This project involves analyzing a dataset provided by Terro's Real Estate Agency, containing information about 506 houses in Boston. The dataset includes various features such as crime rate, nitric oxides concentration, average number of rooms, etc., which are believed to affect the price of a house in a particular locality. The goal of the analysis is to understand the magnitude of each variable's impact on house prices and build regression models to predict house prices based on these variables.
Generate summary statistics for each variable and provide observations. Plot a histogram of the average price variable and infer insights. Compute the covariance matrix and share observations. Create a correlation matrix and identify the top 3 positively and negatively correlated pairs. Build an initial regression model with one independent variable (LSTAT) and analyze the model's performance. Build a new regression model with two independent variables (LSTAT and AVG_ROOM) and interpret the results. Build a regression model with all variables and interpret the output, highlighting the significance of each independent variable. Select significant variables from the previous model and build another regression model, interpreting the output and comparing it with previous models. The project aims to provide insights into the factors affecting house prices in Boston and develop regression models to predict house prices accurately. The analysis utilizes exploratory data analysis techniques and regression modeling in Microsoft Excel, demonstrating proficiency in statistical analysis and data-driven decision-making.