Data Analysis and Machine Learning Projects

Welcome to my repository of Data Analysis and Machine Learning projects. Below you will find a collection of my work, categorized by their business domains for easy navigation.

Analysis Categories

1. Business Intelligence and Sales Analysis

• Maven Roasters EDA and Sales Prediction: Analysis of sales data to predict future sales trends.
• Marketing Campaigns: Analysis of customer behaviour and purchase patterns.
• Online Retail Sales Clustering): Clustering analysis of online retail sales data. The project aims to segment customers based on their purchasing behavior using clustering techniques.

2. Marketing and Customer Analysis

• Red Wine Quality: Analysis of factors influencing wine quality to understand customer preferences.
• Netflix Dataset: Insights into content consumption trends to aid in content marketing strategies.
• Spotify Data Analysis: Analyzing music streaming data to understand listener preferences and behavior.
• Udemy Courses: Analysis of online course data to understand market trends in education.
• Automobile Dataset: This repository contains a Jupyter Notebook (Automobile.ipynb) that explores the Automobile dataset. The notebook covers various aspects of data analysis including data cleaning, visualization, and exploratory data analysis (EDA).
• Bank Customer Churn Prediction:This project focuses on predicting customer churn for a bank using various data analysis and machine learning techniques.
• Customer Segmentation:This repository contains a Jupyter Notebook for segmenting customers based on their purchasing behavior.

3. Risk Management and Financial Analysis

• Stocks Prices (2006-2018)): Analysis of stock prices for investment risk management.
• California Housing Analysis: Predicting housing prices to aid in real estate risk assessment.
• Diamonds Dataset: Predictive modeling on diamond prices for financial decisions in the luxury market.
• Utah Real Estates: Analysis of houses and listing price prediction.
• Credit Card Fraud Detetion: The project involves data preprocessing, exploratory data analysis (EDA), and building machine learning models to identify fraudulent transactions.
• Customer E-Signing Classification: This repository contains a Jupyter Notebook for classifying customers based on their likelihood to electronically sign financial documents.
• House Prices:This repository contains a Jupyter Notebook for predicting house prices using regression models. The project involves data preprocessing, feature engineering, model training, and evaluation.

4. Public Health and Safety

• COVID-19 Analysis: Analysis of COVID-19 trends and patterns for public health policy and safety measures.
• Police Dataset: Analysis of police stops, violations, and outcomes for public safety and policy making.
• Titanic Dataset: Historical data analysis providing insights into customer segmentation and survival prediction.
• Heart Attach Prediction: Analyzing and Prediction of heart attachs from historical data.
• Breast Cancer Detection%20Data%20Set): Analysis of the Breast Cancer Wisconsin (Diagnostic) data set, which includes machine learning and data analysis techniques.

- Medical Insurance Prediction: This repository contains a Jupyter Notebook that performs data analysis and machine learning to predict medical insurance costs based on various features. - Water Quality: This repository contains a Jupyter Notebook that performs data analysis and machine learning to analyze potable water.

• Weather Clustering: This project contains Data Analysis and Clustering weather by KMeans & Hierarchical Clustering methods.

5. Transportation and Logistics

• Flights Dataset: Analyzing flight delays and related factors for logistics and transportation management.
• Seaborn Taxis Dataset: Analysis of taxi data to understand transportation patterns.

6. Employment and Workforce

• Data Science Jobs: Preprocessing and analysis of job listings to understand market demand for data science skills and employment trends.
• Salaries Dataset: This data analysis, explores the Salary Dataset through Exploratory Data Analysis (EDA). The notebook covers various aspects of data analysis, including data cleaning, visualization, and deriving insights from the data.
• Employee Attrition Analysis: The project involves data preprocessing, exploratory data analysis (EDA), and building classification models to predict whether an employee will leave the company.

7. Sentiment Analysis

• Restaurant Review Classification and Sentiment Analysis: This repository contains a Jupyter Notebook for classifying restaurant reviews and performing sentiment analysis.
• IMDB Movie's Review - Sentiment Analysis: Analysis of IMDB movie reviews. The project aims to classify movie reviews as positive or negative using various machine learning techniques.
• - Reviews Sentiment Analysis Using NLP): The project aims to classify reviews as positive or negative using various NLTK and LogisticRegression.

8. Predictive Maintenance and Reliability Engineering

• Remaining Useful Life (RUL) Prediction for Jet Engines%20Prediction%20for%20Jet%20Engines): This project focuses on Exploratory Data Analysis (EDA) of the Turbofan Engine Degradation Simulation Dataset

9. Times Series And Forecasting

• Electricity Consumption Analysis Time Series:This repository includes code and visualizations for time-series analysis and forecasting of electricity consumption

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Repository Structure

Each project contains:

• Data preprocessing and cleaning steps
• Exploratory data analysis (EDA)
• Model building and evaluation (for applicable projects)
• Visualization of insights

Getting Started

To get started with any of these projects, clone the repository and navigate to the specific project folder:

git clone https://github.com/mohammadreza-mohammadi94/DataAnalysisMachine_Learning.git
cd DataAnalysisMachine_Learning/<project-folder>

Each project folder contains a Jupyter Notebook (.ipynb) detailing the analysis and a README.md with further instructions and context.

Contributing

If you would like to contribute to this repository, please fork the project and submit a pull request.