2. EDA Visualization, Math, Statistics & Probability
Sections 7-9: Matplotlib & Seaborn plots, Vectors, Dot Products, Derivatives, Gradient Descent & Hypothesis Testing
Module 1: Visualization & Exploratory Data Analysis (EDA)
Uncovering data distributions, relationships, outliers, and correlations using Matplotlib and Seaborn.
🇬🇧 English Definition
Exploratory Data Analysis (EDA) inspects dataset shape, data types, missing values, duplicates, distributions, outliers, and feature correlations using statistical plots.
🇮🇳 Hinglish Definition
EDA se dataset ki quality, missing values, distribution aur correlations check hote hai Matplotlib aur Seaborn charts dwara.
Core Concepts & Principles
- Matplotlib: Foundation plotting library providing plt.plot(), plt.scatter(), plt.hist().
- Seaborn: Statistical visualization layer providing sns.scatterplot(), sns.heatmap(), sns.boxplot().
- EDA Checklist: Check dtypes, missing values, duplicates, outliers, target balance, and data leakage.
🔍 Line-by-Line Code Breakdown
- Line 1-2: Import matplotlib.pyplot and seaborn plotting modules.
- Line 3-4: Generate scatterplot displaying relationship between age and salary colored by department.
🎯 Interview Focus & Questions
- Q: What is the primary goal of EDA?
A: To understand data patterns, validate assumptions, identify anomalies/outliers, and prepare feature representations.
import matplotlib.pyplot as plt
import seaborn as sns
// Plotting scatter chart example
// sns.scatterplot(data=df, x="age", y="salary", hue="department")
// plt.show()
print("EDA Visualization Pipeline Configured.")Module 2: Mathematics, Linear Algebra & Gradient Descent
Vector dot products, matrix multiplication, derivatives, and iterative gradient descent optimization.
🇬🇧 English Definition
Linear Algebra (vectors, dot products, matrix operations) and Calculus (derivatives, gradients) power model optimization. Gradient Descent iteratively updates parameter weights opposite the loss gradient.
🇮🇳 Hinglish Definition
Dot product weighted sum nikalta hai. Gradient descent weights ko step-by-step update karke loss minimize karta hai (w = w - lr * grad).
Core Concepts & Principles
- Dot Product: w @ x computes weighted sum of inputs and weights.
- Gradient Descent Update Rule: w_new = w_old - (learning_rate * gradient).
- Learning Rate: Controls step size towards global cost minimum.
🔍 Line-by-Line Code Breakdown
- Line 1-2: Initialize weight w=5.0 and learning rate lr=0.1.
- Line 3-5: Iteratively compute loss gradient 2*w and update w = w - lr * grad.
🎯 Interview Focus & Questions
- Q: What is Gradient Descent?
A: An optimization algorithm that iteratively updates model weights in the direction of steepest loss decrease.
w = 5.0
lr = 0.1
for i in range(10):
grad = 2 * w
w = w - lr * grad
print("Optimized Weight w:", round(w, 4))