Activities and Projects Compilation

Introduction

Welcome to my compilation of activities and projects! Below, you'll find a summary of my key projects and activities related to computer courses, data science, machine learning, and quantitative methods.

Table of Contents

1. [Computer Courses](Go to file2.md)
2. Data Science
3. Machine Learning
4. Quantitative Methods

Computer Courses

Programming Courses

1. Introduction to Programming: Developed and delivered a beginner-level programming course covering fundamental concepts such as variables, control structures, and functions.

2. Advanced Programming Techniques: Created a course focusing on advanced programming techniques such as data structures, algorithms, and object-oriented programming principles.

Web Development Projects

1. E-commerce Website: Designed and implemented an e-commerce website using HTML, CSS, and JavaScript, integrating features such as user authentication, product listings, and shopping cart functionality.

2. Blog Application: Developed a blog application using Django, allowing users to create, edit, and delete blog posts, as well as comment on posts.

Data Science

Data Analysis Projects

1. Exploratory Data Analysis (EDA) on Sales Data: Conducted EDA on a retail dataset to identify trends, patterns, and correlations, leading to actionable insights for business stakeholders.

2. Customer Segmentation Using K-Means Clustering: Applied K-means clustering to segment customers based on their purchasing behavior, enabling targeted marketing strategies.

Machine Learning Projects

1. Predictive Modeling for Loan Approval: Built a machine learning model to predict loan approval decisions based on applicant information, achieving an accuracy of 85% on the test dataset.

2. Image Classification with Convolutional Neural Networks (CNNs): Implemented a CNN-based image classifier to classify images into different categories, achieving state-of-the-art accuracy on benchmark datasets.

Quantitative Methods

Statistical Analysis Projects

1. Regression Analysis on Housing Prices: Conducted regression analysis to model the relationship between housing prices and various predictors such as location, size, and amenities.

2. Time Series Forecasting: Developed time series forecasting models using techniques such as ARIMA and exponential smoothing to predict future sales or demand trends.

Conclusion

This compilation provides a snapshot of my activities and projects across various domains. I'm passionate about leveraging technology and data-driven insights to solve real-world problems and drive innovation.

Feel free to reach out if you'd like to learn more about any specific project or collaborate on future endeavors!