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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

• 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.

• 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.

• 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.

• 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.

• 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.