data_science_portfolio

Data Science and Machine Learning Portfolio

Welcome to my data science and machine learning portfolio! This repository showcases a diverse collection of projects demonstrating my skills in data cleaning, exploratory data analysis (EDA), regression, classification, clustering, time series analysis, machine learning, and data visualization. The projects are implemented using Python, Stata, and R to highlight my proficiency across these tools.

Highlights

• Data Cleaning: Efficiently preprocess and clean data for accurate analysis.
• EDA: Uncover insights and trends through comprehensive exploratory data analysis.
• Regression and Classification: Build predictive models for various applications.
• Clustering: Segment data into meaningful groups.
• Time Series Analysis: Forecast future values using historical data.
• Machine Learning: Develop and evaluate advanced machine learning models.
• Visualization and Dashboards: Create interactive visualizations and dashboards.
• Deployment: Deploy machine learning models and applications.

Explore the projects to see detailed documentation, code, and results. Each project is designed to solve real-world problems and demonstrate practical applications of data science and machine learning techniques.

Python Projects

1. Data Cleaning

Project: Customer Data Cleaning

• Objective: Clean and preprocess raw customer data to make it suitable for analysis.
• Tools: Python, Pandas, NumPy
• Description: Handle missing values, outliers, and inconsistencies in a customer dataset. Document each step of the cleaning process.

Project: Web Scraped Data Cleaning

• Objective: Clean and organize data scraped from the web.
• Tools: Python, BeautifulSoup, Scrapy, Pandas
• Description: Scrape data from a website, then clean and format it for analysis. Include handling of HTML tags, special characters, and converting data types.

2. Exploratory Data Analysis (EDA)

Project: EDA on Movie Data

• Objective: Perform exploratory data analysis on a dataset of movies.
• Tools: Python, Pandas, Matplotlib, Seaborn, Numpy
• Description: Analyze movie data to find trends, correlations, and insights. Visualize distributions, relationships, and summary statistics.

Project: EDA on Sales Data

• Objective: Explore a sales dataset to understand sales trends and patterns.
• Tools: Python, Pandas, Matplotlib, Seaborn, plotly
• Description: Analyze sales data to uncover seasonal trends, top-selling products, and customer segments. Visualize findings with charts and graphs.

3. Regression Analysis

Project: House Price Prediction

• Objective: Predict house prices using regression techniques.
• Tools: Python, Pandas, Scikit-Learn, Matplotlib
• Description: Build and evaluate linear and polynomial regression models to predict house prices based on various features.

Project: Car Price Prediction

• Objective: Predict car prices based on various attributes.
• Tools: Python, Pandas, Scikit-Learn, Matplotlib
• Description: Use multiple regression models to predict car prices. Evaluate model performance and interpret the coefficients.

4. Classification Projects

Project: Customer Churn Prediction

• Objective: Predict customer churn using classification algorithms.
• Tools: Python, Pandas, Scikit-Learn, Matplotlib
• Description: Build and evaluate classification models (logistic regression, decision trees, etc.) to predict if a customer will churn based on historical data.

Project: Spam Email Detection

• Objective: Classify emails as spam or not spam.
• Tools: Python, Pandas, Scikit-Learn
• Description: Use XGBoost classifer in machine learning to build a spam detection model. Evaluate its accuracy and precision.

5. Clustering Projects

Project: Customer Segmentation

• Objective: Segment customers into distinct groups based on purchasing behavior.
• Tools: Python, Pandas, Scikit-Learn, Matplotlib, Seaborn
• Description: Apply clustering algorithms (K-means, hierarchical clustering) to group customers. Analyze and interpret the segments.

Project: Market Basket Analysis

• Objective: Identify patterns in customer purchases using association rule learning.
• Tools: Python, Pandas, mlxtend
• Description: Use Apriori algorithm to find frequent itemsets and association rules in transaction data. Visualize the results.

