charts. • Example: Gender vs Exam scores → Do males/females score differently? 📊 Categorical vs Categorical: • Use cross tables or stacked bar charts. • Example: Education level vs Job type → Which education leads to which jobs? 💡 Goal: Find meaningful connections between two variables. 🔹 2. Multivariate Analysis (3 or More Variables) 👉 Analyzing multiple variables together to see complex patterns. 📊 Techniques: • Pair plots → Scatter plots for all variable pairs. • Heatmaps → Show correlation between many features. • Multidimensional visualization (3D plots, PCA, etc.). 📚 Example (Student Dataset): • Hours studied + Attendance + Sleep hours → Predict Exam Scores. • Gender + Study hours + Internet usage → Relation with Performance. 💡 Goal: Understand hidden patterns & interactions among multiple factors. ✅ Takeaway: • Univariate = Single variable view 🔍 • Bivariate = Pairwise relationships 🔗 • Multivariate = Whole system view 🌐 #MachineLearning #BivariateAnalysis #MultivariateAnalysis #DataScience #100DaysOfML

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