Python Exploratory Data Analysis
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Comprehensive analysis of datasets using Python libraries such as pandas, matplotlib, and seaborn
Stroke Analysis
Detailed EDA of stroke dataset: missing‑value architecture, variable types, age/gender/medical‑history interactions. Builds the foundation before modelling.
Laptop Price Analysis
Price drivers—RAM, CPU, SSD, resolution—visualised. Outliers removed, categoricals encoded.
Bike Sharing Analysis
Demand versus weather, temperature, humidity. Hourly usage heatmaps, temporal features and correlations.
Diabetes Analysis
Impact of glucose, insulin, BMI, age. Missing‑value checks, distributions and target‑wise comparisons.
Sleep Health Analysis
Sleep patterns vs metabolic, immune & mental health. Cleaned data, correlation webs, chronic‑risk visuals.
Netflix Trend Analysis
User demographics, hourly density, content preferences—all feeding recommendation insights.
Student Performance Analysis
Exam results cross‑tabbed with study habits, family support, school environment. Predictive factors illuminated.
Supermarket Analysis
Revenue by product, seasonality, region. Campaign lift and stocking guidance derived.
Health Expenditure Analysis
Cost effects of age, BMI, insurance, chronic disease. Distribution exploration & regression takeaways.
Real Estate Analysis
2001‑2022 data cleansed → price trends, economic shocks, regional patterns. Visual narrative of housing market.