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paris-housing-prices

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squareMeters
int64
numberOfRooms
int64
hasYard
int64
hasPool
int64
floors
int64
cityCode
int64
cityPartRange
int64
numPrevOwners
int64
made
int64
isNewBuilt
int64
hasStormProtector
int64
basement
int64
attic
int64
garage
int64
hasStorageRoom
int64
hasGuestRoom
int64
price
float64
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1
4,313
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956
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7
7,559,081.5
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39
1
1
98
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8
6
2,015
1
0
3,653
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1
2
8,085,989.5
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1
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1
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1
3,281
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8
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1
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0
58
46,834
7
4
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0
6,486
3,310
366
0
0
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1
1
46
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6
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9,584
8,587
677
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8
9,257,840.9
52,325
60
1
1
24
76,804
6
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0
8,987
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808
1
4
5,238,533.2
67,311
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45,626
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3
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1
6,928
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774
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6,732,249
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1
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8,974
755
1
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6,159,875.1
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1
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22,718
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2,668
4,669
766
0
8
8,414,104.3
77,579
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1
1
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88,798
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1
1,999
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4,850
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1
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7,767,797.9
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1
1
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2,530,488
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Check out the documentation for more information.

Paris Housing Prices Analysis

Project Overview

This project explores the Paris Housing Dataset to understand how various house characteristics influence price levels.
The analysis focuses on identifying the main factors that drive property value, while addressing specific research questions through visual and statistical exploration.

Data Preparation & Cleaning

  • Verified there were no missing or duplicated values in the dataset.
  • Conducted outlier detection using both the IQR (Interquartile Range) and Z-Score methods.
  • Results showed zero outliers across all columns, confirming that the dataset is clean, consistent, and statistically balanced.
  • No data transformation, removal, or capping was needed.

Exploratory Data Analysis (EDA)

🔹 Research Question 1:

For an average house (with all features close to the mean, except for the garage), how does garage size affect the price?

Approach:
We filtered the dataset to include only houses near the average in all numeric features (within ±1.5 standard deviations), excluding garage size.
A regression plot was then used to test the relationship between garage size and price for these average homes.

Visualization:

image

Interpretation:

The regression line is nearly flat, showing no meaningful relationship between garage size and house price.
Even when including a broader range of homes (±1.5 std), the correlation remained very weak and close to zero.
This indicates that, among average Paris homes, garage size does not significantly influence property value.

🔹 Research Question 2:

What are the strongest correlations between house features and price?

Approach:
We computed the correlation matrix for all numerical columns and visualized it using a heatmap to highlight relationships among variables.

Visualization:

image

Interpretation:

The heatmap reveals that squareMeters has an almost perfect correlation with price (~1.0), confirming it as the primary determinant of house value.
All other variables (such as garage, floors, or hasPool) show very weak or near-zero correlations with price.
This means house size is the dominant factor driving pricing in the Paris housing market.

🔹 Research Question 3:

How are house prices distributed across the dataset?

Approach:
We plotted a histogram with a density curve (KDE) to visualize the distribution of house prices.

Visualization:
image

Interpretation:

The histogram shows a balanced and nearly uniform distribution of prices.
There are no extreme peaks or tails, which indicates that the dataset includes homes from a wide range of prices without bias.
This supports the reliability of statistical analyses performed on this dataset.

Key Insights

  1. Garage size has almost no correlation with property price once other variables are controlled.
  2. Square meters (total area) is the strongest and most consistent driver of housing price.
  3. The dataset is clean and balanced, with no missing data, no outliers, and no major skewness.
  4. The Paris housing market appears to value total living area significantly more than secondary features such as garage, pool, or basement.

Tools & Libraries

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn

Conclusions & Next Steps

  • The results confirm that property size (square meters) overwhelmingly determines housing price in Paris.
  • Garage size and other smaller features have minimal predictive power.
  • Future analysis could extend this by integrating location-based variables (e.g., proximity to city center or public transport) to build a machine learning model predicting house prices.

Author

Name: May
Dataset: Paris Housing Prices
Platform: Hugging Face Datasets

Youtube URL for the Video review of the project: https://www.youtube.com/watch?v=cnTDlbKcGz4
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