📊 Data Science & Analytics Portfolio

Welcome to my Data Science & Analytics Project Repository! This repository contains a curated collection of practical and academic projects that apply various data analysis, visualization, and machine learning techniques to solve real-world problems. Each folder includes datasets, Jupyter notebooks, and output visualizations.

---

📁 Project List

1. COVID-19 Data Analysis

Objective: Analyze global COVID-19 trends to understand the progression, recovery, and mortality rates.

• Techniques: Data cleaning, line plots, heatmaps
• Tools: Pandas, Matplotlib, Seaborn

2. Customer Segmentation Using K-Means

Objective: Segment customers based on purchasing behavior using unsupervised machine learning.

• Techniques: K-Means Clustering, Elbow Method
• Tools: Scikit-learn, Seaborn, Matplotlib

3. E-Commerce Data Insights

Objective: Analyze e-commerce transaction data to extract insights about user behavior and sales trends.

• Techniques: Grouping, Aggregation, Trend Analysis
• Tools: Pandas, Seaborn, Matplotlib

4. Netflix User Behavior Analysis

Objective: Explore Netflix viewing patterns to derive content preference and genre popularity.

• Techniques: EDA, Pie Charts, Histograms
• Tools: Pandas, Plotly, Matplotlib

5. Sales Forecasting with Linear Regression

Objective: Predict future sales based on past data using linear regression modeling.

• Techniques: Linear Regression, Model Evaluation
• Tools: Scikit-learn, Pandas, Seaborn

6. Student Performance Analytics Dashboard

Objective: Build an interactive dashboard to explore student performance across subjects and demographics.

• Techniques: Dashboarding, Group Analysis
• Tools: Plotly Dash / Streamlit, Pandas, Matplotlib

7. Survey Data Visualization

Objective: Transform survey results into meaningful visualizations to identify trends and sentiment.

• Techniques: Bar Charts, Heatmaps, Pie Charts
• Tools: Matplotlib, Seaborn, Plotly

8. Traffic Pattern Analysis

Objective: Analyze traffic data to identify congestion trends and peak hours.

• Techniques: Time Series Analysis, EDA
• Tools: Pandas, Seaborn, Matplotlib

---

📌 Getting Started

Each folder contains:

• Raw or cleaned dataset
• Python code (.ipynb)
• Output plots and findings

To run:

1. Clone this repository
2. Navigate into any project folder
3. Run the notebook or script in your preferred IDE or environment

---

🛠 Technologies Used

• Python
• Pandas, Numpy
• Matplotlib, Seaborn, Plotly
• Scikit-learn
• Jupyter Notebook

---

📬 Contact

Feel free to connect with me on LinkedIn or reach out for collaboration opportunities!