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Ames Housing Dataset: Key Factors Affecting Prices

This repository contains the analysis of the Ames Housing dataset from kaggel. The project aims to explore and analyze the housing data to uncover insights and trends.

Problem Statement

The goal of this project is to analyze the Ames Housing dataset to identify key factors affecting house prices. By understanding these factors, we can provide actionable insights for stakeholders in the real estate market, such as home buyers, sellers, and real estate agents.

Objective

  • To perform exploratory data analysis (EDA) on the Ames Housing dataset.
  • To identify significant features that influence house prices.
  • To visualize the data for better understanding and communication of insights.
  • To provide a comprehensive summary and conclusion based on the analysis.

Dataset

The dataset used in this project is a subset of the Ames Housing dataset, which includes the following columns:

  • PID: Property ID
  • Neighborhood: The neighborhood where the house is located
  • Year Built: The year the house was built
  • Overall Qual: Overall material and finish quality
  • Kitchen Qual: Kitchen quality
  • Exter Qual: Exterior quality
  • Lot Area: Lot size in square feet
  • SalePrice: Sale price of the house

Project Structure

The project includes the following key files:

  • Ames_Housing_Project_1.ipynb: Jupyter notebook containing the analysis and visualizations.
  • Ames_Housing_Subset.csv: CSV file containing the subset of the Ames Housing dataset.
  • Ames_Housing_Subset_Feature_Description.txt: Text file containing descriptions of the features in the dataset.

Analysis

Importing Libraries

The project begins by importing the necessary libraries for data manipulation and visualization:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Data Exploration

  • Loading the dataset: The dataset is loaded into a DataFrame for analysis.
  • Dataset Shape: The dataset contains 2930 rows and 7 columns.
  • Displaying Last 10 Rows: Displaying the last 10 rows of the dataset for a quick overview.

Visualization Explanation and Insights

Histogram of Year Built

Description: A histogram displaying the distribution of the years in which the houses were built.

Insights:

  • Most houses were built between the 1950s and 2000s.
  • The mean and median years are marked with blue and red vertical lines respectively, providing a quick summary of the central tendency.
  • There is a noticeable increase in construction activity during certain periods, indicating potential development booms.

Scatter Plot of Sale Price vs. Lot Area

Description: A scatter plot showing the relationship between sale price and lot area.

Insights:

  • There is a positive correlation between lot area and sale price, indicating that larger lots tend to have higher sale prices.
  • Some outliers may indicate luxury properties with exceptionally high prices for their lot size.

Box Plot of Sale Price by Neighborhood

Description: A box plot comparing sale prices across different neighborhoods.

Insights:

  • Certain neighborhoods consistently show higher median sale prices.
  • The spread of sale prices varies significantly by neighborhood, suggesting diverse property values within some areas.

Bar Plot of Overall Quality

Description: A bar plot showing the frequency of different overall quality ratings.

Insights:

  • Most houses fall within the mid-range quality ratings.
  • Few houses have very low or very high overall quality ratings, indicating a generally consistent quality level across the dataset.

Heatmap of Correlations

Description: A heatmap displaying the correlations between various features in the dataset.

Insights:

  • Strong positive correlations are observed between sale price and overall quality, living area, and other key features.
  • Some features show little to no correlation, indicating they may have less impact on the sale price.

Conclusion

This project provides an in-depth analysis of the Ames Housing dataset, offering insights into the factors affecting house prices and quality. The visualizations and summaries help in understanding the data better and making informed decisions based on the analysis.