Welcome to my Data Science Projects repository! This repository contains a collection of machine learning and data analysis projects developed using Python, Pandas, NumPy, Scikit-learn, Matplotlib, and Jupyter Notebook.
These projects demonstrate my understanding of data preprocessing, exploratory data analysis (EDA), feature engineering, model building, evaluation, and prediction.
- Classified Iris flowers into three species using Machine Learning.
- Performed data preprocessing and visualization.
- Built and evaluated classification models.
Tech Stack: Python, Pandas, Scikit-learn, Matplotlib
- Predicted movie ratings based on different features.
- Applied regression algorithms for prediction.
- Evaluated model performance using regression metrics.
Tech Stack: Python, Pandas, Scikit-learn
- Built a machine learning model to predict future sales.
- Performed data cleaning, feature selection, and model training.
- Compared prediction accuracy using evaluation metrics.
Tech Stack: Python, NumPy, Pandas, Scikit-learn
- Predicted passenger survival using the Titanic dataset.
- Conducted data preprocessing and feature engineering.
- Implemented classification algorithms and evaluated accuracy.
Tech Stack: Python, Pandas, Scikit-learn
- Detected fraudulent credit card transactions using machine learning.
- Handled imbalanced datasets and evaluated classification performance.
- Focused on improving fraud detection accuracy.
Tech Stack: Python, Pandas, Scikit-learn
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- Jupyter Notebook
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Data Visualization
- Classification
- Regression
- Model Evaluation
- Prediction
- Hyperparameter Tuning
- Model Deployment using Flask/Streamlit
- Deep Learning Models
- Advanced Feature Engineering