[karma Tshomo] - Fab Futures - Data Science

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import matplotlib as mp
print(mp.__version__)

3.10.7

import pandas as pd
df=pd.read_csv("datasets/Housing.csv")
df.head()

	price	area	bedrooms	bathrooms	stories	mainroad	guestroom	basement	hotwaterheating	airconditioning	parking	prefarea	furnishingstatus
0	13300000	7420	4	2	3	yes	no	no	no	yes	2	yes	furnished
1	12250000	8960	4	4	4	yes	no	no	no	yes	3	no	furnished
2	12250000	9960	3	2	2	yes	no	yes	no	no	2	yes	semi-furnished
3	12215000	7500	4	2	2	yes	no	yes	no	yes	3	yes	furnished
4	11410000	7420	4	1	2	yes	yes	yes	no	yes	2	no	furnished

import matplotlib.pyplot as plt

plt.hist(df["bedrooms"], bins=30)
plt.xlabel("bedrooms")
plt.ylabel("frequency")
plt.title("Distribution of bedrooms")
plt.show()

Interpretation:

Shows how many houses have 1, 2, 3, 4, or more bedrooms

Most houses have 3 or 4 bedrooms.

Very few houses have 1 or 2 bedrooms.

plt.boxplot(df["price"])
plt.ylabel("Price")
plt.title("Boxplot of House Prices")
plt.show()

Interpretation:

The middle line in the box shows the median house price.

Whiskers show the lowest and highest prices within a normal range.

Points outside the whiskers are outliers, meaning some houses are much more expensive than most.

plt.scatter(df["parking"], df["price"])
plt.xlabel("parking")
plt.ylabel("Price")
plt.title("parking vs Price")
plt.show()

Interpretation:

Shows the relationship between number of parking spaces and house price.

Generally, more parking spaces might be linked to higher prices.

import seaborn as sns

sns.pairplot(df[["price", "area", "bedrooms", "bathrooms", "stories"]])
plt.show()

df["furnishingstatus"].value_counts().plot(kind="bar")
plt.xlabel("Furnishing Status")
plt.ylabel("Count")
plt.title("Furnishing Status Distribution")
plt.show()

# Select only numeric columns
numeric_df = df.select_dtypes(include=['int64', 'float64'])

plt.figure(figsize=(10,6))
sns.heatmap(numeric_df.corr(), annot=True, cmap="coolwarm")
plt.title("Correlation Heatmap")
plt.show()

Intrepretation:

Dark red = strong positive correlation, dark blue = strong negative correlation, lighter colors = weak correlation.

Helps identify which features may affect house price the most.