📊 Data Analysis Projects Portfolio

Welcome to my Data Analysis Portfolio, where I showcase various real-world projects leveraging Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, and more. Each project involves data cleaning, exploratory data analysis (EDA), visualization, and where applicable, predictive insights and feature engineering.

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🧭 Overview

This repository includes analyses across various domains such as health, e-commerce, tech, and transportation.

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🗂️ Project Structure

Each folder contains:

• dataset/: The raw and cleaned datasets
• notebooks/: Jupyter/Colab notebooks with full analysis
• reports/: Summary reports or presentation slides
• README.md: Project-specific documentation

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📁 Projects Overview

📌 1. Covid-19 Analysis and Visualization

• Objective: Analyze and visualize the spread of COVID-19 globally.
• Features:
  • Time-series trend analysis by country.
  • Daily new cases, recoveries, and deaths.
  • Choropleth maps using Plotly for global impact.
  • Forecasting trends with moving averages.
• Tech Used: Pandas, Matplotlib, Plotly, GeoPandas

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🔍 2. Google Search Analysis with Python

• Objective: Use Google Trends data to analyze public interest over time.
• Features:
  • Extract keyword trends using pytrends API.
  • Compare multiple terms (e.g., COVID vs. Vaccine).
  • Heatmaps for region-wise interest.
  • Weekly vs Monthly interest variation.
• Tech Used: PyTrends, Pandas, Seaborn, Plotly

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📱 3. iPhone Sales Analysis

• Objective: Study the sales patterns and trends of iPhone models.
• Features:
  • Quarterly and yearly revenue breakdown.
  • Correlation of marketing spend vs. sales.
  • Prediction of future sales using Linear Regression.
  • Profit margin and market share visualization.
• Tech Used: Pandas, Seaborn, Scikit-Learn, Matplotlib

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🚗 4. Uber Drive Data Analysis

• Objective: Analyze ride data to derive business insights for Uber.
• Features:
  • Trip frequency analysis per day/time.
  • Idle time and rush hour heatmaps.
  • Outlier detection in trip durations.
  • Driver utilization rates.
• Tech Used: Pandas, Matplotlib, Plotly, NumPy

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🌐 5. Web Scraping Project

• Objective: Collect and analyze data from live websites.
• Features:
  • Web scraping using BeautifulSoup and requests.
  • Clean and normalize scraped data.
  • Store in CSV or SQLite database.
  • Visualize top trends or products from the website.
• Tech Used: BeautifulSoup, Pandas, Matplotlib

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🍽️ 6. Zomato Data Analysis Using Python

• Objective: Analyze Zomato’s restaurant data to uncover user trends.
• Features:
  • Cuisine-based rating and price analysis.
  • Location-wise restaurant density and performance.
  • Sentiment analysis of reviews (optional advanced feature).
  • Recommend top restaurants by region.
• Tech Used: Pandas, Seaborn, Matplotlib, NLP (optional)

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💡 Advanced Features (Across Projects)

• 📈 Interactive Dashboards using Plotly and Streamlit (optional extensions).
• 🧼 Data Cleaning Pipelines using functions/classes.
• 🧠 Basic Machine Learning integration (Regression/Clustering).
• 📊 Custom Visualizations with annotations and interactivity.
• 📝 Automated Report Generation with nbconvert or Jinja2.

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🧰 Tools & Libraries Used

Category	Tools/Libraries
Data Handling	pandas, numpy
Visualization	matplotlib, seaborn, plotly, geopandas
ML (where used)	scikit-learn, statsmodels
Web Scraping	beautifulsoup4, requests, pytrends
IDE/Environment	VS Code, Jupyter Notebook, Google Colab

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🧑‍💻 How to Run

# Clone the repository
git clone https://github.com/yourusername/data-analysis-projects.git
cd data-analysis-projects

# Open Jupyter or VS Code
jupyter notebook
# or
code .