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Overview As a Data Scientist, you work for Hass Consulting Company which is a real estate leader with over 25 years of experience. You have been tasked to study the factors that affect housing prices using the given information on real estate properties that was collected over the past few months. Later onwards, create a model that would allow the company to accurately predict the sale of prices upon being provided with the predictor variables. Within your deliverable you are expected to: Define the question, the metric for success, the context, experimental design taken. Read and explore the given dataset. Define the appropriateness of the available data to answer the given question. Find and deal with outliers, anomalies, and missing data within the dataset. Perform univariate, bivariate and multivariate analysis recording your observations. Performing regression analysis. Incorporate categorical independent variables into your models. Check for multicollinearity Provide a recommendation based on your analysis. Create residual plots for your models, and assess heteroskedasticity using Barlett's test. Challenge your solution by providing insights on how you can make improvements in model improvement. While performing your regression analysis, you will be required to perform modeling using the given regression techniques then evaluate their performance. You will be then required to provide your observations and recommendation on the suitability of each of the tested models on their appropriateness of solving the given problem. Multiple Linear Regression Quantile Regression Ridge Regression Lasso Regression Elastic Net Regression

UnplugCharger/data_engineering

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