6. Time Series Analysis

Project: Stock Price Prediction

• Objective: Forecast future stock prices using time series analysis.
• Tools: Python, Pandas, Statsmodels, Matplotlib
• Description: Use ARIMA, SARIMA, or LSTM models to predict stock prices. Evaluate model accuracy with metrics like RMSE.

Project: Weather Forecasting

• Objective: Predict future weather conditions based on historical data.
• Tools: Python, Pandas, Statsmodels, Matplotlib
• Description: Apply time series forecasting techniques to weather data. Visualize the forecast and compare with actual values.

7. Machine Learning Projects

Project: Image Classification with Convolutional Neural Networks (CNN)

• Objective: Classify images into different categories using CNNs.
• Tools: Python, TensorFlow/Keras, OpenCV
• Description: Build and train a CNN model to classify images from a dataset (e.g., CIFAR-10, MNIST). Evaluate its performance.

Project: Natural Language Processing (NLP) for Sentiment Analysis

• Objective: Perform sentiment analysis on text data.
• Tools: Python, NLTK, Scikit-Learn, TensorFlow/Keras
• Description: Use NLP techniques and machine learning to classify text sentiment (positive, negative, neutral). Visualize results with word clouds and sentiment scores.

8. Dashboards and Visualization

Project: Interactive Sales Dashboard

• Objective: Create an interactive dashboard to visualize sales data.
• Tools: Python, Dash/Plotly, Tableau/Power BI
• Description: Build a dashboard to visualize key sales metrics and trends. Include interactive elements like dropdowns and sliders.

Project: COVID-19 Data Dashboard

• Objective: Visualize COVID-19 data with interactive charts and maps.
• Tools: Python, Dash/Plotly, Tableau/Power BI
• Description: Create a dashboard to track COVID-19 cases, recoveries, and deaths. Include time series charts, maps, and summary statistics.

9. Deployment Projects

Project: Deploying a Machine Learning Model as an API

• Objective: Deploy a trained machine learning model as a web API.
• Tools: Python, Flask/FastAPI, Docker, Heroku/AWS
• Description: Develop an API to serve predictions from a machine learning model. Document the API endpoints and usage.

Project: Building a Web Application with Streamlit

• Objective: Create a web application to showcase a data science project.
• Tools: Python, Streamlit
• Description: Build a Streamlit app to interactively explore and visualize data. Include user inputs, charts, and model predictions.

10. Capstone Project

Project: End-to-End Data Science Project

• Objective: Complete an end-to-end data science project from data collection to deployment.
• Tools: Python, Pandas, Scikit-Learn, TensorFlow/Keras, Flask/FastAPI, Docker
• Description: Choose a real-world problem, gather and clean data, perform EDA, build and evaluate models, and deploy the solution. Document the entire workflow in a comprehensive report.

Stata Projects

1. Data Cleaning

Project: Socioeconomic Data Cleaning

• Objective: Clean and preprocess socioeconomic data for analysis.
• Tools: Stata
• Description: Handle missing values, outliers, and inconsistencies in a socioeconomic dataset. Document each step of the cleaning process.

2. Exploratory Data Analysis (EDA)

Project: EDA on Health Data

• Objective: Perform exploratory data analysis on health data.
• Tools: Stata
• Description: Analyze health data to find trends, correlations, and insights. Visualize distributions, relationships, and summary statistics.

3. Regression Analysis

Project: Wage Determinants Analysis

• Objective: Analyze factors affecting wages using regression techniques.
• Tools: Stata
• Description: Build and evaluate linear regression models to study the impact of various factors on wages.

4. Classification Projects

Project: Loan Default Prediction

• Objective: Predict loan default using classification algorithms.
• Tools: Stata
• Description: Build and evaluate classification models to predict loan defaults based on historical data.

5. Clustering Projects

Project: Household Segmentation

• Objective: Segment households based on socioeconomic indicators.
• Tools: Stata
• Description: Apply clustering algorithms to group households. Analyze and interpret the segments.

