patternproject-230318072015-0781d463.pptx

H h n k k

1. PATTERN RECOGN I TION HOUSE PRICE PREDICTION MUMBAI Presented By : Shashi Ranjan (20BTCSE0061) Sandeep Yadav (20BTCSE0056) Submitted to : Mr. Shubhashish Goswami

2. Objective This project is prepared to predict the price of house in ‘ Mumbai ’ using the concept of Machine Learning.

3. Introduction It is very difficult to search house at places like Mumbai. Even if you find a house it is very difficult to get a perfect price for the same. To overcome such problem Machine Learning technique can be used. Using Machine Learning Technique it will be easy to know the price of house based on the area available, number of bedrooms, facilities available

4. Dataset Description Dataset Size : 6338 x 17 Price Area Location Number of Bedrooms New/Resale Gymnasium Lift Available Car Parking Maintenance Staff 24x7 Security Club House Intercom Landscaped Gardens Indoor Games Gas Connection Jogging Track Swimming Pool Following are the parameters in dataset for house price prediction of ‘Mumbai’ :

5. Dataset Type Dataset CONTENTS CONTENTS Categorical Quantitative Continuous Binary Nominal Ordinal Descrete Ordinal Binary Dataset is a collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer.

6. CATEGORICAL NOMINAL BINARY Location Categorical Attributes ORDINAL New/Resale Gymnasium Car Parking Maintenance 24x7 Security Club House Intercom Landscaped Indoor Games Gas Connection Jogging Track Swimming Pool

7. QUANTITATIVE CONTINUOUS D I SCRETE Number of Bedrooms Lift Available Price Area Quantitative Attributes

8. Snapshot of dataset The following snapshot shows tail part of data taken from Dataset used in House price prediction.

9. Data Visualisation (Scatter Plot) In the given Scatter plot it shows that as the ‘Price’ of house increases with increase in ‘Area’ of house.

10. Data Visualisation (Pie Chart) Pie chart shows houses available with how many ‘ No.of Bedrooms ’ and their percentage out of 100.

11. Data Visualisation (Histogram) Histogram verifies the pie chart as is shows the number of houses having how many ‘No. of Bedrooms’.

12. Data Visualisation (Box-Whisker Plot) Box and whisker plot here shows how many outliers are present in each parameter of the Dataset.

13. Data Visualisation (Box-Whisker Plot) Pie chart shows percentage of No.of Bedrooms

14. Applying Machine Learning Algorithm we set the independent and target variables as X and Y respectively. Split the dataset into training and testing in 70:30 ratio. Fitting the train set to multiple linear regression and getting the score of the model. Fitting the train set to decision tree and getting the score of the model. Fitting the train set to random forest and getting the score of the model. Calculate the model score to understand how our model performed along with the explained variance score.

15. Conclusion

16. Conclusion After applying Linear Regression, Decision tree, and Random forest Machine Learning algorithm we observe that random forest gives highest accuracy of almost 46% .

17. Thank you