Python Simple project
Budget: $30 – $250 AUD
Load ‘listings.csv’ of Berlin dataset into your Jupyter notebook
Task 1 Looking for Correlations.
Step 1: Compute the standard correlation coefficient (also called Pearson's r) between every pair of attributes.
Step 2: Check how much each attribute correlates with ‘number_of_reviews’. Please present your result in descending order.
Step 3: Visualize the key information of the dataset: inspect relationship between 3 numerical attributes with pandas scatter_matrix() function. These attributes are: 'number_of_reviews','reviews_per_month','availability_365'
Task 2 Data Scrubbing. Step 1: Inspecting missing values in all attributes: outputs the total number of missing values for each column.
Step 2: Which attribute/attributes have missing values? Please write down their column names in a markdown cell.
Step 3: Fill the missing values in attribute ‘reviews_per_month’ with the average value for that variable.
Task 1 Looking for Correlations.
Step 1: Compute the standard correlation coefficient (also called Pearson's r) between every pair of attributes.
Step 2: Check how much each attribute correlates with ‘number_of_reviews’. Please present your result in descending order.
Step 3: Visualize the key information of the dataset: inspect relationship between 3 numerical attributes with pandas scatter_matrix() function. These attributes are: 'number_of_reviews','reviews_per_month','availability_365'
Task 2 Data Scrubbing. Step 1: Inspecting missing values in all attributes: outputs the total number of missing values for each column.
Step 2: Which attribute/attributes have missing values? Please write down their column names in a markdown cell.
Step 3: Fill the missing values in attribute ‘reviews_per_month’ with the average value for that variable.