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Accuracymetrics.pdf
Accuracymetrics.pdf
 
 
HousePrediction.pdf
HousePrediction.pdf
 
 
HousePricePrediction (2).ipynb
HousePricePrediction (2).ipynb
 
 
Readme.md
Readme.md
 
 

Readme.md

1] House Price Prediction (Jan 2019- Feb 2019)

Problem Statement:

Predict the prices of homes based on given features

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015 and our task is to build a machine learning model that can predict the house prices.

Description

Columns id. date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront. view, condition, grade, sqft_above, sqft_basement, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15

Steps followed:

1] Data Analysis (To find out Outliers, Null Values)

2] Data Cleaning (Addressed the problem found in step 1)

3] Data Visualization (Scatter plots, Histograms, Correlation Matrix, Data distribution)

4] Data Munging (encoding of zip code, Logarithmic transformations)

6] Feature Selection (Recursive feature elimination)

7] Machine Learning Models (Linear regression, Lasso regression, Ridge regression, Polynomial regression)