University of Texas

DATA ANALY PGPBSBA

Capstone Project-House Price Prediction
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Content:
1. Introduction……………………………………………………………………………………………………………………………………..…4
1.1 Brief introduction about the problem statement and the need of solving it. ………………………………….4
2. EDA and Business Implication. ………………………………….………………………………….…………………………………..5
2.1 Uni-variate / Bi-variate / Multi-variate analysis to understand relationship b/w variables. How your
analysis is impacting the business? ………………………………….………………………………….………………………………...5
3. Data Cleaning and Pre-processing…………………………………….………………………………….…………………………….9
3.1 Approach used for identifying and treating missing values and outlier treatment (and why)………...12
3.2 Need for variable transformation (if any)… ………………………………….………………………………….……………..12
3.3Variables removed or added and why (if any)… ………………………………….……………………………………………12
4. Model building…………………………………….………………………………….………………………………….……………………..28
4.1Clear on why a particular model(s) was chosen….………………………………….………………………………………...28
4.2Effort to improve model performance…..………………………………….………………………………….………………….28
5. Model validation…………………………………….………………………………….………………………………….…………………35
5.1How was the model validated? Just accuracy, or anything else too?. ……………………………………………...35
6. Final interpretation / recommendation…………………………………….……………………………………………………….37
6.1Detailed recommendations for the management/client based on the analysis done………………………..38
List Of Figures:
1. Missing value…………………………………………………………………………………………………………………………..…...8
2. Univariate analysis……………………………………………………………………………………………………………….………10
3. Outlier treatment………………………………………………………………………………………………………………….……..13
4. Analyzing features………………………………………………………………………………………………………………………..15
5. Pair plot…………………………………………………………………………………………………………………………………..……19
6. Heatmap…………………………………………………………………………………………………………………………………......20
7. Room bed relation with price and leaving measure………………………………………………………………………21
8. Ceil-Relation with Price and Living Measure……………………………………………………………………………....22
9. Coast-Relation with Price and Living Measure……………………………………………………………………………..22
10. Sight-Relation with Price and Living Measure………………………………………………………………………………23
11. condition _Relation with Price and Living Measure………………………………………………………………….....23
12. Quality-Relation with Price and Living Measure…………………………………………………………………………..24
13. Having basement-Relation with Price and Living Measur.……………………………………………………………..24
14. Is renovated-Relation with Price and Living Measur.…………………………………………………………………..25
Capstone Project-House Price Prediction
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15. Furnished-Relation with Price and Living Measure……………………………………………………………………….25
16. Linear Reg-Actual and Predicted joint plot…………………………………………………………………………………….25
17. Linear Lasso-Actual and Predicted jointplot…………………………………………………………………………………….29
18. Linear Ridge-Actual and Predicted jointplot…………………………………………………………………………………….30
19. KNN-Actual and Predicted jointplot…………………………………………………………………………………………………31
20. DT-Actual and Predicted joint plot…………………………………………………………………………………………………..32
21. RF1-Feature_Importancce……………………………………………………………………………………………………………….33
22. GB1-Feature_Importancce………………………………………………………………………………………………………………35
Capstone Project-House Price Prediction
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1. Problem Statement:
a. Problem Statement:
A house value is simply more than location and square footage. Like the features that make up a person,
an educated party would want to know all aspects that give a house its value. For example, you want to
sell a house and you don’t know the price which you may expect — it can’t be too low or too high. To
find house price you usually try to find similar properties in your neighborhood and based on gathered
data you will try to assess your house price.
b. Defining problem statement:
 The goal of this analysis is to understand the relationship between the features of the house and
how those features can predict the house price.
 A house value is simply more than location and square footage. Like the features that make up a
person, an educated party would want to know all aspects that give a house its value.
 If you want to sell a house and you don't know the price which you may expect —it
can't be too low or too high to find house price you usually try to find similar properties in your
neighborhood and based on gathered data you will try to assess your house price.
c. Need of the study/project:
 House price prediction can help the developer determine the selling price of a house and can
help the customer to arrange the right time to purchase a house.
 There are three factors that influence the price of a house which include physical conditions,
concept, and location.
 The price of house increases every year , so the