GitHub topics: matplotlib-seaborn
sabin74/rossmann_sales_forecast
This project forecasts daily sales for Rossmann stores using historical data, store metadata, and engineered features. We use the Random Forest Regression & XGBoost regression model to capture complex patterns and improve predictive accuracy.
Language: Jupyter Notebook - Size: 298 KB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 0 - Forks: 0
sabin74/handwritten_digit_recognition
This project implements a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify handwritten digits from the MNIST dataset. The model learns to recognize digits (0–9) from grayscale images and achieves high accuracy on the test set.
Language: Jupyter Notebook - Size: 2.8 MB - Last synced at: 25 days ago - Pushed at: 25 days ago - Stars: 0 - Forks: 0
sabin74/retail_sales_forecast
This project focuses on predicting retail sales using historical sales data and time-series regression techniques. It leverages Python, Scikit-learn, and XGBoost to build predictive models capable of forecasting sales trends. The goal is to provide actionable insights to retailers for inventory and sales strategy planning.
Language: Jupyter Notebook - Size: 1.82 MB - Last synced at: 17 days ago - Pushed at: 27 days ago - Stars: 0 - Forks: 0
sabin74/heart_disease_prediction
A machine learning project to predict the presence of heart disease using the Kaggle Heart Disease Dataset. The project uses data preprocessing, exploratory data analysis (EDA), feature engineering, and a Random Forest Classifier to achieve high prediction accuracy.
Language: Jupyter Notebook - Size: 1.16 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0
sabin74/loan_approval_prediction
This project predicts whether a loan application will be approved or not using machine learning classification models. The dataset used is from Kaggle’s Loan Prediction problem. The goal is to build a robust model to assist banks or financial institutions in making automated loan approval decisions.
Language: Jupyter Notebook - Size: 923 KB - Last synced at: 17 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0
rohancodestack/C19-Health-Insights-USA
A data-driven platform leveraging advanced visualizations—heatmaps, geo-mapping, and time-series graphs—to analyze COVID-19 trends, vaccination metrics, and demographic impacts for actionable insights.
Language: Python - Size: 1000 Bytes - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0
