https://github.com/tolumie/exploratory-data-analytics-projects

Exploratory Data Analytics – A collection of projects covering data exploration, feature engineering, hypothesis testing, and predictive modeling across diverse datasets, including insurance, real estate, laptops, cars, COVID-19, and the Olympics.
https://github.com/tolumie/exploratory-data-analytics-projects

data-analysis data-visualization data-wrangling exploratory-data-analysis-eda feature-engineering hypothesis-testing machine-learning matplotlib numpy pandas predictive-modeling python seaborn statistical-analysis

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Exploratory Data Analytics – A collection of projects covering data exploration, feature engineering, hypothesis testing, and predictive modeling across diverse datasets, including insurance, real estate, laptops, cars, COVID-19, and the Olympics.

• Host: GitHub
• URL: https://github.com/tolumie/exploratory-data-analytics-projects
• Owner: Tolumie
• Created: 2025-03-07T16:38:58.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-03-07T17:00:47.000Z (7 months ago)
• Last Synced: 2025-03-07T17:33:26.514Z (7 months ago)
• Topics: data-analysis, data-visualization, data-wrangling, exploratory-data-analysis-eda, feature-engineering, hypothesis-testing, machine-learning, matplotlib, numpy, pandas, predictive-modeling, python, seaborn, statistical-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📊 Exploratory Data Analytics  

## 📌 Overview  

This repository contains a collection of **data analytics projects** that focus on **data exploration, feature engineering, hypothesis testing, and predictive modeling**. The datasets cover various domains, including **insurance costs, laptop and used car pricing, house sales, COVID-19 trends, and the Olympic Games**.  

## 📁 Project Descriptions  

### 1️⃣ **Insurance Cost Analysis**  

- Investigates **insurance cost drivers** using statistical methods.  

- Explores relationships between **age, BMI, smoking status, and charges**.  

### 2️⃣ **Laptop Pricing Analysis**  

- Examines factors affecting **laptop prices**, including **brand, specifications, and market demand**.  

- Builds **predictive models** for price estimation.  

### 3️⃣ **Used Car Pricing Analysis**  

- Uses **EDA and machine learning** to understand used car pricing trends.  

- Factors include **mileage, manufacturing year, and brand perception**.  

### 4️⃣ **House Sales in King County (USA)**  

- Analyzes **real estate trends**, identifying key features influencing **house prices**.  

- Uses **regression modeling** for price prediction.  

### 5️⃣ **Exploratory Data Analysis of COVID-19 in India**  

- Examines the **spread, mortality, and recovery trends** of COVID-19 in India.  

- Uses **time series analysis** to visualize case growth.  

### 6️⃣ **Olympic Games Data Analysis**  

- Investigates **medal distributions, country participation trends, and athlete performances** over time.  

### 7️⃣ **Machine Learning & Statistical Analysis Labs**  

- **Classification with Python:** Covers **logistic regression, decision trees, and SVM**.  

- **Feature Engineering Lab:** Focuses on **data transformation and new feature creation**.  

- **Hypothesis Testing Lab:** Applies **z-tests, t-tests, and chi-square tests** to validate assumptions.  

## 🛠️ Key Techniques  

✔ **Data Wrangling & Cleaning** – Handling missing values, outlier detection, and feature extraction.  

✔ **Exploratory Data Analysis (EDA)** – Uncovering patterns and insights through **visualization and statistics**.  

✔ **Feature Engineering** – Creating new features to improve model accuracy.  

✔ **Machine Learning Models** – Regression and classification models for predictive analysis.  

✔ **Hypothesis Testing** – Using **statistical tests to validate findings**.  

## 📂 Files in This Repository  

- **Data Wrangling Notebooks** – Cleaning and preprocessing datasets.  

- **EDA Notebooks** – Exploring data distributions and visualizing trends.  

- **Feature Engineering & Hypothesis Testing Notebooks** – Transforming data and conducting statistical analysis.  

- **Machine Learning Notebooks** – Developing and evaluating predictive models.  

## 🚀 Future Enhancements  

🔹 Integrating **advanced machine learning models** for better predictions.  

🔹 Expanding **time series forecasting** for financial and pricing trends.  

🔹 Incorporating **more real-world datasets** for deeper insights.