opcode-open-spring-fest/accommodating-insights

This project delves into a rich dataset containing information about short-term rentals in a geographic location. By analyzing this data, we aim to uncover insights and trends within the rental market.

accommodating-insights's Issues

[Feature]: Convert the non-numerical features into numerical

So, what is it about?

How can I effectively encode non-numerical features like 'neighbourhood_group', 'neighbourhood', and 'room_type' into numerical representations, ensuring optimal utilization of this categorical data for analysis and modeling purposes while maintaining the meaningful distinctions inherent in each feature?

Code of Conduct

• I agree to follow this project's Code of Conduct

[Feature]: Outlier Analysis 2

So, what is it about?

use the boxplot to find the outliers of 'neighbourhood_group' and price.

Code of Conduct

• I agree to follow this project's Code of Conduct

[Feature]: Correlation Analysis

So, what is it about?

How can we comprehensively analyze the correlations between features within our dataset, discerning which features exhibit significant correlations and potentially uncovering underlying relationships or dependencies? Furthermore, could we visualize this analysis using a heatmap to provide a clear and intuitive representation of the correlations, thereby identifying clusters of correlated features for further investigation and potential feature engineering?

Give the observation made from the heatmap also.

Code of Conduct

• I agree to follow this project's Code of Conduct

[Feature]: Plot the graph

How does the price of listings correlate with the number of reviews they receive?

plot a graph (a suitable one) with 'number_of_reviews' on the x-axis and 'price' on the y-axis.

Observations:

• Examine the graph plot to identify any discernible patterns or trends.
• Analyze the relationship between price and the number of reviews:
  * Are higher-priced listings associated with more reviews, or vice versa?
  * Is there a correlation between price and the number of reviews?

Code of Conduct

• I agree to follow this project's Code of Conduct

[Feature]: Logarithmic Transformation

So, what is it about?

What is the distribution of logarithmically transformed prices (price_log) for listings, and how does it compare to a normal distribution?

Logarithmic Transformation:

Take the logarithmic transformation of the 'price' column and add it as a new column named 'price_log' to the dataframe.

• Plot a histogram or kernel density estimate (KDE) plot to visualize the distribution of 'price_log'.
• Overlay a normal distribution curve on the plot to compare it with the observed distribution.

Observations:

• Analyze the plot to assess the similarity between the distribution of 'price_log' and a normal distribution.
• Determine if the logarithmically transformed prices exhibit characteristics of normality, such as symmetry and peakedness.
• Consider any deviations from normality and their implications for statistical analysis or modeling.

Code of Conduct

• I agree to follow this project's Code of Conduct

[Feature]: Fill the missing Values

So, what is it about?

What is the most effective method for filling missing values in the 'reviews_per_month' column of the dataset, and how does it impact the distribution of reviews per month?

Code of Conduct

• I agree to follow this project's Code of Conduct

[Feature]: Outliers analysis 1

So, what is it about?

Use the boxplot to detect the outliers of 'room_type' v/s 'Price'.

Code of Conduct

• I agree to follow this project's Code of Conduct