Airbnb_Dataset_Analysis

Overview

This project involves analyzing an Airbnb dataset to explore various data patterns, perform data cleaning, and extract meaningful insights. The following steps were undertaken:

1. Data Exploration
2. Data Cleaning
3. Exploratory Data Analysis (EDA)
4. Feature Engineering
5. Visualizations

---

Steps and Insights

1. Data Exploration

Loading the Dataset

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

df = pd.read_csv('datasets.csv')

• Initial Inspection:
  • df.head(): Displays the first few rows of the dataset.
  • df.shape: Provides the dimensions of the dataset.
  • df.info(): Summarizes the dataset, including data types and non-null counts.

Key Observations:

• The dataset contains missing values and duplicate entries.
• The id column is stored as a float but should be categorical.

---

2. Data Cleaning

Handling Missing Values

df.isnull().sum()
df.dropna(inplace=True)
df.isnull().sum()

• Missing values were identified and dropped to ensure clean data.

Handling Duplicate Values

df.duplicated().sum()
df.drop_duplicates(inplace=True)
df.duplicated().sum()

• Duplicate entries were removed.

Adjusting Data Types

df['id'] = df['id'].astype('object')

• The id column was converted to an object data type.

Updated Dataset:

• Shape: df.shape

---

3. Exploratory Data Analysis (EDA)

Univariate Analysis

1. Price Distribution

sns.histplot(data=df, x='price')

• Identified extreme outliers in the price column using a boxplot.

sns.boxplot(data=df, x='price')

• Excluded prices > 16,000 for focused analysis.

data = df[df['price'] < 16000]
sns.histplot(data=data, x='price', bins=120)

2. Availability (365 Days)

sns.histplot(data=df, x='availability_365', bins=30)

Group Analysis

1. Price by Neighborhood Group

data.groupby('neighbourhood_group')['price'].mean()

• Average price varies significantly across neighborhood groups.

2. Price Per Bed

data['price_per_bed'] = data['price'] / data['beds']
data.groupby('neighbourhood_group')['price_per_bed'].mean()

• Introduced a new feature to normalize pricing.

Bivariate Analysis

1. Neighborhood Group vs. Price by Room Type

sns.barplot(data=data, x='neighbourhood_group', y='price', hue='room_type')

2. Reviews vs. Price

sns.scatterplot(data=data, x='number_of_reviews', y='price', hue='neighbourhood_group')

• Explored the relationship between the number of reviews and price.

Multivariate Analysis

sns.pairplot(data=data, vars=['price', 'number_of_reviews', 'availability_365', 'beds'], hue='room_type')

• Examined relationships among numerical columns.

Geographical Distribution

sns.scatterplot(data=data, x='longitude', y='latitude', hue='room_type')

• Visualized the spatial distribution of Airbnb listings.

---

Visualizations

• Histograms: Displayed distributions of price and availability_365.
• Boxplots: Highlighted outliers in pricing.
• Scatterplots: Explored geographic and review-based trends.
• Pairplot: Showed interrelationships between numeric variables.

---

Conclusions

• Outliers: High-priced listings (>16,000) were removed for a more focused analysis.
• Neighborhood Insights:
  • Pricing and price per bed vary significantly across neighborhoods.
  • Room type is a strong determinant of price within neighborhoods.
• Geographical Trends:
  • Listings are clustered in specific geographic locations.

This analysis highlights key trends and relationships within the Airbnb dataset and sets the foundation for further modeling or operational insights.