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Buying a house that suits their choices is every person's desire, and it is thus known as their dream house. One considers several aspects while purchasing a home, starting from the budget, the location, the number of rooms available, and many more. But how to find a house that satisfies one's requirements? This is not a quick and easy task.
But no need to worry; homebuyers can nowadays find their dream home with a click of a button. Zillow is a popular estimator for house evaluation available online. It is considered one of the top real estate marketplaces for buying a house in the United States. Zillow's Zestimate allows the homebuyers to search for a home that satisfies their location, area, budget, etc.
The Zillow Zestimate provides the homebuyers with information on the actual worth of the house based on public data. The accuracy of the Zestimate information depends on the location and availability of the data of a specific area. Hence the more data available, the more is the accuracy of the Zestimate.
This project aims to build a machine learning model that will give the best future sales prediction of homes.
The Zillow Zestimate dataset is a dataset from Kaggle used to make future sales predictions and improve the log error.
There are two datasets available –
There are around 60 attributes in the dataset on basis of which the model can be built.
Some of the important features amongst them are as follows:
To predict the sale prices of the houses and improve the log error i.e. the error due to the difference between the actual and the predicted home values.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.