imogen-rickert / statistical_analysis

House Prices: Advanced Regression Techniques

Statistical_Analysis

Imogen Rickert

August cohort, Berlin, 13.09.20

Content

• Project Description
• Questions & Hypotheses
• Dataset
• Database
• Workflow
• Organization
• Links

Project Description

The purpose of this project was to practice statistical analysis using the iterative data analysis process.

Questions & Hypotheses

The goal of this analysis was to identify the most important features of houses that affect the sale prices.

Dataset

I utilised the dataset: 'House Prices: Advanced Regression Techniques', located on Kaggle: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data

Workflow

I began by examining the dataset, then cleaned and manipulated it using Pandas. Since the dataset was very large, I selected several columns to subset, and worked with the subset for my analysis, instead of using the entire dataset. I checked descriptive statistics, conducted linear and multi-variate regression and utilised Seaborn to visualise my results.

I felt that I could find additional variables which would help to better explain the variance in housing price, so I went back to the original dataset, created a new subset with additional variables, and ran further analyses.

Organization

This repository contains the following files:

• Notebooks:
  • house_prices.ipynb (for data cleaning and creating subsets)
  • statistical_analysis.ipynb (for data analysis)
• Datasets:
  • train.csv (original dataset from Kaggle)
  • houses_cleaned (full dataset after cleaning/manipulation)
  • houses_subset (first subset)
  • houses_subset2 (second subset)