6. Time Series Analysis

Project: Economic Indicators Forecasting

• Objective: Forecast economic indicators using time series analysis.
• Tools: Stata
• Description: Use ARIMA models to predict economic indicators. Evaluate model accuracy with metrics like RMSE.

7. Machine Learning Projects

Project: Logistic Regression for Health Outcomes

• Objective: Predict health outcomes using logistic regression.
• Tools: Stata
• Description: Build and evaluate a logistic regression model to predict health outcomes based on various predictors.

8. Dashboards and Visualization

Project: Economic Data Dashboard

• Objective: Create a dashboard to visualize economic data.
• Tools: Stata, Tableau/Power BI
• Description: Build a dashboard to visualize key economic metrics and trends. Include interactive elements like dropdowns and sliders.

9. Deployment Projects

Project: Deploying a Predictive Model

• Objective: Deploy a predictive model for public use.
• Tools: Stata, Shiny
• Description: Develop a Shiny app to serve predictions from a Stata model. Document the app usage and functionality.

10. Capstone Project

Project: End-to-End Data Science Project

• Objective: Complete an end-to-end data science project from data collection to deployment.
• Tools: Stata
• Description: Choose a real-world problem, gather and clean data, perform EDA, build and evaluate models, and deploy the solution. Document the entire workflow in a comprehensive report.

R Projects

1. Data Cleaning

Project: Financial Data Cleaning

• Objective: Clean and preprocess financial data for analysis.
• Tools: R, dplyr, tidyr
• Description: Handle missing values, outliers, and inconsistencies in a financial dataset. Document each step of the cleaning process.

2. Exploratory Data Analysis (EDA)

Project: EDA on mtcars dataset

• Objective: Perform exploratory data analysis on retail data.
• Tools: R, ggplot2, dplyr
• Description: Analyze car data to find correlations, and insights. Visualize distributions, relationships, and summary statistics.

3. Regression Analysis

Project: Sales Forecasting

• Objective: Predict sales using regression techniques.
• Tools: R, lm, ggplot2
• Description: Build and evaluate linear and polynomial regression models to predict sales based on various features.

4. Classification Projects

Project: Customer Segmentation with Decision Trees

• Objective: Segment customers using decision tree classification.
• Tools: R, rpart, caret
• Description: Build and evaluate decision tree models to segment customers based on purchasing behavior.

5. Clustering Projects

Project: Market Segmentation

• Objective: Segment the market based on consumer behavior.
• Tools: R, kmeans, cluster
• Description: Apply clustering algorithms to group consumers. Analyze and interpret the segments.

6. Time Series Analysis

Project: Monthly Sales Forecasting

• Objective: Forecast monthly sales using time series analysis.
• Tools: R, forecast, zoo
• Description: Use ARIMA models to predict monthly sales. Evaluate model accuracy with metrics like RMSE.

7. Machine Learning Projects

Project: Random Forest for Classification

• Objective: Classify data using Random Forest algorithm.
• Tools: R, randomForest, caret
• Description: Build and evaluate a Random Forest model to classify data. Interpret the results and assess model performance.

8. Dashboards and Visualization

Project: Interactive Data Dashboard with Shiny

• Objective: Create an interactive data dashboard.
• Tools: R, Shiny, ggplot2
• Description: Build a Shiny dashboard to visualize key metrics and trends. Include interactive elements like dropdowns and sliders.

9. Deployment Projects

Project: Deploying a Machine Learning Model with Plumber

• Objective: Deploy a trained machine learning model as a web API.
• Tools: R, Plumber, Docker
• Description: Develop an API to serve predictions from an R model. Document the API endpoints and usage.

10. Capstone Project

Project: End-to-End Data Science Project

• Objective: Complete an end-to-end data science project from data collection to deployment.
• Tools: R, dplyr, ggplot2, caret, Shiny
• Description: Choose a real-world problem, gather and clean data, perform EDA, build and evaluate models, and deploy the solution. Document the entire workflow in a comprehensive report.