Build a linear regression model that predicts the prices

Reference no: EM133528908

Regression Project: Boston House Price Prediction

The problem at hand is to predict the housing prices of a town or a suburb based on the features of the locality provided to us. In the process, we need to identify the most important features affecting the price of the house. We need to employ techniques of data preprocessing and build a linear regression model that predicts the prices for the unseen data.

Each record in the database describes a house in Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. Detailed attribute information can be attached below.

Exploratory Data Analysis
- EDA is an important part of any project involving data.
"- It is important to investigate and understand the data better before building a model with it.
"- A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
"- A thorough analysis of the data, in addition to the questions mentioned below, should be done

Questions

"1. What does the distribution of 'MEDV' look like?
"2. What can we infer form the correlation heatmap? Is there correlation between the dependent and independent variables?
"3. What are all the inferences that can be found by doing univariate analysis for different variables?
"4. Do bivariate analysis to visualize the relationship between the features having significant correlations

Model Performance Check

1. How does the model is performing? Check using Rsquared, RSME, MAE, MAPE",
"2. Is there multicollinearity? Check using VIF",
"3. How does the model is performing after cross validation?

In order to make statistical inferences from a linear regression model, it is important to ensure that the assumptions of linear regression are satisfied."

Content:

• Executive Summary
• Business Problem Overview and Solution Approach
• Data Overview
• EDA and Data Preprocessing
• Linear Regression and its Assumptions
• Appendix

Executive Summary

• Overview of the most important insights from the project.

Business Problem Overview

• Description of the business problem that the analysis aims to solve
• Explanation of how the analysis can help achieve the business objective
• Mention the solution approach/methodology

Data Overview

• Overview of the data used in the analysis

EDA and Data Preprocessing

• Explain the key insights and findings from the univariate and bivariate analysis of all the variables
• Identification and handling of missing values and outliers
• Visualizations and summary statistics of the data

Regression analysis

• Description of the techniques used in the analysis
• Explanation of how the insights gained from the regression analysis can be used to improve the decision making

Conclusion

• Proposal of specific actions that the business can take based on the regression analysis
• Explain how the analysis can contribute to the business success

Attachment:- Regression Project.rar

Reference no: EM133528908

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