This project involved a detailed analysis of a sales dataset, focusing on currency normalization, sales trends, and predictive modeling. Key components included data cleansing, real-time currency conversion to USD, and exploratory analysis using Power BI for visualization. The core of the project was predictive analysis using a RandomForestRegressor model to forecast sales trends. The aim was to derive actionable business insights for data-driven decision-making in sales strategy and marketing.

This project involved a comprehensive analysis of a real estate dataset, focusing on property valuations, market trends, and buyer preferences. Key components included extensive data preparation with thorough cleansing and transformation, exploratory data analysis using advanced visualization tools in Power BI, and predictive modeling. The core of the project revolved around using a RandomForestRegressor model to predict market trends and property values. The objective was to derive actionable insights for strategic decision-making in real estate development, investment, and marketing, leveraging a data-driven approach to understand and anticipate market movements.

This project centered on analyzing a Netflix dataset, utilizing SQL for selective data extraction and Power BI for visualization. The primary role of SQL was to query and extract specific data subsets, such as viewer ratings, genres, and voting patterns, from the extensive dataset. This selective data extraction was crucial to focus the analysis on relevant aspects, thereby optimizing the dataset for more efficient and targeted visual exploration in Power BI. The strategic use of SQL for data picking laid the groundwork for creating an insightful Power BI dashboard, facilitating a deeper understanding of viewer preferences and aiding in informed decision-making for content and marketing strategies.