Welcome to my repository of Data Science and Machine Learning Projects! This collection showcases various projects that delve into machine learning, data preprocessing, visualization, and predictive modeling. Each project includes structured analyses, dataset insights, exploratory data analysis (EDA), feature engineering, and model development.
This repository contains the following projects:
- Objective: Predict the strength of passwords using machine learning techniques.
- Techniques: Text preprocessing, TF-IDF vectorization, Decision Trees, Logistic Regression.
- Objective: Analyze social media data to understand trends and sentiments.
- Techniques: Data scraping, Natural Language Processing (NLP), Sentiment Analysis, Data Visualization.
- Objective: Detect fraudulent transactions in Fastag systems.
- Techniques: Data preprocessing, Feature Engineering, Decision Trees, Neural Networks.
- Objective: Predict students' grades based on various academic and demographic factors.
- Techniques: Data Cleaning, Regression Models, Feature Selection.
- Objective: Develop a predictive model for heart disease diagnosis using patient health metrics.
- Techniques: Feature Engineering, Logistic Regression, Random Forest, XGBoost.
- Objective: Cluster countries into three categories (Help Needed, Maybe Help Needed, No Help Needed) for better aid distribution.
- Techniques: K-Means Clustering, Hierarchical Clustering, PCA, Feature Selection.
- Languages: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)
- Machine Learning Models: Decision Trees, Logistic Regression, Regression Models, Clustering
- Data Visualization: Matplotlib, Seaborn, Plotly
- Frameworks & Libraries: Scikit-learn,
- Clone this repository:
git clone https://github.com/misrapk/Data-Science-ML-Projects.git