About Data Science📊

📊🔬🧮💻 Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data 📈📉📊📋. It is related to data mining, machine learning, and big data 💡🧠🧐.
Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyze actual phenomena" with data 🌟📚🔍. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge 📚🧮💻.
However, data science is different from computer science and information science 🤔. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge 🚀💥🌊.

📈🤖🔮Stock Market Price Predictor Using Machine Learning

A stock market price predictor 📈💰 was developed using Streamlit 🌊 for the frontend and Recurrent Neural Network (RNN) 🔍🧠 for the backend.
The model was trained on the Dow Jones and other indexes and achieved a Mean Absolute Error (MAE) of 0.0015 and a Mean Squared Error (MSE) of 0.00005, indicating a high level of accuracy in predicting stock market prices. The project utilized Streamlit to create a user-friendly interface for the model and RNN to analyze and process historical stock market data, showcasing proficiency in developing interactive web-based applications and advanced machine learning techniques.
This project demonstrates my ability to use cutting-edge technologies such as RNN and Streamlit to develop a highly accurate stock market price predictor. 💪🏼🚀

Credit Card Fraud Detector 💳🚫

A credit card fraud detection system 💳🕵️‍♀️ was developed by implementing and comparing various machine learning algorithms 🤖 such as Decision Tree 🌳, K-Nearest Neighbors 👨‍👩‍👧‍👦, Logistic Regression 📉, Support Vector Machine 🤝, Random Forest Classifier 🌳 and XGBoost 📈.
The system achieved an accuracy of 0.9995 with K-Nearest Neighbors and 0.9991 with Logistic Regression.
The project utilized a variety of tools and libraries 🛠️ such as scikit-learn, XGBoost, NumPy, Matplotlib and termcolor to implement the project and visualize the results, showcasing proficiency in using popular data science libraries and tools to effectively build and evaluate credit card fraud detection systems.
This project demonstrates my ability to use various machine learning algorithms and data science tools to develop an accurate and efficient credit card fraud detection system. 💪

Fake News Detector📰

A fake news detection system was developed using 💻 NLTK, 📊 TF-IDF vectorizer, and 🤖 Multinomial Naive Bayes algorithm. The system achieved high accuracy and efficiency in detecting fake news with an accuracy of 0.9591 on train data and 0.9511 on test data.
The project utilized various natural language processing techniques such as NLP to pre-process and clean the text data, and implemented vectorization and classification methods to train and test the model.
Additionally, a variety of tools including 📈 Matplotlib, 🔢 NumPy, and 🐼 Pandas were used for handling and visualizing the CSV data, showcasing proficiency in data manipulation and visualization techniques.
This project demonstrates my ability to use advanced machine learning techniques and natural language processing tools to develop an efficient and accurate fake news detection system. 🚀