RETAIL SALES DATA

To analyze retail sales data in order to identify purchasing trends, seasonal patterns, and performance metrics that can assist businesses in optimizing inventory, improving customer satisfaction, and increasing overall sales efficiency through data-driven strategies.

DATA CLEANING

The objectiveis to clean and preprocess raw data for accurate analysis and visualization. This includes handling missing values, removing duplicates, correcting data types, and standardizing inconsistent entries to ensure the dataset is reliable, structured, and ready for data analysis tasks.

HOUSE PRICE PREDICTION

The objective is to build a machine learning model to estimate house prices based on key features. The process includes data cleaning, feature selection, model training, and evaluation using tools like Pandas, Scikit-learn, NumPy, and Matplotlib for accurate predictions.

WINE QUALITY PREDICTION

Predict the quality of wine based on its chemical properties using machine learning. The process involves data preprocessing, feature selection, and model training with tools like Pandas, NumPy, Scikit-learn, and Matplotlib to ensure accurate and insightful predictions.