Day 29 – Choosing the Right Chart | AI Course in Tamil

Описание: Day 29 of your Complete AI Course in Tamil teaches how to choose the perfect visualization for any AI/Data Science scenario. This practical notebook-based session covers 8 essential chart types with real AI use-cases, Python code using Matplotlib + Seaborn, and clear rules for when to use each chart – transforming raw data into stakeholder-ready insights.

Section 1: Introduction explains why visualization matters in AI (spotting patterns, communicating results, debugging models) and previews Line → Bar → Histogram → Box → Scatter → Pie → Heatmap journey.

Line Chart (trend over time): sns.lineplot(x='month', y='sales') for monthly sales growth prediction. Perfect for continuous data, time series, model performance tracking.

Bar Chart (category comparison): sns.barplot(x='department', y='projects') shows AI projects per department. Ideal for discrete categories, rankings, part-to-whole comparisons.

Histogram (distribution): sns.histplot(data['age']) reveals customer age distribution. Essential for understanding feature spread, skewness, multimodal patterns before modeling.

Box Plot (outliers + spread): sns.boxplot(x='role', y='salary') analyzes AI startup salaries. Detects outliers, compares distributions across groups, shows quartiles.

Scatter Plot (relationships): sns.scatterplot(x='hours_studied', y='exam_score') explores study time vs exam score correlation. Perfect for 2 numerical features, linear relationships, clusters.

Pie Chart (proportions): plt.pie(company_usage, labels=['TensorFlow', 'PyTorch']) shows AI model usage % across companies. Use sparingly for 5-7 categories max.

Heatmap (correlations): sns.heatmap(df.corr(), annot=True) reveals feature correlations. Critical for feature selection, multicollinearity detection, model interpretation.

Live coding demonstrates each chart on real datasets with AI case studies: sales forecasting, customer segmentation, model evaluation, feature analysis. Tamil explanations clarify "Line for trends, Bar for categories, Histogram for distributions" decision tree.

By end of Day 29 (Tamil), students master 8 chart types + when to use each for professional AI presentations and EDA.